DevOps stuff

Random comments from a DevOps Engineer

Hybrid Cloud vs Multicloud which strategy is right for you

Cloud computing has been a game-changer, enabling businesses to scale, innovate, and deliver services at a pace once thought impossible. Most companies begin their journey with a single public cloud provider, which serves them well initially. But as a business grows and its needs become more complex, that single-cloud environment often starts to feel restrictive. The one-size-fits-all solution no longer fits.

This is the point at which organizations reach a critical crossroads. The path forward splits, leading toward two powerful strategies that promise greater flexibility, resilience, and freedom: Hybrid Cloud and Multicloud. Let’s unpack these two popular approaches to help you decide which journey is right for you.

When one Cloud is no longer enough

Before diving into definitions, it’s important to understand why businesses are looking beyond a single provider. This isn’t a trend driven by technology for technology’s sake; it’s a strategic evolution fueled by practical business needs.

The core drivers are often a desire for more control over sensitive data, the need to avoid being locked into a single vendor’s ecosystem, and the goal of building a more resilient infrastructure that can withstand outages. As your organization’s digital footprint expands, relying on one provider can feel like putting all your eggs in one basket, a risky proposition in today’s fast-paced digital economy.

Understanding your two main options

Once you’ve decided to expand your cloud strategy, you’ll encounter two primary models. While they sound similar, they solve different problems.

A Hybrid Cloud approach is like having a custom-built workshop at home for your most specialized, delicate work, while also renting a massive, fully-equipped industrial space for heavy-duty production. It’s a mixed computing environment that combines a private cloud (usually on-premises infrastructure you own and manage) with at least one public cloud (like AWS, Azure, or Google Cloud). The two environments are designed to work together, connected by technology that allows data and applications to be shared between them.

A Multicloud strategy, on the other hand, is like deciding to source ingredients for a gourmet meal from different specialty stores. You buy your bread from the best artisan bakery, your cheese from a dedicated fromagerie, and your vegetables from the local farmer’s market. This approach involves using services from multiple public cloud providers at the same time. The key difference is that these cloud environments don’t necessarily need to be integrated. You simply pick and choose the best service from each provider for a specific task.

The hybrid approach is a blend of control and scale

Opting for a hybrid model gives an organization a unique balance of ownership and outsourced power. It’s a popular choice for good reason, offering several distinct advantages.

Flexibility in workload placement

Hybrid setups allow you to run applications and store data in the most suitable location. For example, you can keep your highly sensitive customer database on your private, on-premises servers to meet strict compliance rules, while running your customer-facing web application in the public cloud to handle unpredictable traffic spikes. This ability to “burst” workloads into the public cloud during peak demand is a classic and powerful use case.

Regulatory compliance and security

For industries like finance, healthcare, and government, data sovereignty and privacy regulations (like GDPR or HIPAA) are non-negotiable. A hybrid cloud allows you to keep your most sensitive data within your own four walls, giving you complete control and making it easier to pass security audits. It’s the digital equivalent of keeping your most important documents in a personal safe rather than a rented storage unit.

Enhanced resilience

A well-designed hybrid model offers a robust disaster recovery solution. If your local infrastructure experiences an issue, you can failover critical operations to your public cloud provider, ensuring business continuity with minimal disruption.

However, this approach isn’t without its challenges. Managing and securing two distinct environments requires a more complex operational model and a skilled IT team. Building the “bridge” between the private and public clouds requires careful planning and the right tools to ensure seamless and secure communication.

The multicloud path to freedom and specialization

A multicloud strategy is fundamentally about choice and avoiding dependency. It’s for organizations that want to leverage the unique strengths of different providers without being tied to a single one.

Avoiding vendor lock-in

Dependency on a single provider can be risky. Prices can rise, service quality can decline, or the vendor’s strategic direction might no longer align with yours. Multicloud mitigates this risk. It’s like diversifying your financial investments instead of putting all your money into one stock. This freedom gives you negotiating power and the agility to adapt to market changes.

Access to best-of-breed services

Each cloud provider excels in different areas. AWS is renowned for its mature and extensive set of services, Google Cloud is a leader in data analytics and machine learning, and Azure offers seamless integration with Microsoft’s enterprise software ecosystem. A multicloud strategy allows you to use Google’s AI tools for one project, Azure’s Active Directory for identity management, and AWS’s S3 for robust storage, all at the same time.

Improved global scalability

For businesses with a global user base, multicloud enables you to choose providers that have a strong presence in specific geographic regions. This can reduce latency and improve performance for your customers, while also helping you comply with local data residency laws.

The primary challenge of multicloud is managing the complexity. Each cloud has its own set of APIs, management tools, and security models. Without a unified management platform, your teams could find themselves juggling multiple control panels, leading to operational inefficiencies and potential security gaps. Cost management can also become tricky, requiring careful monitoring to avoid budget overruns.

How to chart your Cloud course

So, how do you decide which path to take? The right choice depends entirely on your organization’s specific circumstances. There is no single “best” answer. Ask yourself these key questions:

What are our business and regulatory needs? Do you handle data that is subject to strict residency or compliance laws? If so, a hybrid approach might be necessary to keep that data on-premises.
How do our legacy systems fit in? If you have significant investments in on-premises hardware or critical legacy applications that are difficult to move, a hybrid strategy can provide a bridge to the cloud without requiring a complete overhaul.
What is our team’s technical maturity? Is your team ready to handle the operational complexity of managing multiple cloud environments? A multicloud strategy requires a higher level of technical expertise and often relies on automation tools like Terraform or orchestration platforms like Kubernetes to be successful.

The road ahead

The lines between hybrid and multicloud are blurring. The future will see these strategies intersect even more with emerging technologies like AI-driven automation, which will simplify management, and edge computing, which will bring processing power even closer to where data is generated.

Ultimately, navigating your cloud journey isn’t about picking a predefined label. It’s about thoughtfully designing a strategy that aligns perfectly with your organization’s unique goals. By clearly understanding the strengths and challenges of each approach, you can build a cloud infrastructure that is strategic, efficient, and ready for the future.

July 15, 2025 by Fernando SRE Cloud stuff DevOps stuff

How Headless services and StatefulSets work together in Kubernetes

Kubernetes is an open-source platform designed to seamlessly manage containerized applications. Imagine the manager at your favorite café coordinating baristas, chefs, and servers effortlessly, ensuring a smooth customer experience every single time. Kubernetes automates deployments, scaling, and operations, making it indispensable for today’s complex digital landscape.

