Cloud stuff

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

The case of the missing EC2 public IP

It was a Tuesday. I did what I’ve done a thousand times: I logged into an Amazon EC2 instance using its public IP, a respectable 52.95.110.21. Routine stuff. Once inside, muscle memory took over, and I typed ip addr to see the network configuration.

And then I blinked.

inet 10.0.10.147/24.

I checked my terminal history. Yes, I had connected to 52.95.110.21. So, where did this 10.0.10.147 character come from? And more importantly, where on earth was the public IP I had just used? For a moment, I felt like a detective at a crime scene where the main evidence had vanished into thin air. I questioned my sanity, my career choice, and the very fabric of the TCP/IP stack.

Suppose you’ve ever felt this flicker of confusion, congratulations. You’ve just stumbled upon one of AWS’s most elegant sleights of hand. The truth is, the public IP doesn’t exist inside the instance. It’s a ghost. A label. A brilliant illusion.

Let’s put on our detective hats and solve this mystery.

Meet the real resident

Every EC2 instance, upon its creation, is handed a private IP address. Please think of this as its legal name, the one on its birth certificate. It’s the address it uses to talk to its neighbors within its cozy, gated community, the Virtual Private Cloud (VPC). This instance, 10.0.10.147, is perfectly happy using this address to get a cup of sugar from the database next door at 10.0.10.200. It’s private, it’s intimate, and frankly, it’s nobody else’s business.

The operating system itself, be it Ubuntu, Amazon Linux, or Windows, is blissfully unaware of any other identity. As far as it’s concerned, its name is 10.0.10.147, and that’s the end of the story. If you check its network configuration, that’s the only IP you’ll see.

# Check the network interface, see only the private IP
$ ip -4 addr show dev eth0 | grep inet
    inet 10.0.10.147/24 brd 10.0.10.255 scope global dynamic eth0

So, if the instance only knows its private name, how does it have a public life on the internet?

The master of disguise

The magic happens outside the instance, at the edge of your VPC. Think of the AWS Internet Gateway as a ridiculously efficient bouncer at an exclusive club. Your instance, wanting to go online, walks up to the bouncer and whispers, “Hey, I’m 10.0.10.147 And I need to fetch a cat picture from the internet.“

The bouncer nods, turns to the vast, chaotic world of the internet, and shouts, “HEY, TRAFFIC FOR 52.95.110.21, COME THIS WAY!”

This translation trick is called 1-to-1 Network Address Translation (NAT). The Internet Gateway maintains a secret map that links the public IP to the private IP. The public IP is just a mask, a public-facing persona. It never actually gets assigned to the instance’s network interface. It’s all handled by AWS behind the scenes, a performance so smooth you never even knew it was happening.

When your instance sends traffic out, the bouncer (Internet Gateway) swaps the private source IP for the public one. When return traffic comes back, the bouncer swaps it back. The instance is none the wiser. It lives its entire life thinking its name is 10.0.10.147, completely oblivious to its international fame as 52.95.110.21.

Interrogating the local gossip

So, the instance is clueless. But what if we, while inside, need to know its public identity? What if we need to confirm our instance’s public IP for a firewall rule somewhere else? We can’t just ask the operating system.

Fortunately, AWS provides a nosy neighbor for just this purpose: the Instance Metadata Service. This is a special, link-local address (169.254.169.254) that an instance can query to learn things about itself, things the operating system doesn’t know. It’s the local gossip line.

To get the public IP, you first have to get a temporary security token (because even gossip has standards these days), and then you can ask your question.

# First, grab a session token. This is good for 6 hours (21600 seconds).
$ TOKEN=$(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 21600")

# Now, use the token to ask for the public IP
$ curl -s -H "X-aws-ec2-metadata-token: $TOKEN" http://169.254.169.254/latest/meta-data/public-ipv4
52.95.110.21

And there it is. The ghost in the machine, confirmed.

A detective’s field guide

Now that you’re in on the secret, you can avoid getting duped. Here are a few field notes to keep in your detective’s journal.

