March 30, 2026

Blog NivelEpsilon

They left AWS to save money. Coming back cost even more

Not long ago, a partner I work with told me about a company that decided it had finally had enough of AWS.

The monthly bill had become the sort of document people opened with the facial expression usually reserved for dental estimates. Consultants were invited in. Spreadsheets were produced. Serious people said serious things about control, efficiency, and the wisdom of getting off the cloud treadmill.

The conclusion sounded almost virtuous. Leave AWS, move the workloads to a colocation facility, buy the hardware, and stop renting what could surely be owned more cheaply.

It was neat. It was rational. It was, for a while, deeply satisfying.

And then reality arrived, carrying invoices.

The company spent a substantial sum getting out of AWS. Servers were bought. Contracts were signed. Staff had to be hired to manage all the things cloud providers manage quietly in the background while everyone else gets on with their jobs. Not long after, the economics began to fray. Reversing course costs even more than leaving in the first place.

That is the part worth paying attention to.

Not because it makes for a dramatic story, though it does. Not because it is especially rare, but because it is not. It matters because it exposes one of the oldest tricks in infrastructure decision-making. Companies compare a visible bill with an invisible burden, decide the bill is the scandal, and only later discover that the burden was doing quite a lot of useful work.

The spreadsheet seduction

On paper, the move away from AWS looked wonderfully sensible.

The cloud bill was obvious, monthly, and impolite enough to keep turning up. On-premises looked calmer. Hardware could be amortized. Rack space, power, and bandwidth could be priced. With a bit of care, the whole thing could be made to resemble prudence.

This is where many repatriation plans become dangerously persuasive. The cloud is cast as an extravagant landlord. On-premises is presented as the mature decision to stop renting and finally buy the house.

Unfortunately, a data center is not a house. It is closer to owning a very large hotel whose plumbing, wiring, keys, security, fire precautions, laundry, and unexpected midnight incidents are all your responsibility, except the guests are servers and none of them leave a tip.

The spreadsheet had done a decent job of pricing the obvious things. Hardware. Colocation space. Power. Connectivity.

What was priced badly were all the dull, expensive capabilities that public cloud tends to bundle into the bill. Managed failover. Backup automation. Key rotation. Elastic capacity. Security controls. Compliance support. Monitoring that does not depend on a specific engineer being awake, available, and emotionally prepared.

What looked like cloud excess turned out to include a great deal of cloud competence.

That distinction matters.

A large cloud bill is easy to resent because it is visible. Operational competence is harder to resent because it tends to be hidden in the walls.

What the cloud had been doing all along

One of the costliest mistakes in infrastructure is confusing convenience with fluff.

A managed database can look expensive right up to the moment you have to build and test failover yourself, define recovery objectives, handle maintenance windows, rotate credentials, validate backups, and explain to auditors why one awkward part of the process still depends on a human remembering to do something after lunch.

A content delivery network may seem like a luxury until you try to reproduce low-latency delivery, edge caching, certificate handling, resilience, and attack mitigation with a mixture of hardware, internal effort, procurement delays, and hope.

The company, in this case, had not really been paying AWS only for compute and storage. It had been paying AWS to absorb a long list of repetitive operational chores, specialized platform decisions, and uncomfortable edge cases.

Once those chores came back in-house, they did not return politely.

Redundancy stopped being a feature and became a budget line, followed by an implementation plan, followed by a maintenance burden. Security controls that had once been inherited now had to be selected, deployed, documented, checked, and defended. Compliance work that had once been partly automated became a steady stream of evidence gathering, procedural discipline, and administrative repetition.

Cloud bills can look high. So can plumbing. You only discover its emotional value when it stops working.

The talent tax

The easiest part of moving on premises is buying equipment.

The harder part is finding enough people who know how to run the surrounding world properly.

Cloud expertise is now common enough that many companies can hire engineers comfortable with infrastructure as code, IAM, managed services, container platforms, observability, autoscaling, and cost controls. Strong cloud engineers are not cheap, but they are at least visible in the market.

Deep on-premises expertise is another matter. People who are strong in storage, backup infrastructure, virtualization, physical networking, hardware lifecycle, and operational recovery still exist, but they are not standing about in large numbers waiting to be discovered. They are experienced, expensive, and often well aware of their market value.

There is also a cultural issue that rarely appears in repatriation slide decks. A great many engineers would rather write Terraform than troubleshoot a hardware issue under unflattering lighting at two in the morning. This is not a moral failure. It is simple market gravity. The industry has spent years abstracting away routine infrastructure pain because abstraction is usually a better use of skilled human attention.

The partner who told me this story was particularly clear on this point. The staffing line looked manageable in planning. In practice, it turned into one of the most stubborn and underestimated parts of the whole effort.

Cloud is not cheap because expertise is cheap. Cloud is often cheaper because rebuilding enough expertise inside one company is very expensive.

Why does utilization lie so beautifully

Projected utilization is one of those numbers that becomes more charming the less time it spends near reality.

Many repatriation models assume that servers will be well used, capacity will be planned sensibly, and waste will be modest. It sounds disciplined. Responsible, even.

Real workloads behave less like equations and more like kitchens during a family gathering. There are quiet periods, sudden rushes, abandoned experiments, quarter-end panics, new projects that arrive with urgency and no warning, and services no one remembers until they break.

Elasticity is not a decorative feature added by cloud providers to justify themselves. It is one of the main ways organizations avoid buying for peak demand and then spending the rest of the year paying for machinery to sit about waiting.

Without elasticity, you provision for the busiest day and fund the silence in between.

Silence, in infrastructure, is expensive.

A half-used on-premises platform still consumes power, occupies space, demands maintenance, requires patching, and waits patiently for a workload spike that visits only now and then. Spare capacity has excellent manners. It makes no fuss. It simply eats money quietly and on schedule.

This was one of the turning points in the story I heard. Forecast utilization turned out to be far more flattering than actual utilization. Once that happened, the economics began to sag under their own good intentions.

The cost of becoming slower

Traditional total-cost comparisons handle direct spending reasonably well. They are much worse at pricing lost momentum.

When a company runs on a large cloud platform, it does not merely rent infrastructure. It also gains access to a constant flow of improvements and options. Better analytics tools. New security integrations. Managed AI services. Identity features. Database capabilities. Deployment patterns. Networking enhancements. Observability tooling.

No single addition changes everything overnight. The effect is cumulative. It is a thousand small conveniences arriving over time and sparing teams from having to rebuild ordinary civilization every quarter.

An on-premises platform can be stable and well run. For the right workloads, that may be perfectly acceptable. But it does not evolve at the pace of a hyperscaler. Upgrades become projects. New capabilities require procurement, testing, staffing, and patience. The platform becomes more careful and, usually, slower.

That slower pace does not always show up neatly in a spreadsheet, but engineers feel it almost immediately.

While competitors are experimenting with new managed services or shipping new capabilities faster, the repatriated organization may be spending its time improving backup procedures, standardizing tools, negotiating maintenance arrangements, or replacing hardware that has chosen an inconvenient moment to become philosophical.

There is nothing glamorous about that. There is also nothing free about it.

Who should actually consider on-premises

None of this means on-premises is foolish.

That would be a lazy conclusion, and lazy conclusions are where expensive architecture plans begin.

For some organizations, on-premises remains entirely reasonable. It makes sense for highly predictable workloads with very little variability. It can make sense in tightly regulated environments where legal, sovereignty, or operational constraints sharply limit the use of public cloud. And at a very large scale, some organizations genuinely can justify building substantial parts of their own platform.

But most companies tempted by repatriation are not in that category.

They are not hyperscalers. They are not all running flat, perfectly predictable workloads. They are not all boxed in by constraints that make public cloud impossible. More often, they are reacting to a painful cloud bill caused by weak cost governance, poor workload fit, loose architecture discipline, or a lack of serious FinOps.

That is a very different problem.

Leaving AWS because you are using AWS badly is a bit like selling your refrigerator because the groceries keep going off while the door is open. The appliance may not be the heart of the matter.

The middle ground companies skip past

One of the stranger features of cloud debates is how quickly they become binary.

Either remain in public cloud forever, or march solemnly back to racks and cages as if returning to a lost ancestral craft.

There is, of course, a middle ground.

Some workloads do benefit from local placement because of latency, residency, plant integration, or operational constraints. But needing hardware closer to the ground does not automatically mean rebuilding the entire service model from scratch. The more useful question is often not whether the hardware should be local, but whether the control plane, automation model, and day-to-day operations should still feel cloud-like.

That is a much more practical conversation.

A company may need some infrastructure nearby while still gaining enormous value from managed identity, familiar APIs, consistent automation, and operational patterns learned in the cloud. This tends to sound less heroic than a full repatriation story, but heroism is not a particularly reliable basis for infrastructure strategy.

The partner who described this case said as much. If they had explored the middle road earlier, they might have kept the local advantages they wanted without assuming quite so much of the surrounding operational burden.

What a real repatriation audit should include

Any company seriously considering a move off AWS should pause long enough to perform an audit that is a little less enchanted by ownership.

Start with the full cloud picture, not just the line items everyone enjoys complaining about. Include engineering effort, compliance automation, security services, platform speed, operational overhead, and the cost of scaling quickly when demand changes.

Then build the on-premises model with uncommon honesty. Price round-the-clock operations. Price redundancy properly. Price backup and recovery as if they matter, because they do. Price refresh cycles, maintenance contracts, spare capacity, patching, testing, physical security, audit evidence, and the awkward certainty that hardware fails when it is least convenient.

Then ask a cultural question, not just a financial one. How many of your engineers actually want to spend more of their time dealing with the physical stack and the operational plumbing that comes with it?

That answer matters more than many executives would like.

A strategy that looks cheaper on paper but nudges your best engineers toward the door is not, in any meaningful sense, cheaper.

Finally, compare repatriation not only against your current cloud bill, but against what a disciplined cloud optimization program could achieve. Rightsizing, storage improvements, better instance strategy, autoscaling discipline, reserved capacity planning, architecture cleanup, and proper FinOps can all change the economics without requiring anyone to rediscover the intimate emotional texture of broken hardware.

The bill behind the bill

What has stayed with me about this story is that it was never really a story about AWS.

It was a story about accounting for the wrong thing.

The visible bill was treated as the entire problem. The hidden work behind the bill was treated as background scenery. Once the company moved off AWS, the scenery walked to the front of the stage and began sending invoices.

That is the trap.