Understanding headless services

At first glance, Headless Services might seem unusual, yet they’re essential Kubernetes components. Regular Kubernetes Services act as receptionists routing your calls; Headless Services, however, skip the receptionist altogether and connect you directly to individual pods via their unique IP addresses.

Consider them as a neighborhood directory listing direct phone numbers, eliminating the central switchboard. This direct approach is particularly beneficial when individual pod identity and communication are critical, such as with database clusters.

Example YAML for a headless service:

apiVersion: v1
kind: Service
metadata:
  name: my-headless-service
spec:
  clusterIP: None
  selector:
    app: my-app
  ports:
  - port: 80

Demystifying StatefulSets

StatefulSets uniquely manage stateful applications by assigning each pod a stable identity and persistent storage. Imagine a classroom where each student (pod) has an assigned desk (storage) that remains consistent, no matter how often they come and go.

Comparing StatefulSets and deployments

Deployments are ideal for stateless applications, where each instance is interchangeable and can be replaced without affecting the overall system. StatefulSets, however, excel with stateful applications, ensuring pods have stable identities and persistent storage, perfect for databases and message queues.

Example YAML for a StatefulSet:

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: my-stateful-app
spec:
  serviceName: my-headless-service
  replicas: 3
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: app-container
        image: my-app-image
        ports:
        - containerPort: 80
        volumeMounts:
        - name: my-volume
          mountPath: /data
  volumeClaimTemplates:
  - metadata:
      name: my-volume
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 1Gi

The strength of pairing headless services with StatefulSets

Headless Services and StatefulSets each have significant strengths independently, but they truly shine when combined. Headless Services provide stable network identities for StatefulSet pods, akin to each member of a specialized team having their direct communication line for efficient collaboration.

Picture an emergency medical team; direct lines enable doctors and nurses to coordinate rapidly and precisely during critical situations. Similarly, distributed databases such as Cassandra or MongoDB rely heavily on this direct communication model to maintain data consistency and reliability.

Practical use-case

Consider a Cassandra database running on Kubernetes. StatefulSets ensure each Cassandra node has dedicated data storage and a unique identity. With Headless Services, these nodes communicate directly, consistently synchronizing data and ensuring seamless accessibility, irrespective of which node handles the incoming requests.

Concluding insights

Headless Services combined with StatefulSets form a powerful solution for managing stateful applications within Kubernetes. They address distinct challenges in state management and network stability, ensuring reliability and scalability for your applications.

Leveraging these Kubernetes capabilities equips your infrastructure for success, akin to empowering each team member with the necessary tools for clear communication and consistent performance. Embrace this dynamic duo for a more robust and efficient Kubernetes environment.

An irreverent tour of Linux disk space and RAM mysteries

Linux feels a lot like living in a loft apartment: the pipes are on display, every clank echoes, and when something leaks, you’re the first to squelch through the puddle. This guide hands you a mop, half a dozen snappy commands that expose where your disk space and memory have wandered off to, plus a couple of click‑friendly detours. Expect prose that winks, occasionally rolls its eyes, and never ever sounds like tax law.

Why checking disk and memory matters

Think of storage and RAM as the pantry and fridge in a shared flat. Ignore them for a week, and you end up with three half‑finished jars of salsa (log files) and leftovers from roommates long gone (orphaned kernels). A five‑minute audit every Friday spares you the frantic sprint for extra space, or worse, the freeze just before a production deploy.

Disk panic survival kit

Get the big picture fast

df is the bird’s‑eye drone shot of your mounted filesystems, everything lines up like contestants at a weigh‑in.

# Exclude temporary filesystems for clarity
$ df -hT -x tmpfs -x devtmpfs

-h prints friendly sizes, -T shows filesystem type, and the two -x flags hide the short‑lived stuff.

Zoom in on space hogs

du is your tape measure. Pair it with a little sort and head for instant gossip about the top offenders in any directory:

# Top 10 fattest directories under /var
$ sudo du -h --max-depth=1 /var 2>/dev/null | sort -hr | head -n 10

If /var/log looks like it skipped leg day and went straight for bulking season, you’ve found the culprit.

Bring in the interactive detective

When scrolling text gets dull, ncdu adds caffeine and colour:

# Install on most Debian‑based distros
$ sudo apt install ncdu

# Start at root (may take a minute)
$ sudo ncdu /

Navigate with the arrow keys, press d to delete, and feel the instant gratification of reclaiming gigabytes, the Marie Kondo of storage.

Visualise block devices

# Tree view of drives, partitions, and mount points
$ lsblk -o NAME,SIZE,FSTYPE,MOUNTPOINT --tree

Handy when that phantom 8 GB USB stick from last week still lurks in /media like an uninvited houseguest.

Memory and swap reality check

Check the ledger

The free command is a quick wallet peek, straightforward, and slightly judgemental:

$ free -h

Focus on the available column; that’s what you can still spend without the kernel reaching for its credit card (a.k.a. swap).

Real‑Time spy cam

# Refresh every two seconds, ordered by RAM gluttons
$ top -o %MEM

Prefer your monitoring colourful and charming? Try htop:

$ sudo apt install htop
$ htop

Use F6 to sort by RES (resident memory) and watch your browser tabs duke it out for supremacy.

Meet RAM’s couch‑surfing cousin

Swap steps in when RAM is full, think of it as sleeping on the living‑room sofa: doable, but slow and slightly undignified.

# Show active swap files or partitions
$ swapon --show

Seeing swap above 20 % during regular use? Either add RAM or conjure an emergency swap file:

$ sudo fallocate -l 2G /swapfile
$ sudo chmod 600 /swapfile
$ sudo mkswap /swapfile
$ sudo swapon /swapfile

Remember to append it to /etc/fstab so it survives a reboot.

Prefer clicking to typing

Yes, there’s a GUI for that. GNOME Disks and KSysGuard both display live graphs and won’t judge your typos. On Ubuntu, you can run:

$ sudo apt install gnome-disk-utility

Launch it from the menu and watch I/O spikes climb like toddlers on a sugar rush.

Quick reference cheat sheet

Show all mounts minus temp stuff
Command: df -hT -x tmpfs -x devtmpfs
Memory aid: df = disk fly‑over
Top ten heaviest directories
Command: du -h –max-depth=1 /path | sort -hr | head
Memory aid: du = directory weight
Interactive cleanup
Command: ncdu /
Memory aid: ncdu = du after espresso
Live RAM counter
Command: free -h
Memory aid: free = funds left
Spot memory‑hogging apps
Command: top -o %MEM
Memory aid: top = talent show
Swap usage
Command: swapon –show
Memory aid: swap on stage

Stick this list on your clipboard; your future self will thank you.