The clueless security guard. If you’re configuring a firewall inside your instance (like iptables or UFW), remember that it only knows the instance by its private IP. Don’t try to create a rule for your public IP; the firewall will just stare at you blankly. Always use the private IP for internal security configurations.

The persistent twin: What if you need a public IP that doesn’t change every time you stop and start your instance? That’s what an Elastic IP (EIP) is for. But don’t be fooled. An EIP is just a permanent public IP that you own. Behind the scenes, it works the same way: it’s a persistent mask mapped via NAT to your instance’s private IP. The instance remains just as gloriously ignorant as before.

The vanishing act. If you’re not using an Elastic IP, your instance’s public IP is ephemeral. It’s more like a hotel room number than a home address. Stop the instance, and it gives up the IP. Start it again, and it gets a brand new one. This is a classic “gotcha” that has broken countless DNS records and firewall rules.

The case is closed

So, the next time you log into an EC2 instance and see a private IP, don’t panic. You haven’t SSH’d into your smart fridge by mistake. You’re simply seeing the instance for who it truly is. The public IP was never missing; it was just a clever disguise, an elegant illusion managed by AWS so your instance can have a public life without ever having to deal with the paparazzi. The heavy lifting is done for you, leaving your instance to worry about more important things. Like serving those cat pictures.

July 12, 2025 by Fernando SRE Cloud 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.

July 9, 2025 by Fernando SRE Cloud stuff DevOps stuff Kubernetes SRE stuff

Choosing your message queue AWS SQS or GCP Pub/Sub

In the world of modern software, applications are rarely monolithic islands. Instead, they are bustling cities of interconnected services, each performing a specific job. For this city to function smoothly, its inhabitants, microservices, functions, and components need a reliable way to communicate without being directly tethered to one another. This is where message brokers come in, acting as the city’s postal service, ensuring that messages are delivered efficiently and reliably.

Two of the most prominent cloud-based postal services are Amazon Web Services’ Simple Queue Service (SQS) and Google Cloud’s Pub/Sub. Both are exceptional at what they do, but they operate on different philosophies. Understanding their unique characteristics is crucial for any cloud architect or DevOps engineer aiming to build robust, scalable, and event-driven systems. This guide will explore their differences to help you choose the right service for your application’s needs.

A quick look at our contenders

Before we examine the details, let’s get a general feel for each service.

AWS SQS is the seasoned veteran of message queuing. Think of it as a highly organized system of mailboxes. A service writes a letter (a message) and places it into a specific mailbox (a queue). The recipient service then comes to that mailbox and picks up its mail when it has the capacity to process it. It’s a straightforward, incredibly reliable system that has been battle-tested for years.

GCP Pub/Sub operates more like a global newspaper subscription. A publisher (your service) doesn’t send a message to a specific recipient. Instead, it publishes a message to a “topic,” like a news flash for the “user-signup” channel. Any service that has subscribed to that topic instantly receives a copy of the message. It’s designed for broad, real-time distribution of information on a global scale.

The delivery dilemma Push versus Pull

The most fundamental difference between SQS and Pub/Sub lies in how messages are delivered. This is often referred to as the “push vs. pull” model.

The pull model, which is SQS’s native approach, is like checking your P.O. box. The consumer application is responsible for periodically asking the queue, “Is there any mail for me?” This gives the consumer complete control over the rate of consumption. If it’s overwhelmed with work, it can slow down its requests or stop asking for new messages altogether. This is ideal for batch processing or any workload where you need to manage the processing pace carefully.

The push model, where Pub/Sub shines, is akin to home mail delivery. When a message is published, Pub/Sub actively “pushes” it to all subscribed endpoints, such as a serverless function or a webhook. The recipient doesn’t have to ask; the message just arrives. This is incredibly efficient for real-time notifications and event-driven workflows where immediate reaction is key. While Pub/Sub also supports a pull model, its architecture is optimized for push-based delivery.

Comparing key features

Let’s break down how these two services stack up in a few critical areas.

Message ordering

Sometimes, the sequence of events is just as important as the events themselves. For these cases, AWS SQS offers a specific FIFO (First-In, First-Out) queue type. This works exactly like a single-file line at a bank; the first person to get in line is the first one to be served. It provides a strict guarantee that messages will be processed in the exact order they were sent, which is critical for tasks like processing financial transactions or application logs.