Cloud can absolutely be expensive. Plenty of organizations run it badly and pay for the privilege. But on-premises is not automatically the sober adult in the room. Quite often, it is simply a different payment model, one that hides more of the cost in staffing, slower delivery, operational fragility, maintenance overhead, and all the unlovely little chores that cloud platforms had been taking care of out of sight.

The lesson from this case was not that every workload belongs in AWS forever. It was that infrastructure decisions become dangerous when they are made in reaction to irritation rather than in response to a full economic picture.

Leaving the cloud may still be the right answer for some organizations. For many others, the more useful answer is much less theatrical. Use the cloud better. Govern it better. Design it properly. Understand what you are paying for before deciding you would prefer to rebuild it yourself.

A large monthly cloud bill can be offensive to look at.

The bill that arrives after a bad attempt to escape it is usually less offensive than heartbreaking.

And heartbreak, unlike EC2, rarely comes with autoscaling.

March 30, 2026 by Fernando SRE Cloud stuff Computer Science stuff

Why Crossplane is the Kubernetes therapy your multi-cloud setup needs

Let us be perfectly honest about multi-cloud environments. They are not a harmonious symphony of computing power. They are a logistical nightmare, roughly equivalent to hosting a dinner party where one guest only eats raw vegan food, another demands a deep-fried turkey, and the third will only consume blue candy. You are running around three different kitchens trying to keep everyone alive and happy while speaking three different languages.

For years, we relied on Terraform or its open-source sibling OpenTofu to manage this chaos. These tools are fantastic, but they come with a terrifying piece of baggage known as the state file. The state file is essentially a fragile, highly sensitive diary holding the deepest, darkest secrets of your infrastructure. If that file gets corrupted or someone forgets to lock it, your cloud provider develops sudden amnesia and forgets where it put the database.

Kubernetes evolved quite a bit while we were busy babysitting our state files. It stopped being just a container orchestrator and started trying to run the whole house. Every major cloud provider released their own Kubernetes operator. Suddenly, you could manage a storage bucket or a database directly from inside your cluster. But there was a catch. The operators refused to speak to each other. You essentially hired a team of brilliant specialists who absolutely hate each other.

This is exactly where Crossplane steps in to act as the universal, unbothered therapist for your infrastructure.

Meet your new obsessive infrastructure butler

Crossplane does not care about vendor rivalries. It installs itself into your Kubernetes cluster and uses the native Kubernetes reconciliation loop to manage your external cloud resources.

If you are unfamiliar with the reconciliation loop, think of it as an aggressively helpful, obsessive-compulsive butler. You hand this butler a piece of YAML paper stating that you require a specific storage bucket in a specific region. The butler goes out, builds the bucket, and then stands there staring at it forever. If a rogue developer logs into the cloud console and manually deletes that bucket, the butler simply builds it again before the developer has even finished their morning coffee. It is relentless, slightly unnerving, and exactly what you want to keep your infrastructure in check.

Because Crossplane lives inside Kubernetes, you do not need to run a separate pipeline just to execute an infrastructure plan. The cluster itself is the engine. You declare what you want, and the cluster makes reality match your desires.

The anatomy of a multi-cloud combo meal

To understand how this actually works without getting bogged down in endless documentation, you only need to understand three main concepts.

First, you have Providers. These are the translator modules. You install the AWS Provider, the Azure Provider, or the Google Cloud Provider, and suddenly your Kubernetes cluster knows how to speak their specific dialects.

Next, you have Managed Resources. These are the raw ingredients. A single virtual machine, a single virtual network, or a single database instance. You can deploy these directly, but asking a developer to configure twenty different Managed Resources just to get a working application is like handing them a live chicken, a sack of flour, and telling them to make a sandwich.

This brings us to the real magic of Crossplane, which is the Composite Resource.

Composite Resources allow you to bundle all those raw ingredients into a single, easy-to-digest package. It is the infrastructure equivalent of a fast-food drive-through. A developer does not need to know about subnets, security groups, or routing tables. They just submitted a claim for a “Standard Web Database” value meal. Crossplane takes that simple request and translates it into the complex web of resources required behind the scenes.

Looking at the code without falling asleep

To prove that this is not just theoretical nonsense, let us look at what it takes to command two completely different cloud providers from the exact same place.

Normally, doing this requires switching between different tools, authenticating multiple times, and praying you do not execute the wrong command in the wrong terminal. With Crossplane, you just throw your YAML files into the cluster.

Here is a sanitized, totally harmless example of how you might ask AWS for a storage bucket.

apiVersion: s3.aws.upbound.io/v1beta1
kind: Bucket
metadata:
  name: acme-corp-financial-reports
spec:
  forProvider:
    region: eu-west-1
  providerConfigRef:
    name: aws-default-provider

And right next to it, in the exact same directory, you can drop this snippet to demand a Resource Group from Azure.

apiVersion: azure.upbound.io/v1beta1
kind: ResourceGroup
metadata:
  name: rg-marketing-dev-01
spec:
  forProvider:
    location: West Europe
  providerConfigRef:
    name: azure-default-provider

You apply these manifests, and Crossplane handles the authentication, the API calls, and the aggressive babysitting of the resources. No Terraform state file is required. It is completely stateless GitOps magic.

The ugly truth about operating at scale

Of course, getting rid of the state file is like going to a music festival without a cell phone. It sounds incredibly liberating until you lose your friends and cannot find your way home.

Operating Crossplane at scale is not always a walk in the park. When things go wrong during provisioning, and they absolutely will go wrong, you do not get a neatly formatted error summary. Because there is no central state file to reference, finding out why a resource failed requires interrogating the Kubernetes API directly.

You type a command to check the status of your resources, and the cluster vomits a massive wall of text onto your screen. It is like trying to find a typo in a phone book while someone shouts at you in a foreign language. Running multiple kubectl commands just to figure out why an Azure database refused to spin up gets very old, very fast.

To survive this chaos, you cannot rely on manual terminal commands. You must pair Crossplane with a dedicated GitOps tool like ArgoCD or FluxCD.

These tools act as the adult in the room. They keep track of what was actually deployed, provide a visual dashboard, and translate the cluster’s internal panic into something a human being can actually read. They give you the visibility that Crossplane lacks out of the box.

Ultimately, moving to Crossplane is a paradigm shift. It requires letting go of the comfortable, procedural workflows of traditional infrastructure as code and embracing the chaotic, eventual consistency of Kubernetes. It has a learning curve that might make you pull your hair out initially, but once you set up your Composite Resources and your GitOps pipelines, you will never want to go back to juggling state files again.

Azure DevOps to GCP without static keys

Static service account keys have an odd domestic quality to them. They begin life as a sensible convenience and, after a few months, end up tucked into variable groups, copied into wikis, or lurking in a repository with the innocent menace of a spare house key under a flowerpot. They work, certainly. So does leaving your front door on the latch. The problem is not whether it works. The problem is how long you can keep pretending it is a good idea.

This article shows how to let Azure DevOps authenticate to Google Cloud without creating or storing a long-lived service account key. Instead, Azure DevOps presents a short-lived OIDC token, Google Cloud checks that token against a workload identity provider, and the pipeline receives temporary Google credentials only for the duration of the job.

The result is cleaner, safer, and far less likely to produce the sort of sentence nobody enjoys reading in a postmortem, namely, “we found an old credential in a place that should not have contained a credential.”

Why this setup is worth the trouble

The old pattern is familiar. You create a Google Cloud service account, download a JSON key, store it somewhere “temporary”, and then spend the next year hoping nobody has copied it into four other places. Even if the key never leaks, it still becomes one more secret to rotate, one more thing to explain to auditors, and one more awkward dependency between your pipeline and a file that should not really exist.

Workload Identity Federation replaces that with short-lived trust. Azure DevOps proves who it is at runtime. Google Cloud verifies that proof. No static key is issued, no secret needs to be rotated, and there is much less housekeeping disguised as security.

Strictly speaking, you can grant permissions directly to the federated principal in Google Cloud. In this article, I am using service account impersonation instead. It is a little easier to reason about, it fits neatly with how many teams already model CI identities, and it behaves consistently across a wide range of Google Cloud services.

What is actually happening

Under the hood, the flow is less mystical than it first appears.

Azure DevOps has a service connection that can mint an OIDC ID token for the running pipeline. Google Cloud has a workload identity pool and an OIDC provider configured to trust tokens issued by that Azure DevOps organization. When the pipeline runs, it retrieves the token, writes a small credential configuration file, and uses that file to exchange the token for temporary Google credentials. Those credentials are then used to impersonate a Google Cloud service account with the exact roles needed for the job.

If you prefer a more ordinary analogy, think of it as a reception desk in an office building. Azure DevOps arrives with a temporary visitor badge. Google Cloud checks whether the badge was issued by a reception desk it trusts, whether it belongs to the expected visitor, and whether that visitor is allowed through the next door. If all of that checks out, access is granted for a while and then expires. Nobody hands over the master keys to the building.

Preparing Azure DevOps

The Azure DevOps side is simpler than it first looks, although the menus do their best to suggest otherwise.

Create an Azure Resource Manager service connection in your Azure DevOps project and use these settings:

Identity type: App registration (automatic)
Credential: Workload identity federation
Scope level: Subscription

Yes, you still need to select a subscription even if your real destination is Google Cloud. It feels slightly like being asked for your train ticket while boarding a ferry, but that is the supported path.

Once the service connection is saved, note down two values from the Workload Identity federation details section:

Issuer
Subject identifier

The issuer identifies your Azure DevOps organization. The subject identifier identifies the service connection. In practice, the subject identifier follows this pattern:

sc://your-organization/your-project/your-service-connection

That detail matters because Google Cloud will ultimately trust this specific identity, not merely “some pipeline from somewhere in the general direction of Azure.”

A practical naming note is worth making here. Choose a stable, descriptive service connection name early. Renaming things later is always possible in the same way as replacing the plumbing in a bathroom is possible. The word possible is doing quite a lot of work.

Teaching Google Cloud to trust Azure DevOps

Now we move to Google Cloud, where the important trick is to trust the right thing in the right way.