Wrapping up without a bow

You now own the detective kit for disk and memory mysteries, no cosmic metaphors, just straight talk with a wink. Run df -hT right now; if the numbers give you heartburn, take three deep breaths and start paging through ncdu. Storage leaks and RAM gluttons are inevitable, but letting them linger is optional.

Found an even better one‑liner? Drop it in the comments and make the rest of us look lazy. Until then, happy sleuthing, and may your logs stay trim and your swap forever bored.

July 4, 2025 by Fernando SRE DevOps stuff Linux Stuff SRE stuff

Edge computing reshapes DevOps for the real-time era

A new frontier at your doorstep

When Amazon started placing delivery lockers in neighborhoods, packages arrived faster and more reliably. Edge computing follows a similar logic, bringing computational power closer to the user. Instead of sending data halfway around the world, edge computing processes it locally, dramatically reducing latency, enhancing privacy, and maintaining autonomy.

For DevOps teams, this shift isn’t trivial. Like switching from central mail hubs to neighborhood lockers, it demands new strategies and skills.

CI/CD faces a new reality

Classic cloud pipelines are centralized, much like a single distribution center. Edge computing flips that model upside-down, scattering deployments across numerous tiny locations. Deploying updates to thousands of edge devices isn’t the same as updating a handful of cloud servers.

DevOps teams now battle version drift, a scenario similar to managing software on thousands of smartphones with different versions. The solutions? Smaller, incremental updates and lightweight build artifacts, ensuring that pushing changes doesn’t overwhelm limited network bandwidth or hardware resources.

Designing for when things go dark

Planning a family dinner knowing there’s a possibility of a power outage means stocking up on candles and sandwiches. Similarly, edge devices must be designed for disconnection, ensuring operations continue uninterrupted during network downtime.

Offline-first architectures become critical here. Techniques like local queuing and eventual data reconciliation help edge applications function seamlessly, even if connectivity is lost for hours or days. Managing schema migrations carefully is crucial; it’s akin to updating recipes without knowing if family members received the memo.

Keeping data consistently in sync

Imagine organizing a city-wide neighborhood watch: push notifications ensure quick alerts, while pull mechanisms periodically fetch updates. Edge deployments use similar synchronization tactics.

Techniques such as Conflict-Free Replicated Data Types (CRDTs) help manage data consistency, even when devices are offline or slow to respond. DevOps engineers also need to factor in bandwidth budgeting, using intelligent compression and prioritizing data to ensure crucial information reaches its destination promptly.

Observability without seeing everything

Monitoring edge deployments is like managing a fleet of food trucks spread across the city. You can’t constantly keep an eye on every truck. Instead, you rely on periodic check-ins and key signals.

Telemetry sampling, data aggregation at the edge, and effective back-pressure management prevent network floods. Selecting a few meaningful metrics, like checking a truck’s gas gauge rather than tracking every sandwich sold, helps quickly pinpoint issues without drowning in data.

Incident response across the edge

Responding to issues at thousands of remote locations is challenging, like troubleshooting vending machines scattered nationwide without direct access.

Edge incident response leverages runbook templates, policy-as-code, and remote diagnostics tools. Because traditional SSH access isn’t always viable, tactics like automated self-healing and structured escalation paths blending central SRE teams with local staff become indispensable.

Bridging cloud and edge

Integrating IoT devices into your infrastructure is similar to securely registering visitors at a large event, you need clear identification, managed credentials, and accurate headcounts.

Edge computing uses secure onboarding, rotating credentials, and message brokers that maintain state coherence across the network. Digital twins represent device states virtually, helping maintain consistent and accurate information between edge and cloud environments. Cost-effective strategies determine whether workloads run locally or in centralized clouds.

Preparing for what’s next

Edge computing evolves rapidly, with emerging standards like WebAssembly (WASM) running applications directly at the edge, and maturing tools like OpenTelemetry simplifying observability.

DevOps teams should embrace these changes early. Developing skills in hardware awareness and basic radio frequency (RF) knowledge becomes increasingly valuable. Experimenting now, rigorously measuring results, and sharing insights ensures teams stay ahead.

Innovate and adapt for the road ahead

Edge computing is reshaping DevOps in real-time. Thriving in this era requires adapting practices, tooling, and mindset. Bring your computational lockers closer to home, plan proactively for network disruptions, streamline synchronization, enhance remote observability, and respond intelligently to incidents.

By preparing today, your DevOps team can confidently navigate tomorrow’s distributed landscape. Embracing edge computing means more than just keeping pace with technology; it positions your team to deliver faster, more reliable services, capitalize on emerging business opportunities, and maintain a competitive advantage. Investing now in the right tools, processes, and skills not only safeguards against future challenges but also unlocks potential for innovation, growth, and sustained success in a rapidly evolving technological world.

In short, the future belongs to those who embrace change and adapt quickly; let your team be among them.

July 3, 2025 by Fernando SRE Cloud stuff DevOps stuff SRE stuff

Free that stuck Linux port and get on with your day

A rogue process squatting on port 8080 is the tech-equivalent of leaving your front-door key in the lock: nothing else gets in or out, and the neighbours start gossiping. Ports are exclusive party venues; one process per port, no exceptions. When an app crashes, restarts awkwardly, or you simply forget it’s still running, it grips that port like a toddler with the last cookie, triggering the dreaded “address already in use” error and freezing your deployment plans.

Below is a brisk, slightly irreverent field guide to evicting those squatters, gracefully when possible, forcefully when they ignore polite knocks, and automatically so you can get on with more interesting problems.

When ports act like gate crashers

Ports are finite. Your Linux box has 65535 of them, but every service worth its salt wants one of the “good seats” (80, 443, 5432…). Let a single zombie process linger, and you’ll be running deployment whack-a-mole all afternoon. Keeping ports free is therefore less superstition and more basic hygiene, like throwing out last night’s takeaway before the office starts to smell.

Spot the culprit

Before brandishing a digital axe, find out who is hogging the socket.

lsof, the bouncer with the clipboard

sudo lsof -Pn -iTCP:8080 -sTCP:LISTEN

lsof prints the PID, the user, and even whether our offender is IPv4 or IPv6. It’s as chatty as the security guard who tells you exactly which cousin tried to crash the wedding.

ss, the Formula 1 mechanic

Modern kernels prefer ss, it’s faster and less creaky than netstat.

sudo ss -lptn sport = :8080

fuser, the debt collector

When subtlety fails, fuser spells out which processes own the file or socket:

sudo fuser -v 8080/tcp

It displays the PID and the user, handy for blaming Dave from QA by name.