GCP Pub/Sub, in contrast, does not have a dedicated FIFO queue type. Instead, it achieves partial ordering through the use of ordering keys. You can assign a key to messages (for example, a userId), and Pub/Sub will ensure that all messages with that specific key are delivered in order. However, it doesn’t guarantee order between different keys. To reuse the analogy, it’s less like a single line and more like a deli with separate ticket numbers for the butcher and the bakery. It keeps orders straight within each department, but not across the entire store.

Scale and reach

This is where their architectural differences become clear. SQS is a regional service. It’s incredibly scalable and resilient, but its scope is confined to a single AWS region.

Pub/Sub is inherently global. You publish a message once, and it can be delivered to subscribers in any region around the world with low latency. If your application has a global user base and you need to propagate events worldwide, Pub/Sub has a distinct advantage.

Message size and retention

Think of SQS as being for postcards and letters. It supports messages up to 256 KB. It can hold onto these messages for up to 14 days, giving your consumers plenty of time to process them.

Pub/Sub, on the other hand, can handle larger packages, with a maximum message size of 10 MB. However, its standard retention period is shorter, at 7 days.

Special delivery options

SQS has a native feature called Delay Queues. This allows you to postpone the delivery of a new message for up to 15 minutes. It’s like writing a post-dated check; the message sits in the queue but is invisible to consumers until the timer expires. This is useful for scheduling tasks without a complex scheduling service. Pub/Sub does not offer a similar built-in feature.

When to choose AWS SQS

SQS is your go-to choice when you need a dependable, orderly mailroom for your application. It excels in scenarios where:

Strict ordering is non-negotiable. For task sequencing or financial ledgers, SQS FIFO is the gold standard.
You need to control the pace of consumption. The pull model is perfect for decoupling a fast producer from a slower consumer or for batch processing jobs.
Task scheduling is required. The native delay queue feature is a simple yet powerful tool.
Your application’s architecture is primarily contained within a single AWS region.

When to choose GCP Pub/Sub

Pub/Sub is the right tool when you’re building a global broadcasting system or a highly reactive, event-driven platform. Consider it when:

You need to fan-out messages to many consumers. Pub/Sub’s topic-and-subscription model is designed for this.
Global distribution with low latency is a priority. Its global nature is a massive benefit for distributed systems.
You are sending large messages. The 10 MB limit offers much more flexibility than SQS.
A push-based model fits your architecture. It integrates seamlessly with serverless functions for instant, event-triggered execution.

A final word

So, after all this technical deliberation, which digital courier should you entrust with your precious data packets? The one that meticulously forms a single, orderly queue, or the one that shouts your message through a global megaphone to anyone who will listen?

The truth is, there’s no single “best” service. There’s only the one whose particular brand of crazy best matches your application’s personality. Is your app a stickler for the rules, demanding every event be processed in perfect sequence, lest it have a digital panic attack? Then the quiet, predictable, and slightly obsessive SQS is your soulmate. Or is your app more of a drama queen, needing to announce every minor update to the entire world, immediately? Then the boisterous, globe-trotting Pub/Sub is probably already sliding into your DMs.

Ultimately, the best way to choose is to put them to the test. Think of it as a job interview. Give them both a trial run with your actual workload and see which one handles the pressure with more grace, or at least breaks in a more interesting, less catastrophic way. Go on, do it for science. And for the future sanity of your on-call engineer.

July 6, 2025 by Fernando SRE Cloud 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

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

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

The strange world of serverless data processing made simple

Data isn’t just “big” anymore. It’s feral. It stampedes in from every direction, websites, mobile apps, a million sentient toasters, and it rarely arrives neatly packaged. It’s messy, chaotic, and stubbornly resistant to being neatly organized into rows for analysis. For years, taming this digital beast meant building vast, complicated corrals of servers, clusters, and configurations. It was a full-time job to keep the lights on, let alone do anything useful with the data itself.