Create a dedicated workload identity pool and OIDC provider. You can do this from the console, but the CLI version is easier to keep, review, and repeat.

export IDENTITY_PROJECT_ID="acme-identity-hub"
export IDENTITY_PROJECT_NUMBER="998877665544"
export POOL_ID="ado-pool"
export PROVIDER_ID="ado-oidc"
export ISSUER_URI="https://vstoken.dev.azure.com/11111111-2222-3333-4444-555555555555"

# Enable the required APIs

gcloud services enable \
  iam.googleapis.com \
  cloudresourcemanager.googleapis.com \
  iamcredentials.googleapis.com \
  sts.googleapis.com \
  --project="$IDENTITY_PROJECT_ID"

# Create the workload identity pool

gcloud iam workload-identity-pools create "$POOL_ID" \
  --project="$IDENTITY_PROJECT_ID" \
  --location="global" \
  --display-name="Azure DevOps pool" \
  --description="Federation trust for Azure DevOps pipelines"

# Create the OIDC provider

gcloud iam workload-identity-pools providers create-oidc "$PROVIDER_ID" \
  --project="$IDENTITY_PROJECT_ID" \
  --location="global" \
  --workload-identity-pool="$POOL_ID" \
  --display-name="Azure DevOps provider" \
  --issuer-uri="$ISSUER_URI" \
  --allowed-audiences="api://AzureADTokenExchange" \
  --attribute-mapping="google.subject=assertion.sub.extract('/sc/{service_connection}')"

There are two details here that are easy to get wrong.

First, the allowed audience for the provider is “api://AzureADTokenExchange”. It is not a random per-connection UUID, and it is not the audience string that later appears inside the external account credential file used by the pipeline.

Second, the attribute mapping should not map “google.subject” to “assertion.aud”. For Azure DevOps, the supported workaround for the 127 byte subject limit is to extract the service connection portion from the “sub” claim:

google.subject=assertion.sub.extract('/sc/{service_connection}')

This matters because the raw Azure DevOps subject can be too long for “google.subject”. Extracting the useful part solves the length issue neatly and still gives Google Cloud a stable subject to authorize.

You do not need an attribute condition for Azure DevOps. The issuer is already tenant-specific, which keeps this case pleasantly less dramatic than some other CI systems.

Creating the service account

Next, create the Google Cloud service account that your pipeline will impersonate.

The exact roles depend on what your pipeline needs to do. If the job only uploads artifacts to Cloud Storage, grant a storage role and stop there. If it deploys Cloud Run services, grant the Cloud Run roles it genuinely needs. This is one of those rare moments in cloud engineering where restraint is both morally admirable and operationally useful.

Here is a simple example:

export DEPLOY_PROJECT_ID="acme-observability-dev"
export SERVICE_ACCOUNT_NAME="ci-deployer"
export SERVICE_ACCOUNT_EMAIL="${SERVICE_ACCOUNT_NAME}@${DEPLOY_PROJECT_ID}.iam.gserviceaccount.com"
export FEDERATED_SUBJECT="your-organization/your-project/your-service-connection"

# Create the service account

gcloud iam service-accounts create "$SERVICE_ACCOUNT_NAME" \
  --project="$DEPLOY_PROJECT_ID" \
  --display-name="CI deployer for Azure DevOps"

# Grant only the roles your pipeline really needs

gcloud projects add-iam-policy-binding "$DEPLOY_PROJECT_ID" \
  --member="serviceAccount:${SERVICE_ACCOUNT_EMAIL}" \
  --role="roles/storage.objectAdmin"

# Allow the federated Azure DevOps identity to impersonate the service account

gcloud iam service-accounts add-iam-policy-binding "$SERVICE_ACCOUNT_EMAIL" \
  --project="$DEPLOY_PROJECT_ID" \
  --role="roles/iam.workloadIdentityUser" \
  --member="principal://iam.googleapis.com/projects/${IDENTITY_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${POOL_ID}/subject/${FEDERATED_SUBJECT}"

The “FEDERATED_SUBJECT” value must match the subject produced by your attribute mapping. In plain English, that means the service connection identity that Google Cloud should trust. If the pool lives in one project and the service account lives in another, that is fine, but be careful to use the project number of the identity project in the principal URI.

Building the pipeline

Now for the part everyone actually came for.

The pipeline below uses the AzureCLI task to obtain the Azure DevOps OIDC token, stores it in a temporary file, writes an external account credential file for Google Cloud, signs in with “gcloud”, and then runs a test command.

trigger:
- main

pool:
  vmImage: 'ubuntu-latest'

variables:
  azureServiceConnection: 'gcp-federation-prod'
  gcpProjectId: 'acme-observability-dev'
  gcpProjectNumber: '998877665544'
  gcpPoolId: 'ado-pool'
  gcpProviderId: 'ado-oidc'
  gcpServiceAccount: 'ci-deployer@acme-observability-dev.iam.gserviceaccount.com'
  GOOGLE_APPLICATION_CREDENTIALS: '$(Pipeline.Workspace)/gcp-wif.json'

steps:
- checkout: self

- task: AzureCLI@2
  displayName: 'Authenticate to Google Cloud with workload identity federation'
  inputs:
    azureSubscription: '$(azureServiceConnection)'
    addSpnToEnvironment: true
    scriptType: 'bash'
    scriptLocation: 'inlineScript'
    inlineScript: |
      set -euo pipefail

      TOKEN_FILE="$(Pipeline.Workspace)/ado-token.jwt"
      printf '%s' "$idToken" > "$TOKEN_FILE"

      cat > "$GOOGLE_APPLICATION_CREDENTIALS" <<EOF
      {
        "type": "external_account",
        "audience": "//iam.googleapis.com/projects/$(gcpProjectNumber)/locations/global/workloadIdentityPools/$(gcpPoolId)/providers/$(gcpProviderId)",
        "subject_token_type": "urn:ietf:params:oauth:token-type:jwt",
        "token_url": "https://sts.googleapis.com/v1/token",
        "credential_source": {
          "file": "$TOKEN_FILE"
        },
        "service_account_impersonation_url": "https://iamcredentials.googleapis.com/v1/projects/-/serviceAccounts/$(gcpServiceAccount):generateAccessToken"
      }
      EOF

      gcloud auth login --cred-file="$GOOGLE_APPLICATION_CREDENTIALS" --quiet
      gcloud config set project "$(gcpProjectId)" --quiet

      echo "Authenticated as federated workload"
      gcloud storage buckets list --limit=5

A couple of details are doing more work here than they appear to be doing.

“addSpnToEnvironment: true” is essential. Without it, the task does not expose the “idToken” variable to your script. The pipeline then behaves like a very polite person who has shown up for an exam without bringing a pen.

The “audience” inside the generated JSON file is also important. This is the full resource name of the workload identity provider in Google Cloud. It is not the same thing as the allowed audience configured on the provider itself. The two values serve different purposes, which is perfectly reasonable once you know it and deeply annoying before you do.

An alternative credential file approach

If you prefer to generate the configuration file with “gcloud” rather than writing JSON inline, you can do that too:

gcloud iam workload-identity-pools create-cred-config \
  "projects/${gcpProjectNumber}/locations/global/workloadIdentityPools/${gcpPoolId}/providers/${gcpProviderId}" \
  --service-account="${gcpServiceAccount}" \
  --credential-source-file="$TOKEN_FILE" \
  --output-file="$GOOGLE_APPLICATION_CREDENTIALS"

That version is perfectly serviceable and often a little tidier if you dislike heredocs. I have shown the explicit JSON version in the main pipeline because it makes each moving part visible, which is useful while learning or troubleshooting.

Common pitfalls

There are a few places where people lose an afternoon.

The token exists, but the pipeline still fails

Make sure the AzureCLI task is using the correct service connection and that “addSpnToEnvironment” is enabled. If “$idToken” is empty, the problem is usually on the Azure DevOps side, not in Google Cloud.

The principal binding looks right, but impersonation is denied

Check the project number in the principal URI. It must be the project number that owns the workload identity pool, not necessarily the project where the service account lives.

Also, check the federated subject. Because of the attribute mapping, the subject is the extracted service connection path, not the raw OIDC subject, and not a made-up shorthand invented during a stressful coffee break.

The pipeline freezes on an authentication prompt

Use ‘–quiet’ with ‘gcloud auth login’ and similar commands. CI jobs are many things, but conversationalists they are not.

Hosted agents are not available

If your Azure DevOps organization has not yet been granted hosted parallelism, use a self-hosted agent temporarily. In that case, make sure the machine already has ‘az’ and ‘gcloud’ installed and available on the ‘PATH’.

A minimal self-hosted pool declaration looks like this:

pool:
  name: 'Default'

On Windows, remember to switch the script type to PowerShell or PowerShell Core and adjust the environment variable syntax accordingly.

Leaving the keys behind

This setup removes one of the more tiresome habits of cross-cloud automation, namely, manufacturing a secret only to spend the rest of its natural life protecting it from yourself. Azure DevOps can obtain a short-lived token, Google Cloud can verify it, and your pipeline can impersonate a tightly scoped service account without anybody downloading a JSON key and promising to delete it later.

That is the technical benefit. The practical benefit is even nicer. Once this is in place, your pipeline starts to feel less like a cupboard full of labelled jars, some of which may or may not contain explosives, and more like a system that knows who it is, proves it when asked, and then gets on with the job.

Which, in cloud engineering, is about as close as one gets to elegance.

March 23, 2026 by Fernando SRE Cloud stuff DevOps stuff SRE stuff

Surviving the Ingress NGINX apocalypse without breaking a sweat

Look at the calendar. It is March 2026. The deadline we have been hearing about for months has officially arrived, and across the globe, engineers are clutching their coffee mugs, staring at their terminals, and waiting for their Kubernetes clusters to spontaneously combust. There is a palpable panic in the air. Tech forums are overflowing with dramatic declarations that the internet is broken, all because a specific piece of software is officially retiring.

Take a deep breath. Your servers are not going to melt. Traffic is not going to suddenly hit a brick wall. But you do need to pack up your things and move, because the building you are living in just fired its maintenance staff.

To understand how we got here and how to get out alive, we need to stop treating this retirement like a digital Greek tragedy and start looking at it like a mundane eviction notice. We are going to peel back the layers of this particular onion, dry our eyes, and figure out how to migrate our traffic routing without breaking a sweat.

The great misunderstanding of what is actually dying

Before we start packing boxes, we need to address the rampant identity confusion that has turned a routine software lifecycle event into a source of mass hysteria. A lot of online discussion has mixed up three entirely different things, treating them like a single, multi-headed beast. Let us separate them.

First, there is NGINX. This is the web server and reverse proxy that has been moving packets around the internet since you were still excited about flip phones. NGINX is fine. Nobody is retiring NGINX. It is healthy, wealthy, and continues to route a massive chunk of the global internet.

Second, there is the Ingress API. This is the Kubernetes object you use to describe your HTTP and HTTPS routing rules. It is just a set of instructions. The Ingress API is not being removed. The Kubernetes maintainers are not going to sneak into your cluster at night and delete your YAML files.