Tip: Add the -k flag to fuser to terminate offenders in one swoop, great for scripts, dangerous for fingers-on-keyboard humans.

Gentle persuasion first

A well-behaved process will exit graciously if you offer it a polite SIGTERM (15):

kill -15 3245     # give the app a chance to clean up

Think of it as tapping someone’s shoulder at closing time: “Finish your drink, mate.”

If it doesn’t listen, escalate to SIGINT (2), the Ctrl-C of signals, or SIGHUP (1) to make daemons reload configs without dying.

Bring out the big stick

Sometimes you need the digital equivalent of cutting the mains power. SIGKILL (9) is that guillotine:

kill -9 3245      # immediate, unsentimental termination

No cleanup, no goodbye note, just a corpse on the floor. Databases hate this, log files dislike it, and system-wide supervisors may auto-restart the process, so use sparingly.

One-liners for the impatient

sudo kill -9 $(sudo ss -lptn sport = :8080 | awk 'NR==2{split($NF,a,"pid=");split(a[2],b,",");print b[1]}')

Single line, single breath, done. It’s the Fast & Furious of port freeing, but remember: copy-paste speed correlates strongly with “oops-I-just-killed-production”.

Automate the cleanup

A pocket Bash script

#!/usr/bin/env bash
port=${1:-3000}
pid=$(ss -lptn "sport = :$port" | awk 'NR==2 {split($NF,a,"pid="); split(a[2],b,","); print b[1]}')

if [[ -n $pid ]]; then
  echo "Port $port is busy (PID $pid). Sending SIGTERM."
  kill -15 "$pid"
  sleep 2
  kill -0 "$pid" 2>/dev/null && echo "Still alive; escalating..." && kill -9 "$pid"
else
  echo "Port $port is already free."
fi

Drop it in ~/bin/freeport, mark executable, and call freeport 8080 before every dev run. Fewer keystrokes, fewer swearwords.

systemd, your tireless janitor

Create a watchdog service so the OS restarts your app only when it exits cleanly, not when you manually murder it:

[Unit]
Description=Watchdog for MyApp on 8080

[Service]
ExecStart=/usr/local/bin/myapp
Restart=on-failure
RestartPreventExitStatus=64   # don’t restart if we SIGKILLed

Enable with systemctl enable myapp.service, grab coffee, forget ports ever mattered.

Ansible for the herd

- name: Free port 8080 across dev boxes
  hosts: dev
  become: true
  tasks:
    - name: Terminate offender on 8080
      shell: |
        pid=$(ss -lptn 'sport = :8080' | awk 'NR==2{split($NF,a,"pid=");split(a[2],b,",");print b[1]}')
        [ -n "$pid" ] && kill -15 "$pid" || echo "Nothing to kill"

Run it before each CI deploy; your colleagues will assume you possess sorcery.

A few cautionary tales

Containers restart themselves. Kill a process inside Docker, and the orchestrator may spin it right back up. Either stop the container or adjust restart policies.
Dependency dominoes. Shooting a backend API can topple every microservice that chats to it. Check systemctl status or your Kubernetes liveness probes before opening fire .
Sudo isn’t seasoning. Use it only when the victim process belongs to another user. Over-salting scripts with sudo causes security heartburn.

Wrap-up

Freeing a port isn’t arcane black magic; it’s janitorial work that keeps your development velocity brisk and your ops team sane. Identify the squatter, ask it nicely to leave, evict it if it refuses, and automate the routine so you rarely have to think about it again. Got a port-conflict horror story involving 3 a.m. pager alerts and too much caffeine? Tell me in the comments, schadenfreude is a powerful teacher.

Now shut that laptop lid and actually get on with your day. The ports are free, and so are you.

June 28, 2025 by Fernando SRE DevOps stuff Linux Stuff SRE stuff

Why Kubernetes Ingress feels outdated and Gateway API is stepping up

Kubernetes has transformed container orchestration, rapidly pushing the boundaries of scalability and flexibility. Yet some core components haven’t evolved as gracefully. Kubernetes Ingress is a prime example; it’s beginning to feel like using an old flip phone when everyone else has moved on to smartphones.

What’s driving this shift away from the once-reliable Ingress, and why are more Kubernetes professionals turning to Gateway API?

The rise and limits of Kubernetes Ingress

When Kubernetes introduced Ingress, its appeal lay in its simplicity. Its job was straightforward: route HTTP and HTTPS traffic into Kubernetes clusters predictably. Like traffic lights at a busy intersection, it provided clear and reliable outcomes: set paths and hostnames, and your Ingress controller (NGINX, Traefik, or others) took care of the rest.

However, as Kubernetes workloads grew more complex, this simplicity became restrictive. Teams began seeking advanced capabilities such as canary deployments, complex traffic management, support for additional protocols, and finer control. Unfortunately, Ingress remained static, forcing teams to rely on cumbersome vendor-specific customizations.

Why Ingress now feels outdated

Ingress still performs adequately, but managing it becomes increasingly cumbersome as complexity rises. It’s comparable to owning a reliable but outdated vehicle; it gets you to your destination but lacks modern conveniences. Here’s why Ingress feels out-of-date:

Limited protocol support – Only HTTP and HTTPS are supported natively. If your applications require gRPC, TCP, or UDP, you’re out of luck.
Vendor lock-in with annotations – Advanced routing features and authentication mechanisms often require vendor-specific annotations, locking you into particular solutions.
Rigid permission models – Managing shared control across multiple teams is complicated and inefficient, similar to having a single TV remote for an entire household.
No evolutionary path – Ingress will remain stable but static, unable to evolve as the Kubernetes ecosystem demands greater flexibility.

Gateway API offers a modern alternative

Gateway API isn’t merely an upgraded Ingress; it’s a fundamental rethink of how Kubernetes handles external traffic. It cleanly separates roles and responsibilities, streamlining interactions between network administrators, platform teams, and developers. Think of it as a well-run restaurant: chefs, managers, and servers each have clear roles, ensuring smooth and efficient operation.

Additionally, Gateway API supports multiple protocols, including gRPC, TCP, and UDP, natively. This eliminates reliance on awkward annotations and vendor lock-in, resembling an upgrade from single-purpose appliances to versatile multi-function tools that adapt smoothly to emerging needs.