Then, the cloud giants whispered a sweet promise in our ears: “serverless.” Let us handle the tedious infrastructure, they said. You just focus on the data. It sounds like magic, and sometimes it is. But it’s a specific kind of magic, with its own incantations and rules. Let’s explore the fundamental principles of this magic through Google Cloud’s Dataflow, and then see how its cousins at Amazon, AWS Glue and AWS Kinesis, perform similar tricks.

The anatomy of a data pipeline

No matter which magical cloud service you use, the core ritual is always the same. It’s a simple, three-step dance.

Read: You grab your wild data from a source.
Transform: You perform some arcane logic to clean, shape, enrich, or otherwise domesticate it.
Write: You deposit the now-tamed data into a sink, like a database or data warehouse, where it can finally be useful.

This sequence is called a pipeline. In the serverless world, the pipeline is not a physical thing but a logical construct, a recipe that tells the cloud how to process your data.

Shaping the data clay

Once data enters a pipeline, it needs to be held in something. You can’t just let it slosh around. In Dataflow, data is scooped into a PCollection. The ‘P’ stands for ‘Parallel’, which is a hint that this collection is designed to be scattered across many machines and processed all at once. A key feature of a PCollection is that it’s immutable. When you apply a transformation, you don’t change the original collection; you create a brand-new one. It’s like a paranoid form of data alchemy where you never destroy your original ingredients.

Over in the AWS world, Glue prefers to work with DynamicFrames. Think of them as souped-up DataFrames from the Spark universe, built to handle the messy, semi-structured data that Glue often finds in the wild. Kinesis Data Analytics, being a specialist in fast-moving data, treats data as a continuous stream that you operate on as it flows by. The concept is the same, an in-memory representation of your data, but the name and nuances change depending on the ecosystem.

The art of transformation

A pipeline without transformations is just a very expensive copy-paste command. The real work happens here.

Dataflow uses the Apache Beam SDK, a powerful, open-source framework that lets you define your transformations in Java or Python. These operations are fittingly called Transforms. The beauty of Beam is its portability; you can write a Beam pipeline and, in theory, run it on other platforms (like Apache Flink or Spark) without a complete rewrite. It’s the “write once, run anywhere” dream, applied to data processing.

AWS Glue takes a more direct approach. You can write your transformations using Spark code (Python or Scala) or use Glue Studio, a visual interface that lets you build ETL (Extract, Transform, Load) jobs by dragging and dropping boxes. It’s less about portability and more about deep integration with the AWS ecosystem. Kinesis Data Analytics simplifies things even further for its real-time niche, letting you transform streams primarily through standard SQL queries or, for more complex tasks, by using the Apache Flink framework.

Running wild and scaling free

Here’s the serverless punchline: you define the pipeline, and the cloud runs it. You don’t provision servers, patch operating systems, or worry about cluster management.

When you launch a Dataflow job, Google Cloud automatically spins up a fleet of worker virtual machines to execute your pipeline. Its most celebrated trick is autoscaling. If a flood of data arrives, Dataflow automatically adds more workers. When the flood subsides, it sends them away. For streaming jobs, its Streaming Engine further refines this process, making scaling faster and more efficient.

AWS Glue and Kinesis Data Analytics operate on a similar principle, though with different acronyms. Glue jobs run on a pre-configured amount of “Data Processing Units” (DPUs), which it can autoscale. Kinesis applications run on “Kinesis Processing Units” (KPUs), which also scale based on throughput. The core benefit is identical across all three: you’re freed from the shackles of capacity planning.

Choosing your flow batch or stream

Not all data processing needs are created equal. Sometimes you need to process a massive, finite dataset, and other times you need to react to an endless flow of events.

Batch processing: This is like doing all your laundry at the end of the month. It’s perfect for generating daily reports, analyzing historical data, or running large-scale ETL jobs. Dataflow and AWS Glue are both excellent at batch processing.
Streaming processing: This is like washing each dish the moment you’re done with it. It’s essential for real-time dashboards, fraud detection, and feeding live data into AI models. Dataflow is a streaming powerhouse. Kinesis Data Analytics is a specialist, designed from the ground up exclusively for this kind of real-time work. While Glue has some streaming capabilities, they are typically geared towards continuous ETL rather than complex real-time analytics.