Finally, there is the Ingress NGINX controller. This is the community-maintained piece of software that reads your Ingress API instructions and configures NGINX to actually execute them. This specific controller, maintained by a group of incredibly exhausted volunteers, is the thing that is retiring. As of right now, March 2026, it is no longer receiving updates, bug fixes, or security patches.

That distinction avoids most of the confusion. The bouncer at the door of your nightclub is retiring, but the nightclub itself is still open, and the rules of who gets in remain the same. You just need to hire a new bouncer.

Why the bouncer finally walked off the job

To understand why the community Ingress NGINX controller is packing its bags, you have to look at what we forced it to do. For years, this controller has been the stoic bouncer at the entrance of your Kubernetes cluster. It stood in the rain, checked the TLS certificates, and decided which request got into the VIP pod and which one got thrown out into the alley.

But the Ingress API itself was fundamentally limited. It only understood the basics. It knew about hostnames and paths, but it had no idea how to handle anything complex, like weighted canary deployments, custom header manipulation, or rate limiting.

Because we developers are needy creatures who demand complex routing, we found a workaround. We started using annotations. We slapped sticky notes all over the bouncer’s forehead. We wrote cryptic instructions on these notes, telling the controller to inject custom configuration snippets directly into the underlying NGINX engine.

Eventually, the bouncer was walking around completely blinded by thousands of contradictory sticky notes. Maintaining this chaotic system became a nightmare for the open-source volunteers. They were basically performing amateur dental surgery in the dark, trying to patch security holes in a system entirely built out of user-injected string workarounds. The technical debt became a mountain, and the maintainers rightly decided they had had enough.

The terrifying reality of unpatched edge components

If the controller is not going to suddenly stop working today, you might be tempted to just leave it running. This is a terrible idea.

Leaving an obsolete, unmaintained Ingress controller facing the public internet is exactly like leaving the front door of your house under the strict protection of a scarecrow. The crows might stay away for the first week. But eventually, the local burglars will realize your security system is made of straw and old clothes.

Edge proxies are the absolute favorite targets for attackers. They sit right on the boundary between the wild, unfiltered internet and your soft, vulnerable application data. When a new vulnerability is discovered next month, there will be no patch for your retired Ingress NGINX controller. Attackers will scan the internet for that specific outdated signature, and they will walk right past your scarecrow. Do not be the person explaining to your boss that the company data was stolen because you did not want to write a few new YAML files.

Meet the new security firm known as Gateway API

If Ingress was a single bouncer overwhelmed by sticky notes, the new standard, known as Gateway API, is a professional security firm with distinct departments.

The core problem with Ingress was that it forced the infrastructure team and the application developers to fight over the same file. The platform engineer wanted to manage the TLS certificates, while the developer just wanted to route traffic to their new shopping cart service.

Gateway API fixes this by splitting the responsibilities into different objects. You have a GatewayClass (the type of security firm), a Gateway (the physical building entrance managed by the platform team), and an HTTPRoute (the specific room VIP lists managed by the developers). It is structured, it is typed, and most importantly, it drastically reduces the need for those horrible annotation sticky notes.

You do not have to migrate to the Gateway API. You can simply switch to a different, commercially supported Ingress controller that still reads your old files. But if you are going to rip off the bandage and change your routing infrastructure, you might as well upgrade to the modern standard.

A before-and-after infomercial for your YAML files

Let us look at a practical example. Has this ever happened to you? Are your YAML files bloated, confusing, and causing you physical pain to read? Look at this disastrous piece of legacy Ingress configuration.

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: desperate-cries-for-help
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /$2
    nginx.ingress.kubernetes.io/use-regex: "true"
    nginx.ingress.kubernetes.io/server-snippet: |
      location ~* ^/really-bad-regex/ {
        return 418;
      }
    nginx.ingress.kubernetes.io/canary: "true"
    nginx.ingress.kubernetes.io/canary-weight: "15"
spec:
  ingressClassName: nginx
  rules:
  - host: chaotic-store.example.local
    http:
      paths:
      - path: /catalog(/|$)(.*)
        pathType: Prefix
        backend:
          service:
            name: catalog-service-v2
            port:
              number: 8080

This is not a configuration. This is a hostage note. You are begging the controller to understand regex rewrites and canary deployments by passing simple strings through annotations.

Now, wipe away those tears and look at the clean, structured beauty of an HTTPRoute in the Gateway API world.

apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  name: calm-and-collected-routing
spec:
  parentRefs:
  - name: main-company-gateway
  hostnames:
  - "smooth-store.example.local"
  rules:
  - matches:
    - path:
        type: PathPrefix
        value: /catalog
    filters:
    - type: URLRewrite
      urlRewrite:
        path:
          type: ReplacePrefixMatch
          replacePrefixMatch: /
    backendRefs:
    - name: catalog-service-v1
      port: 8080
      weight: 85
    - name: catalog-service-v2
      port: 8080
      weight: 15

Look at that. No sticky notes. No injected server snippets. The routing weights and the URL rewrites are native, structured fields. Your linter can actually read this and tell you if you made a typo before you deploy it and take down the entire production environment.

A twelve-step rehabilitation program for your cluster

You cannot just delete the old controller on a Friday afternoon and hope for the best. You need a controlled rehabilitation program for your cluster. Treat this as a serious infrastructure project.

Phase 1: The honest inventory

You need to look at yourself in the mirror and figure out exactly what you have deployed. Find every single Ingress object in your cluster. Document every bizarre annotation your developers have added over the years. You will likely find routing rules for services that were decommissioned three years ago.

Phase 2: Choosing your new guardian

Evaluate the replacements. If you want to stick with NGINX, look at the official F5 NGINX Ingress Controller. If you want something modern, look at Envoy-based solutions like Gateway API implementations from Cilium, Istio, or Contour. Deploy your choice into a sandbox environment.

Phase 3: The great translation

Start converting those sticky notes. Take your legacy Ingress objects and translate them into Gateway API routes, or at least clean them up for your new controller. This is the hardest part. You will have to decipher what nginx.ingress.kubernetes.io/configuration-snippet actually does in your specific context.

Phase 4: The side-by-side test

Run the new controller alongside the retiring community one. Use a test domain. Throw traffic at it. Watch the metrics. Ensure that your monitoring dashboards and alerting rules still work, because the new controller will expose entirely different metric formats.

Phase 5: The DNS switch

Slowly move your DNS records from the old load balancer to the new one. Do this during business hours when everyone is awake and heavily caffeinated, not at 2 AM on a Sunday.

The final word on not panicking

If you need a message to send to your management team today, keep it simple. Tell them the community ingress-nginx controller is now officially unmaintained. Assure them the website is not down, but inform them that staying on this software is a ticking security time bomb. You need time and resources to move to a modern implementation.

The real lesson here is not that Kubernetes is unstable. It is that the software world relies heavily on the unpaid labor of open-source maintainers. When a critical project no longer has enough volunteers to hold back the tide of technical debt, responsible engineering teams do not sit around complaining on internet forums. They say thank you for the years of free service, they roll up their sleeves, and they migrate before the lack of maintenance becomes an incident report.

March 17, 2026 by Fernando SRE DevOps stuff Kubernetes SRE stuff

AWSMap for smarter AWS migrations

Most AWS migrations begin with a noble ambition and a faintly ridiculous problem.

The ambition is to modernise an estate, reduce risk, tidy the architecture, and perhaps, if fortune smiles, stop paying for three things nobody remembers creating.

The ridiculous problem is that before you can migrate anything, you must first work out what is actually there.

That sounds straightforward until you inherit an AWS account with the accumulated habits of several teams, three naming conventions, resources scattered across regions, and the sort of IAM sprawl that suggests people were granting permissions with the calm restraint of a man feeding pigeons. At that point, architecture gives way to archaeology.

I do not work for AWS, and this is not a sponsored love letter to a shiny console feature. I am an AWS and GCP architect working in the industry, and I have used AWSMap when assessing environments ahead of migration work. The reason I am writing about it is simple enough. It is one of those practical tools that solves a very real problem, and somehow remains less widely known than it deserves.

AWSMap is a third-party command-line utility that inventories AWS resources across regions and services, then lets you explore the result through HTML reports, SQL queries, and plain-English questions. In other words, it turns the early phase of a migration from endless clicking into something closer to a repeatable assessment process.

That does not make it perfect, and it certainly does not replace native AWS services. But in the awkward first hours of understanding an inherited environment, it can be remarkably useful.

The migration problem before the migration

A cloud migration plan usually looks sensible on paper. There will be discovery, analysis, target architecture, dependency mapping, sequencing, testing, cutover, and the usual brave optimism seen in project plans everywhere.

In reality, the first task is often much humbler. You are trying to answer questions that should be easy and rarely are.

What is running in this account?

Which regions are actually in use?

Are there old snapshots, orphaned EIPs, forgotten load balancers, or buckets with names that sound important enough to frighten everyone into leaving them alone?

Which workloads are genuinely active, and which are just historical luggage with a monthly invoice attached?

You can answer those questions from the AWS Management Console, of course. Given enough tabs, enough patience, and a willingness to spend part of your afternoon wandering through services you had not planned to visit, you will eventually get there. But that is not a particularly elegant way to begin a migration.

This is where AWSMap becomes handy. Instead of treating discovery as a long guided tour of the console, it treats it as a data collection exercise.

What AWSMap does well

At its core, AWSMap scans an AWS environment and produces an inventory of resources. The current public package description on PyPI describes it as covering more than 150 AWS services, while version 1.5.0 covers 140 plus services, which is a good reminder that the coverage evolves. The important point is not the exact number on a given Tuesday morning, but that it covers a broad enough slice of the estate to be genuinely useful in early assessments.

What makes the tool more interesting is what it does after the scan.

It can generate a standalone HTML report, store results locally in SQLite, let you query the inventory with SQL, run named audit queries, and translate plain-English prompts into database queries without sending your infrastructure metadata off to an LLM service. The release notes for v1.5.0 describe local SQLite storage, raw SQL querying, named queries, typo-tolerant natural language questions, tag filtering, scoped account views, and browsable examples.

That combination matters because migrations are rarely single, clean events. They are usually a series of discoveries, corrections, and mildly awkward conversations. Having the inventory preserved locally means the account does not need to be rediscovered from scratch every time someone asks a new question two days later.

The report you can actually hand to people

One of the surprisingly practical parts of AWSMap is the report output.