When Gateway API becomes essential

Gateway API won’t suit every situation, but specific scenarios benefit from its use. Consider these questions:

Do your applications require sophisticated traffic handling, like canary deployments or traffic mirroring?
Are your services utilizing protocols beyond HTTP and HTTPS?
Is your Kubernetes cluster shared among multiple teams, each needing distinct control?
Do you seek portability across cloud providers and wish to avoid vendor lock-in?
Do you often desire modern features that are unavailable through traditional Ingress?

Answering “yes” to any of these indicates that Gateway API isn’t just helpful, it’s essential.

Deciding to move forward

Ingress isn’t entirely obsolete. For straightforward HTTP/HTTPS routing for smaller services, it remains effective. But as soon as your needs scale up, involve complex traffic management, or require clear team boundaries, Gateway API becomes the superior choice.

Technology continuously advances, and your infrastructure must evolve with it. Gateway API isn’t a futuristic solution; it’s already here, enhancing your Kubernetes deployments with greater intelligence, flexibility, and manageability.

When better tools appear, upgrading isn’t merely sensible, it’s crucial. Gateway API represents precisely this meaningful advancement, ensuring your Kubernetes environment remains robust, adaptable, and ready for whatever comes next.

June 26, 2025 by Fernando SRE Cloud stuff DevOps stuff Kubernetes SRE stuff

Achieving perfect elasticity in Kubernetes with multidimensional autoscaling

Running a Kubernetes environment can feel like a high-stakes game of guesswork. We estimate our application’s needs, define our resource requests, and hope we’ve struck the right balance. Too generous, and we’re paying for cloud resources that sit idle. Too conservative, and we risk sluggish performance or critical outages when real-world demand spikes. It’s a constant, stressful effort to manually tune a system that is inherently dynamic.

There is, however, a more elegant path. It involves moving away from this static guesswork and towards building a truly adaptive infrastructure. This is not about simply adding more tools; it’s about creating a self-regulating system that breathes with the rhythm of your workload. This is the core promise of a well-orchestrated Kubernetes autoscaling strategy. Let’s explore how to build it, piece by piece.

The three pillars of autoscaling

To build our adaptive system, we need to understand its three fundamental components. Think of them as the different ways a professional restaurant kitchen responds to a dinner rush.

The Horizontal Pod Autoscaler HPA

When a flood of orders hits the kitchen, the head chef doesn’t ask each cook to work twice as fast. The first, most logical step is to bring more cooks to the line. This is precisely what the Horizontal Pod Autoscaler does. It acts as the kitchen’s manager, watching the incoming demand (typically CPU or memory usage). As orders pile up, it adds more identical pod replicas, more “cooks”, to handle the load. When the rush subsides, it sends some cooks home, ensuring you’re only paying for the staff you need. It’s the frontline response to fluctuating demand.

The Vertical Pod Autoscaler VPA

Now, consider a specialized station, like the grill. What if the single grill cook is overwhelmed, not by the number of orders, but because their workspace is too small and inefficient? Simply adding another grill cook might just create more chaos in a cramped space. The better solution is to give the specialist a bigger, better grill station. This is the domain of the Vertical Pod Autoscaler. The VPA doesn’t change the number of pods. Instead, it meticulously observes the real-world resource consumption of a single pod over time and adjusts its allocated CPU and memory, its “workspace”, to be the perfect size. It answers the question, “How much power does this one cook need to do their job perfectly?”

The Cluster Autoscaler CA

What happens if the kitchen runs out of physical space? You can’t add more cooks or bigger grills if there’s no room for them. This is where the Cluster Autoscaler comes in. It is the architect of the kitchen itself. The CA doesn’t pay attention to individual orders or cooks. Its sole focus is space. When it sees pods that can’t be scheduled because no node has enough capacity, our “cooks without a counter”, it expands the kitchen by adding new nodes to the cluster. Conversely, when it sees entire sections of the kitchen sitting empty for too long, it smartly downsizes the space to keep operational costs low.

From static blueprints to dynamic reality

When we first deploy an application on Kubernetes, we manually define its resources.requests, and resources.limits. This is like creating a static architectural blueprint for our kitchen. We draw the lines based on our best assumptions.

But a blueprint doesn’t capture the chaotic, dynamic flow of a real dinner service. An application’s actual needs are rarely static. This is where the VPA transforms our approach. It moves us from relying on a fixed blueprint to observing the kitchen’s real-time workflow. It provides the data-driven intelligence to continuously refine and optimize our initial design, shifting us from a world of reactive fixes to one of proactive optimization.

How a great platform elevates the craft

Anyone can assemble a kitchen, but the difference between a home setup and a Michelin-star facility lies in the integration, quality, and advanced tooling. In the Kubernetes world, this is the value a managed platform like Google Kubernetes Engine (GKE) provides.

While HPA, VPA, and CA are open-source concepts, managing them yourself is like building and maintaining that professional kitchen from scratch. GKE offers them as fully managed, seamlessly integrated services.

Effortless setup. Enabling these autoscalers in GKE is a simple, declarative action, removing significant operational overhead.
An expert consultant, the VPA’s “recommendation-only” mode is a game-changer. It’s like having a master chef observe your kitchen and leave detailed notes on how to improve efficiency, all without interrupting service. This free, built-in guidance is invaluable for right-sizing your workloads.

However, GKE’s most significant innovation is a technique that solves a classic Kubernetes puzzle: The Multidimensional Pod Autoscaler (MPA).

Historically, trying to use HPA (more cooks) and VPA (better workspaces) on the same workload was a recipe for conflict. The two would issue contradictory signals, leading to instability. GKE’s MPA acts as the master head chef, intelligently coordinating both actions. It allows you to scale horizontally and vertically at the same time, ensuring your kitchen can both add more cooks and give them better equipment in one fluid motion. This is the ultimate expression of elasticity.

A practical blueprint for your strategy

With this understanding, you can now design a robust autoscaling strategy:

For Your Stateless Dishes (e.g., web frontends, APIs)
Start with the HPA to handle variable traffic. As you mature, graduate to the MPA to achieve a superior level of efficiency by scaling in both dimensions.
For Your Stateful Specialties (e.g., databases, message queues)
Rely on the VPA to meticulously right-size these critical components, ensuring they always have the exact resources needed for stable and reliable performance.
For the Entire Kitchen
Let the Cluster Autoscaler work in the background as your ever-vigilant architect, always ensuring there is enough underlying infrastructure for your applications to thrive.

An autonomous future awaits

We started with a stressful guessing game and have arrived at the blueprint for an intelligent, self-regulating infrastructure. By thoughtfully combining HPA, VPA, and CA, we evolve from being reactive system administrators to proactive cloud architects.