The tool can generate a self-contained HTML report that opens locally in a browser. That sounds almost suspiciously modest, but it is useful precisely because it is modest. You can attach it to a ticket, share it with a teammate, or open it during a workshop without building a whole reporting pipeline first. The v1.5.0 release notes describe the report as a single, standalone HTML file with filtering, search, charts, and export options.

That makes it suitable for the sort of migration meeting where someone says, “Can we quickly check whether eu-west-1 is really the only active region?” and you would rather not spend the next ten minutes performing a slow ritual through five console pages.

A simple scan might look like this:

awsmap -p client-prod

If you want to narrow the blast radius a bit and focus on a few services that often matter early in migration discovery, you could do this:

awsmap -p client-prod -s ec2,rds,elb,lambda,iam

And if the account is a thicket of shared infrastructure, tags can help reduce the noise:

awsmap -p client-prod -t Environment=Production -t Owner=platform-team

That kind of filtering is helpful when the account contains equal parts business workload and historical clutter, which is to say, most real accounts.

Why SQLite is more important than it sounds

The feature I like most is not the report. It is the local SQLite database.

Every scan can be stored locally, so the inventory becomes queryable over time instead of vanishing the moment the terminal output scrolls away. The default local database path is ‘~/.awsmap/inventory.db’, and the scan results from different runs can accumulate there for later analysis.

This changes the character of the tool quite a bit. It stops being a disposable scanner and becomes something closer to a field notebook.

Suppose you scan a client account today, then return to the same work three days later, after someone mentions an old DR region nobody had documented. Without persistence, you start from scratch. With persistence, you ask the database.

That is a much more civilised way to work.

A query for the busiest services in the collected inventory might look like this:

awsmap query "SELECT service, COUNT(*) AS total
FROM resources
GROUP BY service
ORDER BY total DESC
LIMIT 12"

And a more migration-focused query might be something like:

awsmap query "SELECT account, region, service, name
FROM resources
WHERE service IN ('ec2', 'rds', 'elb', 'lambda')
ORDER BY account, region, service, name"

Neither query is glamorous, but migrations are not built on glamour. They are built on being able to answer dull, important questions reliably.

Security and hygiene checks without the acrobatics

AWSMap also includes named queries for common audit scenarios, which is useful for two reasons.

First, most people do not wake up eager to write SQL joins against IAM relationships. Second, migration assessments almost always drift into security checks sooner or later.

The public release notes describe named queries for scenarios such as admin users, public S3 buckets, unencrypted EBS volumes, unused Elastic IPs, and secrets without rotation.

That means you can move from “What exists?” to “What looks questionable?” without much ceremony.

For example:

awsmap query -n admin-users
awsmap query -n public-s3-buckets
awsmap query -n ebs-unencrypted
awsmap query -n unused-eips

Those are not, strictly speaking, migration-only questions. But they are precisely the kind of questions that surface during migration planning, especially when the destination design is meant to improve governance rather than merely relocate the furniture.

Asking questions in plain English

One of the nicer additions in the newer version is the ability to ask plain-English questions.

That is the sort of feature that normally causes one to brace for disappointment. But here the approach is intentionally local and deterministic. This functionality is a built-in parser rather than an LLM-based service, which means no API keys, no network calls to an external model, and no need to ship resource metadata somewhere mysterious.

That matters in enterprise environments where the phrase “just send the metadata to a third-party AI service” tends to receive the warm reception usually reserved for wasps.

Some examples:

awsmap ask show me lambda functions by region
awsmap ask list databases older than 180 days
awsmap ask find ec2 instances without Owner tag

Even when the exact wording varies, the basic idea is appealing. Team members who do not want to write SQL can still interrogate the inventory. That lowers the barrier for using the tool during workshops, handovers, and review sessions.

Where AWSMap fits next to AWS native services

This is the part worth stating clearly.

AWSMap is useful, but it is not a replacement for AWS Resource Explorer, AWS Config, or every other native mechanism you might use for discovery, governance, and inventory.

AWS Resource Explorer can search across supported resource types and, since 2024, can also discover all tagged AWS resources using the ‘tag:all’ operator. AWS documentation also notes an important limitation for IAM tags in Resource Explorer search.

AWS Config, meanwhile, continues to expand the resource types it can record, assess, and aggregate. AWS has announced multiple additions in 2025 and 2026 alone, which underlines that the native inventory and compliance story is still moving quickly.

So why use AWSMap at all?

Because its strengths are slightly different.

It is local.

It is quick to run.

It gives you a portable HTML report.

It stores results in SQLite for later interrogation.

It lets you query the inventory directly without setting up a broader governance platform first.

That makes it particularly handy in the early assessment phase, in consultancy-style discovery work, or in those awkward inherited environments where you need a fast baseline before deciding what the more permanent controls should be.

The weak points worth admitting

No serious article about a tool should pretend the tool has descended from heaven in perfect condition, so here are the caveats.

First, coverage breadth is not the same thing as universal depth. A tool can support a large number of services and still provide uneven detail between them. That is true of almost every inventory tool ever made.

Second, the quality of the result still depends on the credentials and permissions you use. If your access is partial, your inventory will be partial, and no amount of cheerful HTML will alter that fact.

Third, local storage is convenient, but it also means you should be disciplined about how scan outputs are handled on your machine, especially if you are working with client environments. Convenience and hygiene should remain on speaking terms.

Fourth, for organisation-wide governance, compliance history, managed rules, and native integrations, AWS services such as Config still have an obvious place. AWSMap is best seen as a sharp assessment tool, not a universal control plane.

That is not a criticism so much as a matter of proper expectations.

A practical workflow for migration discovery

If I were using AWSMap at the start of a migration assessment, the workflow would be something like this.

First, run a broad scan of the account or profile you care about.

awsmap -p client-prod

Then, if the account is noisy, refine the scope.

awsmap -p client-prod -s ec2,rds,elb,iam,route53
awsmap -p client-prod --exclude-defaults

Next, use a few named queries to surface obvious issues.

awsmap query -n public-s3-buckets
awsmap query -n secrets-no-rotation
awsmap query -n admin-users

After that, ask targeted questions in either SQL or plain English.

awsmap ask list load balancers by region
awsmap ask show databases with no backup tag
awsmap query "SELECT region, COUNT(*) AS total
FROM resources
WHERE service='ec2'
GROUP BY region
ORDER BY total DESC"

And finally, keep the HTML report and local inventory as a baseline for later design discussions.

That is where the tool earns its keep. It gives you a reasonably fast, reasonably structured picture of an estate before the migration plan turns into a debate based on memory, folklore, and screenshots in old slide decks.

When the guessing stops

There is a particular kind of misery in cloud work that comes from being asked to improve an environment before anyone has properly described it.

Tools do not eliminate that misery, but some of them reduce it to a more manageable size.

AWSMap is one of those.

It is not the only way to inventory AWS resources. It is not a substitute for native governance services. It is not magic. But it is practical, fast to understand, and surprisingly helpful when the first job in a migration is simply to stop guessing.

That alone makes it worth knowing about.

And in cloud migrations, a tool that helps replace guessing with evidence is already doing better than half the room.

March 14, 2026 by Fernando SRE Cloud stuff DevOps stuff SRE stuff

How a Kubernetes Pod comes to life

Run ‘kubectl apply -f pod.yaml’ and Kubernetes has the good manners to make it look simple. You hand over a neat little YAML file, press Enter, and for a brief moment, it feels as if you have politely asked the cluster to start a container.

That is not what happened.

What you actually did was file a request with a distributed bureaucracy. Several components now need to validate your paperwork, record your wishes for posterity, decide where your Pod should live, prepare networking and storage, ask a container runtime to do the heavy lifting, and keep watching the whole arrangement in case it misbehaves. Kubernetes is extremely good at hiding all this. It has the same talent as a hotel lobby. Everything looks calm and polished, while somewhere behind the walls, people are hauling luggage, changing sheets, arguing about room allocation, and trying not to let anything catch fire.

This article follows that process from the moment you submit a manifest to the moment the Pod disappears again. To keep the story tidy, I will use a standalone Pod. In real production environments, Pods are usually created by higher-level controllers such as Deployments, Jobs, or StatefulSets. The Pod is still the thing that ultimately gets scheduled and runs, so it remains the most useful unit to study when you want to understand what Kubernetes is really doing.

The YAML lands on the front desk

Let us start with a very small Pod manifest:

apiVersion: v1
kind: Pod
metadata:
  name: demo-pod
  labels:
    app: demo
spec:
  containers:
    - name: web
      image: nginx:1.27
      ports:
        - containerPort: 80
      resources:
        requests:
          cpu: "100m"
          memory: "128Mi"
        limits:
          cpu: "250m"
          memory: "256Mi"

When you apply this file, the request goes to the Kubernetes API server. That is the front door of the cluster. Nothing important happens without passing through it first.

The API server does more than nod politely and stamp the form. It checks authentication and authorization, validates the object schema, and sends the request through admission control. Admission controllers can modify or reject the request based on policies, quotas, defaults, or security rules. Only when that process is complete does the API server persist the desired state in etcd, the key-value store Kubernetes uses as its source of truth.

At that point, the Pod officially exists as an object in the cluster.

That does not mean it is running.

It means Kubernetes has written down your intentions in a very serious ledger and is now obliged to make reality catch up.

The scheduler looks for a home

Once the Pod exists but has no node assigned, the scheduler takes interest. Its job is not to run the Pod. Its job is to decide where the Pod should run.

This is less mystical than it sounds and more like trying to seat one extra party in a crowded restaurant without blocking the fire exit.

The scheduler first filters out nodes that cannot host the Pod. A node may be ruled out because it lacks CPU or memory, does not match nodeSelector labels, has taints the Pod does not tolerate, violates affinity or anti-affinity rules, or fails other placement constraints.

From the nodes that survive this round of rejection, the scheduler scores the viable candidates and picks one. Different scoring plugins influence the choice, including resource balance and topology preferences. Kubernetes is not asking, “Which node feels lucky today?” It is performing a structured selection process, even if the result arrives so quickly that it looks like instinct.

When the decision is made, the scheduler updates the Pod object with the chosen node.

That is all.

It does not pull images, start containers, mount storage, or wave a wand. It points at a node and says, in effect, “This one. Good luck to everyone involved.”

The kubelet picks up the job

Each node runs an agent called the kubelet. The kubelet watches the API server and notices when a Pod has been assigned to its node.

This is where the abstract promise turns into physical work.

The kubelet reads the Pod specification and starts coordinating with the local container runtime, such as ‘containerd’, to make the Pod real. If there are volumes to mount, secrets to project, environment variables to inject, or images to fetch, the kubelet is the one making sure those steps happen in the correct order.

The kubelet is not glamorous. It is the floor manager. It does not write the policies, it does not choose the table, and it does not get invited to keynote conferences. It simply has to make the plan work on an actual machine with actual limits. That makes it one of the most important components in the whole affair.

The sandbox appears before the containers do

Before your application container starts, Kubernetes prepares a Pod sandbox.

This is one of those wonderfully unglamorous details that turns out to matter a great deal. A Pod is not just “a container.” It is a small execution environment that may contain one or more containers sharing networking and, often, storage.

To build that environment, several things need to happen.

First, the container runtime may need to pull the image from a registry if it is not already cached on the node. This step alone can keep a Pod waiting for longer than people expect, especially when the image is huge, the registry is slow, or somebody has built an image as if hard disk space were a personal insult.

Second, networking must be prepared. Kubernetes relies on a CNI plugin to create the Pod’s network namespace and assign an IP address. All containers in the same Pod share that network namespace, which is why they can communicate over ‘localhost’. This is convenient and occasionally dangerous, much like sharing a flat with someone who assumes every shelf in the fridge belongs to them.

Third, volumes are mounted. If the Pod references ‘emptyDir’, ‘configMap’, ‘secret’, or persistent volumes, those mounts have to be prepared before the containers can use them.

There is also a small infrastructure container, commonly called the ‘pause’ container, whose job is to hold the Pod’s shared namespaces in place. It is not famous, but it is essential. The ‘pause’ container is a bit like the quiet relative at a family gathering who does no storytelling, makes no dramatic entrance, and is nevertheless the reason the chairs are still standing.

Only after this setup is complete can the application containers begin.

Watching the lifecycle from the outside

You can observe part of this process with a few simple commands:

kubectl apply -f pod.yaml
kubectl get pod demo-pod -w
kubectl describe pod demo-pod

The watch output often gives the first visible clue that the cluster is busy doing considerably more than the neatness of YAML would suggest.

A Pod typically moves through a small set of phases:

‘Pending’ means the Pod has been accepted but is still waiting for scheduling, image pulls, volume setup, or other preparation.
‘Running’ means the Pod has been bound to a node and at least one container is running or starting.
‘Succeeded’ means all containers completed successfully and will not be restarted.
‘Failed’ means all containers finished, but at least one exited with an error.
‘Unknown’ means the control plane cannot reliably determine the Pod state, usually because communication with the node has gone sideways.

These phases are useful, but they do not tell the whole story. One of the more common sources of confusion is ‘CrashLoopBackOff’. That is not a Pod phase. It is a container state pattern shown in ‘kubectl get pods’ output when a container keeps crashing, and Kubernetes backs off before trying again.

This matters because people often stare at ‘Running’ and assume everything is fine. Kubernetes, meanwhile, is quietly muttering, “Technically yes, but only in the way a car is technically functional while smoke comes out of the bonnet.”

Running is not the same as ready

Another detail worth understanding is that a Pod can be running without being ready to receive traffic.

This distinction matters in real systems because applications often need a few moments to warm up, load configuration, establish database connections, or otherwise stop acting like startled wildlife.

A readiness probe tells Kubernetes when the container is actually prepared to serve requests. Until that probe succeeds, the Pod should not be considered a healthy backend for a Service.

Here is a minimal example:

readinessProbe:
  httpGet:
    path: /
    port: 80
  initialDelaySeconds: 5
  periodSeconds: 10

With this in place, the container may be running, but Kubernetes will wait before routing traffic to it. This is one of those details that prevents very expensive forms of optimism.

Deletion is a polite process until it is not

Now, let us look at the other end of the Pod’s life.

When you run the following command, the Pod does not vanish in a puff of administrative smoke:

kubectl delete pod demo-pod

Instead, the API server marks the Pod for deletion and sets a grace period. The Pod enters a terminating state. The kubelet on the node sees that instruction and begins shutdown.

The normal sequence looks like this:

Kubernetes may first stop sending new traffic to the Pod if it is behind a Service and no longer considered ready.
A ‘preStop’ hook runs if one has been defined.
The kubelet asks the runtime to send ‘SIGTERM’ to the container’s main process.
Kubernetes waits for the grace period, which is ‘30’ seconds by default and controlled by ‘terminationGracePeriodSeconds’.
If the process still refuses to exit, Kubernetes sends ‘SIGKILL’ and ends the discussion.

That grace period exists for good reasons. Applications may need time to flush logs, finish requests, close connections, write buffers, or otherwise clean up after themselves. Production systems tend to appreciate this courtesy.

Here is a small example of a graceful shutdown configuration:

terminationGracePeriodSeconds: 30
containers:
  - name: web
    image: nginx:1.27
    lifecycle:
      preStop:
        exec:
          command: ["/bin/sh", "-c", "sleep 5"]

Once the containers stop, Kubernetes cleans up the sandbox, releases network resources, unmounts volumes as needed, and frees the node’s CPU and memory.

If the Pod was managed by a Deployment, a replacement Pod will usually be created to maintain the desired replica count. This is an important point. In Kubernetes, individual Pods are disposable. The desired state is what matters. Pods come and go. The controller remains stubborn.

Why this matters in the real world

Understanding this lifecycle is not trivia for people who enjoy suffering through conference diagrams. It is practical.

If a Pod is stuck in ‘Pending’, you need to know whether the issue is scheduling, image pulling, volume attachment, or policy rejection.

If a container is ‘CrashLoopBackOff’, you need to know that the Pod object exists, has probably been scheduled, and that the failure is happening later in the chain.

If traffic is not reaching the application, you need to remember that ‘Running’ and ‘Ready’ are not the same thing.

If shutdowns are ugly, logs are truncated, or users get errors during rollout, you need to inspect readiness probes, ‘preStop’ hooks, and grace periods rather than blaming Kubernetes in the abstract, which it will survive, but your incident report may not.

This is also where commands like these become genuinely useful:

kubectl get pod demo-pod -o wide
kubectl describe pod demo-pod
kubectl logs demo-pod
kubectl get events --sort-by=.metadata.creationTimestamp

Those commands let you inspect node placement, container events, log output, and recent cluster activity. Most Kubernetes troubleshooting starts by figuring out which stage of the Pod lifecycle has gone wrong, then narrowing the problem from there.

The quiet machinery behind a simple command

The next time you type ‘kubectl apply -f pod.yaml’, it is worth remembering that you are not merely starting a container. You are triggering a chain of decisions and side effects across the control plane and a worker node.

The API server validates and records the request. The scheduler finds a suitable home. The kubelet coordinates the local work. The runtime pulls images and starts containers. The CNI plugin wires up networking. Volumes are mounted. Probes decide whether the Pod is truly ready. And when the time comes, Kubernetes tears the whole thing down with the brisk professionalism of hotel staff clearing a room before the next guest arrives.

Which is impressive, really.

Particularly when you consider that from your side of the terminal, it still looks as though you only asked for one modest little Pod.

March 10, 2026 by Fernando SRE DevOps stuff Kubernetes SRE stuff

Tracing the origin of S3 objects with Amazon Athena

A while back, I opened the console for one of my busier S3 buckets and there, nestling among hundreds of perfectly ordinary files, sat something called quarterly_report_20260305.pdf. It looked exactly like the mystery jar of jam you find at the back of the fridge after a family gathering: you know it wasn’t there yesterday, yet nobody owns up to putting it there. In shared buckets, this sort of thing happens all the time, and the console, bless it, only tells you the last modified date. It never whispers who the actual culprit was.

That is where Amazon Athena and S3 server access logs come in. With a few straightforward steps, you can turn those silent log files into a clear answer delivered in plain SQL. The whole process feels pleasantly like detective work you can finish while your tea is still hot, and the article you are reading now should take you no more than ten minutes from start to finish.

The tech stack

Amazon S3 is the sturdy cupboard that holds nearly everything these days, yet for all its virtues, it keeps the identity of each uploader politely hidden. To make that identity visible, you must first switch on server access logging. Once enabled, every request (upload, download, delete) is written as plain text and dropped into a bucket you choose. The logs arrive free of charge to enable, though you do pay the modest storage cost in the destination bucket, a small price for sanity.

Enter Amazon Athena, a serverless query service that lets you run ordinary SQL straight against those log files without moving a single byte. The first time I used it, I felt the same quiet thrill you get when you finally locate the right key in a drawer full of oddments: everything suddenly becomes possible with almost no effort.

Step 1. Ensure logging is enabled

Go to the Properties tab of your source bucket and turn on Server access logging. Point the logs at a separate bucket (I like to call mine something obvious like logs-companyname) and give them a clean prefix such as access-logs/.

A small practical note I learned after waiting in vain one afternoon: the logs can take up to a few hours to appear, rather like a post that has gone round the long way. They do not backfill old activity, so if you need history, you must wait until new events occur. Also, check the target bucket’s policy; a missing permission once left me staring at empty folders and muttering mild British curses at the screen.

Step 2. Create the Athena table with partitioning

Now tell Athena how to read the logs. The following DDL uses partition projection, a quietly brilliant feature that means you never have to run MSCK REPAIR TABLE again. It works out the date folders automatically.

Run this in the Athena query editor (I have changed the names and paths from my real ones so nothing sensitive slips through):

CREATE DATABASE IF NOT EXISTS s3_logs_database;

CREATE EXTERNAL TABLE IF NOT EXISTS access_logs_table (
    bucketowner string,
    bucket_name string,
    requestdatetime string,
    remoteip string,
    requester string,
    requestid string,
    operation string,
    key string,
    request_uri string,
    httpstatus string,
    errorcode string,
    bytessent bigint,
    objectsize bigint,
    totaltime string,
    turnaroundtime string,
    referrer string,
    useragent string,
    versionid string,
    hostid string,
    sigv string,
    ciphersuite string,
    authtype string,
    endpoint string,
    tlsversion string,
    accesspointarn string,
    aclrequired string
)
PARTITIONED BY (timestamp string)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe'
WITH SERDEPROPERTIES (
    'input.regex' = '([^ ]*) ([^ ]*) \\[(.*?)\\] ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) (\"[^\"]*\"|-) (-|[0-9]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) (\"[^\"]*\"|-) ([^ ]*)(?: ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*))?.*$'
)
LOCATION 's3://my-company-logs/access-logs/'
TBLPROPERTIES (
    'projection.enabled' = 'true',
    'projection.timestamp.type' = 'date',
    'projection.timestamp.range' = '2024/01/01,NOW',
    'projection.timestamp.format' = 'yyyy/MM/dd',
    'projection.timestamp.interval' = '1',
    'projection.timestamp.interval.unit' = 'DAYS',
    'storage.location.template' = 's3://my-company-logs/access-logs/${timestamp}/'
);

Replace the LOCATION and storage.location.template with your own bucket and prefix. The regex looks like the sort of thing a medieval monk would doodle in the margin, but it is the official AWS pattern and works reliably.