This journey culminates with tools like GKE’s Multidimensional Pod Autoscaler. The MPA is more than just another feature; it represents a paradigm shift. It solves the fundamental conflict between scaling out and scaling up, allowing our applications to adapt with a new level of intelligence. With MPA, workloads can simultaneously handle sudden traffic surges by adding replicas, while continuously right-sizing the resource footprint of each instance. This dual-axis scaling eliminates the trade-offs we once had to make, unlocking a state of true, cost-effective elasticity.

The path to this autonomous state is an incremental one. The best first step is to harness the power of observation. Start today by enabling VPA in recommendation-only mode on a non-production workload. Listen to its insights, understand your application’s real needs, and use that data to transform your static blueprints. This is the foundational skill that will empower you to confidently adopt multidimensional scaling, creating a dynamic, living system ready to meet any challenge that comes its way.

June 21, 2025 by Fernando SRE Cloud stuff DevOps stuff Kubernetes

Linux commands for the pathologically curious

We all get comfortable. We settle into our favorite chair, our favorite IDE, and our little corner of the Linux command line. We master ls, grep, and cd, and we walk around with the quiet confidence of someone who knows their way around. But the terminal isn’t a neat, modern condo; it’s a sprawling, old mansion filled with secret passages, dusty attics, and bizarre little tools left behind by generations of developers.

Most people stick to the main hallways, completely unaware of the weird, wonderful, and handy commands hiding just behind the wallpaper. These aren’t your everyday tools. These are the secret agents, the oddballs, and the unsung heroes of your operating system. Let’s meet a few of them.

The textual anarchists

Some commands don’t just process text; they delight in mangling it in beautiful and chaotic ways.

First, meet rev, the command-line equivalent of a party trick that turns out to be surprisingly useful. It takes whatever you give it and spits it out backward.

echo "desserts" | rev

This, of course, returns stressed. Coincidence? The terminal thinks not. At first glance, you might dismiss it as a tool for a nerdy poetry slam. But the next time you’re faced with a bizarrely reversed data string from some ancient legacy system, you’ll be typing rev and looking like a wizard.

If rev is a neat trick, shuf is its chaotic cousin. This command takes the lines in your file and shuffles them into a completely random order.

# Create a file with a few choices
echo -e "Order Pizza\nDeploy to Production\nTake a Nap" > decisions.txt

# Let the terminal decide your fate
shuf -n 1 decisions.txt

Why would you want to do this? Maybe you need to randomize a playlist, test an algorithm, or run a lottery for who has to fix the next production bug. shuf is an agent of chaos, and sometimes, chaos is exactly what you need.

Then there’s tac, which is cat spelled backward for a very good reason. While the ever-reliable cat shows you a file from top to bottom, tac shows it to you from bottom to top. This might sound trivial, but anyone who has ever tried to read a massive log file will see the genius.

# Instantly see the last 5 errors in a huge log file
tac /var/log/syslog | grep -i "error" | head -n 5

This lets you get straight to the juicy, most recent details without an eternity of scrolling.

The obsessive organizers

After all that chaos, you might need a little order. The terminal has a few neat freaks ready to help.

The nl command is like cat’s older, more sophisticated cousin who insists on numbering everything. It adds formatted line numbers to a file, turning a simple text document into something that looks official.

# Add line numbers to a script
nl backup_script.sh

Now you can professionally refer to “the critical bug on line 73” during your next code review.

But for true organizational bliss, there’s column. This magnificent tool takes messy, delimited text and formats it into beautiful, perfectly aligned columns.

# Let's say you have a file 'users.csv' like this:
# Name,Role,Location
# Alice,Dev,Remote
# Bob,Sysadmin,Office

cat users.csv | column -t -s,

This command transforms your comma-vomit into a table fit for a king. It’s so satisfying it should be prescribed as a form of therapy.

The tireless workers

Next, we have the commands that just do their job, repeatedly and without complaint.

In the entire universe of Linux, there is no command more agreeable than yes. Its sole purpose in life is to output a string over and over until you tell it to stop.

# Automate the confirmation for a script that keeps asking
yes | sudo apt install my-awesome-package

This is the digital equivalent of nodding along until the installation is complete. It is the ultimate tool for the lazy, the efficient, and the slightly tyrannical system administrator.

If yes is the eternal optimist, watch is the eternal observer. This command executes another program periodically, showing its output in real time.

# Monitor the number of established network connections every 2 seconds
watch -n 2 "ss -t | grep ESTAB | wc -l"

It turns your terminal into a live dashboard. It’s the command-line equivalent of binge-watching your system’s health, and it’s just as addictive.

For an even nosier observer, try dstat. It’s the town gossip of your system, an all-in-one tool that reports on everything from CPU stats to disk I/O.

# Get a running commentary of your system's vitals
dstat -tcnmd

This gives you a timestamped report on cpu, network, disk, and memory usage. It’s like top and iostat had a baby and it came out with a Ph.D. in system performance.

The specialized professionals

Finally, we have the specialists, the commands built for one hyper-specific and crucial job.

The look command is a dictionary search on steroids. It performs a lightning-fast search on a sorted file and prints every line that starts with your string.

# Find all words in the dictionary starting with 'compu'
look compu /usr/share/dict/words

It’s the hyper-efficient librarian who finds “computer,” “computation,” and “compulsion” before you’ve even finished your thought.

For more complex relationships, comm acts as a file comparison counselor. It takes two sorted files and tells you which lines are unique to each and which they share.

# File 1: developers.txt (sorted)
# alice
# bob
# charlie

# File 2: admins.txt (sorted)
# alice
# david
# eve

# See who is just a dev, just an admin, or both
comm developers.txt admins.txt

Perfect for figuring out who has access to what, or who is on both teams and thus doing twice the work.

The desire to procrastinate productively is a noble one, and Linux is here to help. Meet at. This command lets you schedule a job to run once at a specific time.

# Schedule a server reboot for 3 AM tomorrow.
# After hitting enter, you type the command(s) and press Ctrl+D.
at 3:00am tomorrow
reboot
^D (Ctrl+D)

Now you can go to sleep and let your past self handle the dirty work. It’s time travel for the command line.

And for the true control freak, there’s chrt. This command manipulates the real-time scheduling priority of a process. In simple terms, you can tell the kernel that your program is a VIP.

# Run a high-priority data processing script
sudo chrt -f 99 ./process_critical_data.sh

This tells the kernel, “Out of the way, peasants! This script is more important than whatever else you were doing.” With great power comes great responsibility, so use it wisely.

Keep digging

So there you have it, a brief tour of the digital freak show lurking inside your Linux system. These commands are the strange souvenirs left behind by generations of programmers, each one a solution to a problem you probably never knew existed. Your terminal is a treasure chest, but it’s one where half the gold coins might just be cleverly painted bottle caps. Each of these tools walks the fine line between a stroke of genius and a cry for help. The fun part isn’t just memorizing them, but that sudden, glorious moment of realization when one of these oddballs becomes the only thing in the world that can save your day.

June 14, 2025 by Fernando SRE DevOps stuff Linux Stuff

The core AWS services for modern DevOps

In any professional kitchen, there’s a natural tension. The chefs are driven to create new, exciting dishes, pushing the boundaries of flavor and presentation. Meanwhile, the kitchen manager is focused on consistency, safety, and efficiency, ensuring every plate that leaves the kitchen meets a rigorous standard. When these two functions don’t communicate well, the result is chaos. When they work in harmony, it’s a Michelin-star operation.

This is the world of software development. Developers are the chefs, driven by innovation. Operations teams are the managers, responsible for stability. DevOps isn’t just a buzzword; it’s the master plan that turns a chaotic kitchen into a model of culinary excellence. And AWS provides the state-of-the-art appliances and workflows to make it happen.

The blueprint for flawless construction

Building infrastructure without a plan is like a construction crew building a house from memory. Every house will be slightly different, and tiny mistakes can lead to major structural problems down the line. Infrastructure as Code (IaC) is the practice of using detailed architectural blueprints for every project.

AWS CloudFormation is your master blueprint. Using a simple text file (in JSON or YAML format), you define every single resource your application needs, from servers and databases to networking rules. This blueprint can be versioned, shared, and reused, guaranteeing that you build an identical, error-free environment every single time. If something goes wrong, you can simply roll back to a previous version of the blueprint, a feat impossible in traditional construction.

To complement this, the Amazon Machine Image (AMI) acts as a prefabricated module. Instead of building a server from scratch every time, an AMI is a perfect snapshot of a fully configured server, including the operating system, software, and settings. It’s like having a factory that produces identical, ready-to-use rooms for your house, cutting setup time from hours to minutes.

The automated assembly line for your code

In the past, deploying software felt like a high-stakes, manual event, full of risk and stress. Today, with a continuous delivery pipeline, it should feel as routine and reliable as a modern car factory’s assembly line.

AWS CodePipeline is the director of this assembly line. It automates the entire release process, from the moment code is written to the moment it’s delivered to the user. It defines the stages of build, test, and deploy, ensuring the product moves smoothly from one station to the next.

Before the assembly starts, you need a secure warehouse for your parts and designs. AWS CodeCommit provides this, offering a private and secure Git repository to store your code. It’s the vault where your intellectual property is kept safe and versioned.

Finally, AWS CodeDeploy is the precision robotic arm at the end of the line. It takes the finished software and places it onto your servers with zero downtime. It can perform sophisticated release strategies like Blue-Green deployments. Imagine the factory rolling out a new car model onto the showroom floor right next to the old one. Customers can see it and test it, and once it’s approved, a switch is flipped, and the new model seamlessly takes the old one’s place. This eliminates the risk of a “big bang” release.

Self-managing environments that thrive

The best systems are the ones that manage themselves. You don’t want to constantly adjust the thermostat in your house; you want it to maintain the perfect temperature on its own. AWS offers powerful tools to create these self-regulating environments.

AWS Elastic Beanstalk is like a “smart home” system for your application. You simply provide your code, and Beanstalk handles everything else automatically: deploying the code, balancing the load, scaling resources up or down based on traffic, and monitoring health. It’s the easiest way to get an application running in a robust environment without worrying about the underlying infrastructure.

For those who need more control, AWS OpsWorks is a configuration management service that uses Chef and Puppet. Think of it as designing a custom smart home system from modular components. It gives you granular control to automate how you configure and operate your applications and infrastructure, layer by layer.

Gaining full visibility of your operations

Operating an application without monitoring is like trying to run a factory from a windowless room. You have no idea if the machines are running efficiently if a part is about to break, or if there’s a security breach in progress.

AWS CloudWatch is your central control room. It provides a wall of monitors displaying real-time data for every part of your system. You can track performance metrics, collect logs, and set alarms that notify you the instant a problem arises. More importantly, you can automate actions based on these alarms, such as launching new servers when traffic spikes.

Complementing this is AWS CloudTrail, which acts as the unchangeable security logbook for your entire AWS account. It records every single action taken by any user or service, who logged in, what they accessed, and when. For security audits, troubleshooting, or compliance, this log is your definitive source of truth.

The unbreakable rules of engagement

Speed and automation are worthless without strong security. In a large company, not everyone gets a key to every room. Access is granted based on roles and responsibilities.

AWS Identity and Access Management (IAM) is your sophisticated keycard system for the cloud. It allows you to create users and groups and assign them precise permissions. You can define exactly who can access which AWS services and what they are allowed to do. This principle of “least privilege”, granting only the permissions necessary to perform a task, is the foundation of a secure cloud environment.

A cohesive workflow not just a toolbox

Ultimately, a successful DevOps culture isn’t about having the best individual tools. It’s about how those tools integrate into a seamless, efficient workflow. A world-class kitchen isn’t great because it has a sharp knife and a hot oven; it’s great because of the system that connects the flow of ingredients to the final dish on the table.

By leveraging these essential AWS services, you move beyond a simple collection of tools and adopt a new operational philosophy. This is where DevOps transcends theory and becomes a tangible reality: a fully integrated, automated, and secure platform. This empowers teams to spend less time on manual configuration and more time on innovation, building a more resilient and responsive organization that can deliver better software, faster and more reliably than ever before.

June 11, 2025 by Fernando SRE Cloud stuff DevOps stuff SRE stuff

GKE key advantages over other Kubernetes platforms

Exploring the world of containerized applications reveals Kubernetes as the essential conductor for its intricate operations. It’s the common language everyone speaks, much like how standard shipping containers revolutionized global trade by fitting onto any ship or truck. Many cloud providers offer their own managed Kubernetes services, but Google Kubernetes Engine (GKE) often takes center stage. It’s not just another Kubernetes offering; its deep roots in Google Cloud, advanced automation, and unique optimizations make it a compelling choice.

Let’s see what sets GKE apart from alternatives like Amazon EKS, Microsoft AKS, and self-managed Kubernetes, and explore why it might be the most robust platform for your cloud-native ambitions.