Step 3. Uncover the upload details

With the table ready, finding who dropped that quarterly report is almost childishly simple. Here is the query I use (again with changed details):

SELECT 
    requestdatetime AS upload_time,
    requester,
    remoteip,
    operation,
    key AS file_path
FROM access_logs_table
WHERE timestamp = '2026/03/05'
  AND key LIKE '%quarterly_report_20260305%'
  AND operation LIKE '%PUT%'
LIMIT 10;

The requester column is the star of the show: it shows the IAM user ARN or role that performed the action. Requestdatetime gives the exact second in UTC. Operation tells you it was indeed an upload. I once traced a suspicious file only to discover it was my own weekend script behaving like an overenthusiastic puppy that had wandered off and come back with a stick.

If the requester says Anonymous, you know the upload came from public access, a gentle reminder to tighten the bucket policy before the next family gathering in the cloud.

Cost saving tips with partitions

Athena charges five dollars per terabyte scanned. Without the timestamp filter, you can accidentally scan months of logs while hunting for one file, an expense roughly equivalent to buying an entire chocolate cake when you only wanted one slice. With partitioning, the query reads only the folder for that single day, and the bill usually stays under the price of a decent biscuit.

I speak from experience: one forgetful broad query once produced a polite note from AWS that made my eyebrows rise. Since then, I filter on timestamp first and sleep better at night.

Wrapping up

What began as a mild annoyance with a stray file has turned into a small daily pleasure. A few lines of SQL, a properly partitioned table, and the cloud cupboard becomes pleasantly transparent. The same technique helps with compliance checks, pipeline debugging, and the quiet satisfaction of knowing exactly what is going on in your storage.

If you have a few minutes this afternoon, turn on logging for a test bucket and give the table a try. You may find, as I did, that the small effort pays back in clarity and calm far beyond the few pennies it costs. And the next time something mysterious appears in your S3 cupboard, you will know exactly who left it there and when.

March 5, 2026 by Fernando SRE Cloud stuff

Why generic auto scaling is terrible for healthcare pipelines

Let us talk about healthcare data pipelines. Running high volume payer processing pipelines is a lot like hosting a mandatory potluck dinner for a group of deeply eccentric people with severe and conflicting dietary restrictions. Each payer behaves with maddening uniqueness. One payer bursts through the door, demanding an entire roasted pig, which they intend to consume in three minutes flat. This requires massive, short-lived computational horsepower. Another payer arrives with a single boiled pea and proceeds to chew it methodically for the next five hours, requiring a small but agonizingly persistent trickle of processing power.

On top of this culinary nightmare, there are strict rules of etiquette. You absolutely must digest the member data before you even look at the claims data. Eligibility files must be validated before anyone is allowed to touch the dessert tray of downstream jobs. The workload is not just heavy. It is incredibly uneven and delightfully complicated.

Buying folding chairs for a banquet

On paper, Amazon Web Services managed Auto Scaling Mechanisms should fix this problem. They are designed to look at a growing pile of work and automatically hire more help. But applying generic auto scaling to healthcare pipelines is like a restaurant manager seeing a line out the door and solving the problem by buying fifty identical plastic folding chairs.

The manager does not care that one guest needs a high chair and another requires a reinforced steel bench. Auto scaling reacts to the generic brute force of the system load. It cannot look at a specific payer and tailor the compute shape to fit their weird eating habits. It cannot enforce the strict social hierarchy of job priorities. It scales the infrastructure, but it completely fails to scale the intention.

This is why we abandoned the generic approach and built our own dynamic EC2 provisioning system. Instead of maintaining a herd of generic servers waiting around for something to do, we create bespoke servers on demand based on a central configuration table.

The ruthless nightclub bouncer of job scheduling

Let us look at how this actually works regarding prioritization. Our system relies on that central configuration table to dictate order. Think of this table as the guest list at an obnoxiously exclusive nightclub. Our scheduler acts as the ruthless bouncer.

When jobs arrive at the queue, the bouncer checks the list. Member data? Right this way to the VIP lounge, sir. Claims data? Stand on the curb behind the velvet rope until the members are comfortably seated. Generic auto scaling has no native concept of this social hierarchy. It just sees a mob outside the club and opens the front doors wide. Our dynamic approach gives us perfect, tyrannical control over who gets processed first, ensuring our pipelines execute in a beautifully deterministic way. We spin up exactly the compute we specify, exactly when we want it.

Leaving your car running in the garage

Then there is the financial absurdity of warm pools. Standard auto scaling often relies on keeping a baseline of idle instances warm and ready, just in case a payer decides to drop a massive batch of files at two in the morning.

Keeping idle servers running is the technological equivalent of leaving your car engine idling in the closed garage all night just in case you get a sudden craving for a carton of milk at dawn. It is expensive, it is wasteful, and it makes you look a bit foolish when the AWS bill arrives.

Our dynamic system operates with a baseline of zero. We experience one hundred percent burst efficiency because we only pay for the exact compute we use, precisely when we use it. Cost savings happen naturally when you refuse to pay for things that are sitting around doing nothing.

A delightfully brutal server lifecycle

The operational model we ended up with is almost comically simple compared to traditional methods. A generic scaling group requires complex scaling policies, tricky cooldown periods, and endless tweaking of CloudWatch alarms. It is like managing a highly sensitive, moody teenager.

Our dynamic EC2 model is wonderfully ruthless. We create the instance and inject it with a single, highly specific purpose via a startup script. The instance wakes up, processes the healthcare data with absolute precision, and then politely self destructs so it stops billing us. They are the mayflies of the cloud computing world. They live just long enough to do their job, and then they vanish. There are no orphaned instances wandering the cloud.

This dynamic provisioning model has fundamentally altered how we digest payer workloads. We have somehow achieved a weird but perfect holy grail of cloud architecture. We get the granular flexibility of serverless functions, the raw, unadulterated horsepower of dedicated EC2 instances, and the stingy cost efficiency of a pure event-driven design.

If your processing jobs vary wildly from payer to payer, and if you care deeply about enforcing priorities without burning money on idle metal, building a disposable compute army might be exactly what your architecture is missing. We said goodbye to our idle servers, and honestly, we do not miss them at all.

February 28, 2026 by Fernando SRE Cloud stuff DevOps stuff SRE stuff

The lazy cloud architect guide to AWS automation

The shortcuts I use on every project now, after learning that scale mostly changes the bill, not the mistakes.

Let me tell you how this started. I used to measure my productivity by how many AWS services I could haphazardly stitch together in a single afternoon. Big mistake.

One night, I was deploying what should have been a boring, routine feature. Nothing fancy. Just basic plumbing. Six hours later, I was still babysitting the deployment, clicking through the AWS console like a caffeinated lab rat, re-running scripts, and manually patching up tiny human errors.

That is when the epiphany hit me like a rogue server rack. I was not slow because AWS is a labyrinth of complexity. I was slow because I was doing things manually that AWS already knows how to do in its sleep.

The patterns below did not come from sanitized tutorials. They were forged in the fires of shipping systems under immense pressure and desperately wanting my weekends back.

Event-driven everything and absolutely no polling

If you are polling, you are essentially paying Jeff Bezos for the privilege of wasting your own time. Polling is the digital equivalent of sitting in the backseat of a car and constantly asking, “Are we there yet?” every five seconds.

AWS is an event machine. Treat it like one. Instead of writing cron jobs that anxiously ask the database if something changed, just let AWS tap you on the shoulder when it actually happens.

Where this shines:

File uploads
Database updates
Infrastructure state changes
Cross-account automation

Example of reacting to an S3 upload instantly:

def lambda_handler(event, context):
    for record in event['Records']:
        bucket_name = record['s3']['bucket']['name']
        object_key = record['s3']['object']['key']

        # Stop asking if the file is there. AWS just handed it to you.
        trigger_completely_automated_workflow(bucket_name, object_key)

No loops. No waiting. Just action.

Pro tip: Event-driven systems fail less frequently simply because they do less work. They are the lazy geniuses of the cloud world.

Immutable deployments or nothing

SSH is not a deployment strategy. It is a desperate cry for help.

If your deployment plan involves SSH, SCP, or uttering the cursed phrase “just this one quick change in production”, you do not have a system. You have a fragile ecosystem built on hope and duct tape. I stopped “fixing” servers years ago. Now, I just murder them and replace them with fresh clones.

The pattern is brutally simple:

Build once
Deploy new
Destroy old

Example of launching a new EC2 version programmatically:

import boto3

ec2_client = boto3.client('ec2', region_name='eu-west-1')
response = ec2_client.run_instances(
    ImageId='ami-0123456789abcdef0', # Totally fake AMI
    InstanceType='t3a.nano',
    MinCount=1,
    MaxCount=1,
    TagSpecifications=[{
        'ResourceType': 'instance',
        'Tags': [{'Key': 'Purpose', 'Value': 'EphemeralClone'}]
    }]
)

It is like doing open-heart surgery. Instead of trying to fix the heart while the patient is running a marathon, just build a new patient with a healthy heart and disintegrate the old one. When something breaks, I do not debug the server. I debug the build process. That is where the real parasites live.

Infrastructure as code for the forgettable things

Most teams only use IaC for the big, glamorous stuff. VPCs. Kubernetes clusters. Massive databases.

This is completely backwards. It is like wearing a bespoke tuxedo but forgetting your underwear. The small, forgettable resources are the ones that will inevitably bite you when you least expect it.

What I automate with religious fervor:

IAM roles
Alarms
Schedules
Policies
Log retention

Example of creating a CloudWatch alarm in code:

cloudwatch.put_metric_alarm(
    AlarmName="QueueIsExploding",
    MetricName="ApproximateNumberOfMessagesVisible",
    Namespace="AWS/SQS",
    Threshold=10000,
    ComparisonOperator="GreaterThanThreshold",
    EvaluationPeriods=1,
    Period=300,
    Statistic="Sum"
)

If it matters in production, it lives in code. No exceptions.