Google’s inherent Kubernetes expertise

To truly understand GKE’s edge, we need to look at its origins. Google didn’t just adopt Kubernetes; they invented it, evolving it from their internal powerhouse, Borg. Think of it like learning a complex recipe. You could learn from a skilled chef who has mastered it, or you could learn from the very person who created the dish, understanding every nuance and ingredient choice. That’s GKE.

This “creator” status means:

Direct, Unfiltered Expertise: GKE benefits directly from the insights and ongoing contributions of the engineers who live and breathe Kubernetes.
Early Access to Innovation: GKE often supports the latest stable Kubernetes features before competitors can. It’s like getting the newest tools straight from the workshop.
Seamless Google Cloud Synergy: The integration with Google Cloud services like Cloud Logging, Cloud Monitoring, and Anthos is incredibly tight and natural, not an afterthought.

How Others Compare:

While Amazon EKS and Microsoft AKS are capable managed services, they don’t share this native lineage. Self-managed Kubernetes, whether on-premises or set up with tools like kops, places the full burden of upgrades, maintenance, and deep expertise squarely on your shoulders.

The simplicity of Autopilot fully managed Kubernetes

GKE offers a game-changing operational model called Autopilot, alongside its Standard mode (which is more akin to EKS/AKS where you manage node pools). Autopilot is like hiring an expert event planning team that also handles all the setup, catering, and cleanup for your party, leaving you to simply enjoy hosting. It offers a truly serverless Kubernetes experience.

Key benefits of Autopilot:

Zero Node Management: Google takes care of node provisioning, scaling, and all underlying infrastructure concerns. You focus on your applications, not the plumbing.
Optimized Cost Efficiency: You pay for the resources your pods actually consume, not for idle nodes. It’s like only paying for the electricity your appliances use, not a flat fee for being connected to the grid.
Built-in Enhanced Security: Security best practices are automatically applied and managed by Google, hardening your clusters by default.

How others compare:

EKS and AKS require you to actively manage and scale your node pools. Self-managed clusters demand significant, ongoing operational efforts to keep everything running smoothly and securely.

Unified multi-cluster and multi-cloud operations with Anthos

In an increasingly distributed world, managing applications across different environments can feel like juggling too many balls. GKE’s integration with Anthos, Google’s hybrid and multi-cloud platform, acts as a master control panel.

Anthos allows for:

Centralized command: Manage GKE clusters alongside those on other clouds like EKS and AKS, and even your on-premises deployments, all from a single viewpoint. It’s like having one universal remote for all your different entertainment systems.
Consistent policies everywhere: Apply uniform configurations and security policies across all your environments using Anthos Config Management, ensuring consistency no matter where your workloads run.
True workload portability: Design for flexibility and avoid vendor lock-in, moving applications where they make the most sense.

How Others Compare:

EKS and AKS generally lack such comprehensive, native multi-cloud management tools. Self-managed Kubernetes often requires integrating third-party solutions like Rancher to achieve similar multi-cluster oversight, adding complexity.

Sophisticated networking and security foundations

GKE comes packed with unique networking and security features that are deeply woven into the platform.

Networking highlights:

Global load balancing power: Native integration with Google’s global load balancer means faster, more scalable, and more resilient traffic management than many traditional setups.
Automated certificate management: Google-managed Certificate Authority simplifies securing your services.
Dataplane V2 advantage: This Cilium-based networking stack provides enhanced security, finer-grained policy enforcement, and better observability. Think of it as upgrading your building’s basic security camera system to one with AI-powered threat detection and detailed access logs.

Security fortifications:

Workload identity clarity: This is a more secure way to grant Kubernetes service accounts access to Google Cloud resources. Instead of managing static, exportable service account keys (like having physical keys that can be lost or copied), each workload gets a verifiable, short-lived identity, much like a temporary, auto-expiring digital pass.
Binary authorization assurance: Enforce policies that only allow trusted, signed container images to be deployed.
Shielded GKE nodes protection: These nodes benefit from secure boot, vTPM, and integrity monitoring, offering a hardened foundation for your workloads.

How Others Compare:

While EKS and AKS leverage AWS and Azure security tools respectively, achieving the same level of integration, Kubernetes-native security often requires more manual configuration and piecing together different services. Self-managed clusters place the entire burden of security hardening and ongoing vigilance on your team.

Smart cost efficiency and pricing structure

GKE’s pricing model is competitive, and Autopilot, in particular, can lead to significant savings.

No control plane fees for Autopilot: Unlike EKS, which charges an hourly fee per cluster control plane, GKE Autopilot clusters don’t have this charge. Standard GKE clusters have one free zonal cluster per billing account, with a small hourly fee for regional clusters or additional zonal ones.
Sustained use discounts: Automatic discounts are applied for workloads that run for extended periods.
Cost-Saving VM options: Support for Preemptible VMs and Spot VMs allows for substantial cost reductions for fault-tolerant or batch workloads.

How Others Compare:

EKS incurs control plane costs on top of node costs. AKS offers a free control plane but may not match GKE’s automation depth, potentially leading to other operational costs.

Optimized for AI ML and Big Data workloads

For teams working with Artificial Intelligence, Machine Learning, or Big Data, GKE offers a highly optimized environment.

Seamless GPU and TPU access: Effortless provisioning and utilization of GPUs and Google’s powerful TPUs.
Kubeflow integration: Streamlines the deployment and management of ML pipelines.
Strong BigQuery ML and Vertex AI synergy: Tight compatibility with Google’s leading data analytics and AI platforms.

How Others Compare:

EKS and AKS support GPUs, but native TPU integration is a unique Google Cloud advantage. Self-managed setups require manual configuration and integration of the entire ML stack.

Why GKE stands out

Choosing the right Kubernetes platform is crucial. While all managed services aim to simplify Kubernetes operations, GKE offers a unique blend of heritage, innovation, and deep integration.

GKE emerges as a firm contender if you prioritize:

A truly hands-off, serverless-like Kubernetes experience with Autopilot.
The benefits of Google’s foundational Kubernetes expertise and rapid feature adoption.
Seamless hybrid and multi-cloud capabilities through Anthos.
Advanced, built-in security and networking designed for modern applications.

If your workloads involve AI/ML, and big data analytics, or you’re deeply invested in the Google Cloud ecosystem, GKE provides an exceptionally integrated and powerful experience. It’s about choosing a platform that not only manages Kubernetes but elevates what you can achieve with it.

June 5, 2025 by Fernando SRE Cloud stuff DevOps stuff Kubernetes SRE stuff