Let Step Functions own the flow

Early in my career, I crammed all my business logic into Lambdas. Retries, branching, timeouts, bizarre edge cases. I treated them like a digital junk drawer.

I do not do that anymore. Lambdas should be as dumb and fast as a golden retriever chasing a tennis ball.

The new rule: One Lambda equals one job. If you need a workflow, use Step Functions. They are the micromanaging middle managers your architecture desperately needs.

Example of a simple workflow state:

{
  "Type": "Task",
  "Resource": "arn:aws:lambda:eu-west-1:123456789012:function:DoOneThingWell",
  "Retry": [
    {
      "ErrorEquals": ["States.TaskFailed"],
      "IntervalSeconds": 3,
      "MaxAttempts": 2
    }
  ],
  "Next": "CelebrateSuccess"
}

This separation makes debugging highly visual, makes retries explicit, and makes onboarding the new guy infinitely less painful. Your future self will thank you.

Kill cron jobs and use managed schedulers

Cron jobs are perfectly fine until they suddenly are not.

They are the ghosts of your infrastructure. They are completely invisible until they fail, and when they do fail, they die in absolute silence like a ninja with a sudden heart condition. AWS gives you managed scheduling. Just use it.

Why this is fundamentally faster:

Central visibility
Built-in retries
IAM-native permissions

Example of creating a scheduled rule:

eventbridge.put_rule(
    Name="TriggerNightlyChaos",
    ScheduleExpression="cron(0 2 * * ? *)",
    State="ENABLED",
    Description="Wakes up the system when nobody is looking"
)

Automation should be highly observable. Cron jobs are just waiting in the dark to ruin your Tuesday.

Bake cost controls into automation

Speed without cost awareness is just a highly efficient way to bankrupt your employer. The fastest teams I have ever worked with were not just shipping fast. They were failing cheaply.

What I automate now with the ruthlessness of a debt collector:

Budget alerts
Resource TTLs
Auto-shutdowns for non-production environments

Example of tagging resources with an expiration date:

ec2.create_tags(
    Resources=['i-0deadbeef12345678'],
    Tags=[
        {"Key": "TerminateAfter", "Value": "2026-12-31"},
        {"Key": "Owner", "Value": "TheVoid"}
    ]
)

Leaving resources without an owner or an expiration date is like leaving the stove on, except this stove bills you by the millisecond. Anything without a TTL is just technical debt waiting to invoice you.

A quote I live by: “Automation does not cut costs by magic. It cuts costs by quietly preventing the expensive little mistakes humans call normal.”

The death of the cloud hero

These patterns did not make me faster because they are particularly clever. They made me faster because they completely eliminated the need to make decisions.

Less clicking. Less remembering. Absolutely zero heroics.

If you want to move ten times faster on AWS, stop asking what to build next. Once automation is in charge, real speed usually arrives as work you no longer have to remember.

February 24, 2026 by Fernando SRE Cloud stuff SRE stuff

The profitable art of being difficult to replace

I once held the charmingly idiotic belief that net worth was directly correlated to calorie expenditure. As a younger man staring up at the financial stratosphere where the ultra-high earners floated, I assumed their lives were a relentless marathon of physiological exertion. I pictured CEOs and Senior Architects sweating through their Italian suits, solving quadratic equations while running on treadmills, their cortisol levels permanently redlining as they suffered for every single cent.

It was a comforting delusion because it implied the universe was a meritocracy based on thermodynamics. It suggested that if I just gritted my teeth hard enough and pushed until my vision blurred, the universe would eventually hand me a corner office and a watch that cost more than my first car.

Then I entered the actual workforce and realized that the universe is not fair. Worse than that, it is not even logical. The market does not care about your lactic acid buildup. In fact, there seems to be an inverse relationship between how much your back hurts at the end of the day and how many zeros are on your paycheck.

The thermodynamic lie of manual labor

Consider the holiday season retail worker. If you have ever worked in a shop during December, you know it is less of a job and more of a biological stress test designed by a sadist. You are on your feet for eight hours. You are smiling at people who are actively trying to return a toaster they clearly dropped in a bathtub. You are lifting boxes, dodging frantic shoppers, and absorbing the collective anxiety of a population that forgot to buy gifts until Christmas Eve.

It is physically draining, emotionally taxing, and mentally numbing. By any objective measure of human suffering, it is “hard work.”

And yet the compensation for this marathon of patience is often a number that barely covers the cost of the therapeutic insoles you need to survive the shift. If hard work were the currency of wealth, the person stacking shelves at 2 AM would be buying the yacht. Instead, they are usually the ones waiting for the night bus while the mall owner sleeps soundly in a bed that probably costs more than the worker’s annual rent.

This is the brutal reality of the labor market. We are not paid for the calories we burn. We are not paid for the “effort” in the strict physics sense of work equals force times distance. We are paid based on a much colder, less human metric. We are paid based on how annoying it would be to find someone else to do it.

The lucrative business of sitting very still

Let us look at my current reality as a DevOps engineer and Cloud Architect. My daily caloric burn is roughly equivalent to a hibernating sloth. While a construction worker is dissolving their kneecaps on concrete, I am sitting in an ergonomic chair designed by NASA, getting irrationally upset because my coffee is slightly below optimal temperature.

To an outside observer, my job looks like a scam. I type a few lines of YAML. I stare at a progress bar. I frown at a dashboard. Occasionally, I sigh dramatically to signal to my colleagues that I am doing something very complex with Kubernetes.

And yet the market values this sedentary behavior at a premium. Why?

It is certainly not because typing is difficult. Most people can type. It is not because I am working “harder” than the retail employee. I am definitely not. The reason is fear. Specifically, the fear of what happens when the progress bar turns red.

We are not paid for the typing. We are paid because we are the only ones willing to perform open-heart surgery on a zombie platform while the CEO watches. The ability to stare into the abyss of a crashing production database without vomiting is a rare and expensive evolutionary trait.

Companies do not pay us for the hours when everything is working. They pay us a retainer fee for the fifteen minutes a year when the entire digital infrastructure threatens to evaporate. We are basically insurance policies that drink too much caffeine.

The panic tax

This brings us to the core of the salary misunderstanding. Most technical professionals think they are paid to build things. This is only partially true. We are largely paid to absorb panic.

When a server farm goes dark, the average business manager experiences a visceral fight-or-flight response. They see revenue dropping to zero. They see lawsuits. They see their bonus fluttering away like a moth. The person who can walk into that room, look at the chaos, and say “I know which wire to wiggle” is not charging for the wire-wiggling. They are charging a “Panic Tax.”

The harder the problem is to understand, and the fewer people there are who can stomach the risk of solving it, the higher the tax you can levy.

If your job can be explained to a five-year-old in a single sentence, you are likely underpaid. If your job involves acronyms that sound like a robotic sneeze and requires you to understand why a specific version of a library hates a specific version of an operating system, you are in the money.

You are being paid for the obscurity of your suffering, not the intensity of it.

The golden retriever replacement theory

To understand your true value, you have to look at yourself with the cold, unfeeling eyes of a hiring manager. You have to ask yourself how easy it would be to replace you.

If you are a generalist who works very hard, follows all the rules, and does exactly what is asked, you are a wonderful employee. You are also doomed. To the algorithm of capitalism, a generalist worker is essentially a standard spare part. If you vanish, the organization simply scoops another warm body from the LinkedIn gene pool and plugs it into the socket before the seat gets cold.

However, consider the engineer who manages the legacy authentication system. You know the one. The system was written ten years ago by a guy named Dave who didn’t believe in documentation and is now living in a yurt in Montana. The code is a terrifying plate of spaghetti that somehow processes payments.

The engineer who knows how to keep Dave’s ghost alive is not working “hard.” They might spend four hours a day reading Reddit. But if they leave, the company stops making money. That engineer is difficult to replace.

This is the goal. You do not want to be the shiny new cog that fits perfectly in the machine. You want to be the weird, knobby, custom-forged piece of metal that holds the entire transmission together. You want to be the structural integrity of the department.

This does not mean you should hoard knowledge or refuse to document your work. That makes you a villain, not an asset. It means you should tackle the problems that are so messy, so risky, and so complex that other people are afraid to touch them.

The art of being a delightful bottleneck

There is a nuance here that is often missed. Being difficult to replace does not mean being difficult to work with. There is a specific type of IT professional who tries to create job security by being the “Guru on the Mountain.” They are grumpy, they refuse to explain anything, and they treat every question as a personal insult.

Do not be that person. Companies will tolerate that person for a while, but they will actively plot to replace them. It is a resentment-based retention strategy.

The profitable approach is to be the “Delightful Bottleneck.” You are the only one who can solve the problem, but you are also happy to help. You become the wizard who saves the day, not the troll under the bridge who demands a toll.

When you position yourself as the only person who can navigate the complexity of the cloud architecture, and you do it with a smile, you create a dependency that feels like a partnership. Management stops looking for your replacement and starts looking for ways to keep you happy. That is when the salary negotiations stop being a battle and start being a formality.

Navigating the scarcity market

If you want to increase your salary, stop trying to increase your effort. You cannot physically work harder than a script. You cannot out-process a serverless function. You will lose that battle every time because biology is inefficient.

Instead, focus on lowering your replaceability.

Niche down until it hurts. Find a corner of the cloud ecosystem that makes other developers wince. Learn the tools that are high in demand but low in experts because the documentation is written in riddles. It is not about working harder. It is about positioning yourself in the market where the supply line is thin and the desperation is high.

Look for the “unsexy” problems. Everyone wants to work on the new AI features. It is shiny. It is fun. It is great for dinner party conversation. But because everyone wants to do it, the supply of labor is high.

Fewer people want to work on compliance automation, security governance, or mainframe migration. These tasks are the digital equivalent of plumbing. They are not glamorous. They involve dealing with sludge. But when the toilet backs up, the plumber can charge whatever they want because nobody else wants to touch it.

Final thoughts on leverage

We often confuse motion with progress. We confuse exhaustion with value. We have been trained since school to believe that the student who studies the longest gets the best grade.

The market does not care about your exhaustion. It cares about your leverage.

Leverage comes from specific knowledge. It comes from owning a problem set that scares other people. It comes from being the person who can walk into a room where everyone is panicking and lower the collective blood pressure by simply existing.

Do not grind yourself into dust trying to be the hardest worker in the room. Be the most difficult one to replace. It pays better, and your lower back will thank you for it.

February 17, 2026 by Fernando SRE Computer Science stuff DevOps stuff