Picking your champion

So, which tool should you choose for your data-taming adventure? It’s less about which is “best” and more about which is right for your specific quest.

Choose Google Cloud Dataflow if you value portability. The Apache Beam model is a powerful abstraction that prevents vendor lock-in and is exceptionally good at handling both complex batch and streaming scenarios with a single programming model.
Choose AWS Glue if your world is already painted in AWS colors. Its primary strength is serverless ETL. It integrates seamlessly with the entire AWS data stack, from S3 data lakes to Redshift warehouses, making it the default choice for data preparation within that ecosystem.
Choose AWS Kinesis Data Analytics when your only concern is now. If you need to analyze, aggregate, and react to data in milliseconds or seconds, Kinesis is the sharp, specialized tool for the job.

The serverless horizon

Ultimately, these services represent a fundamental shift in how we approach data engineering. They allow us to move our focus away from the mundane mechanics of managing infrastructure and toward the far more interesting challenge of extracting value from data. Whether you’re using Dataflow, Glue, or Kinesis, you’re leveraging an incredible amount of abstracted complexity to build powerful, scalable, and resilient data solutions. The future of data processing isn’t about building bigger servers; it’s about writing smarter logic and letting the cloud handle the rest.

June 8, 2025 by Fernando SRE Cloud stuff

How AI transformed cloud computing forever

When ChatGPT emerged onto the tech scene in late 2022, it felt like someone had suddenly switched on the lights in a dimly lit room. Overnight, generative AI went from a niche technical curiosity to a global phenomenon. Behind the headlines and excitement, however, something deeper was shifting: cloud computing was experiencing its most significant transformation since its inception.

For nearly fifteen years, the cloud computing model was a story of steady, predictable evolution. At its core, the concept was revolutionary yet straightforward, much like switching from owning a private well to relying on public water utilities. Instead of investing heavily in physical servers, businesses could rent computing power, storage, and networking from providers like AWS, Google Cloud, or Azure. It democratized technology, empowering startups to scale into global giants without massive upfront costs. Services became faster, cheaper, and better, yet the fundamental model remained largely unchanged.

Then, almost overnight, AI changed everything. The game suddenly had new rules.

The hardware revolution beneath our feet

The first transformative shift occurred deep inside data centers, a hardware revolution triggered by AI.

Traditionally, cloud servers relied heavily on CPUs, versatile processors adept at handling diverse tasks one after another, much like a skilled chef expertly preparing dishes one by one. However AI workloads are fundamentally different; training AI models involves executing thousands of parallel computations simultaneously. CPUs simply weren’t built for such intense multitasking.

Enter GPUs, Graphics Processing Units. Originally designed for video games to render graphics rapidly, GPUs excel at handling many calculations simultaneously. Imagine a bustling pizzeria with a massive oven that can bake hundreds of pizzas all at once, compared to a traditional restaurant kitchen serving dishes individually. For AI tasks, GPUs can be up to 100 times faster than standard CPUs.

This demand for GPUs turned them into high-value commodities, transforming Nvidia into a household name and prompting tech companies to construct specialized “AI factories”, data centers built specifically to handle these intense AI workloads.

The financial impact businesses didn’t see coming

The second seismic shift is financial. Running AI workloads is extremely costly, often 20 to 100 times more expensive than traditional cloud computing tasks.

Several factors drive these costs. First, specialized GPU hardware is significantly pricier. Second, unlike traditional web applications that experience usage spikes, AI model training requires continuous, heavy computing power, often 24/7, for weeks or even months. Finally, massive datasets needed for AI are expensive to store and transfer.

This cost surge has created a new digital divide. Today, CEOs everywhere face urgent questions from their boards: “What is our AI strategy?” The pressure to adopt AI technologies is immense, yet high costs pose a significant barrier. This raises a crucial dilemma for businesses: What’s the cost of not adopting AI? The potential competitive disadvantage pushes companies into difficult financial trade-offs, making AI a high-stakes game for everyone involved.

From infrastructure to intelligent utility

Perhaps the most profound shift lies in what cloud providers actually offer their customers today.

Historically, cloud providers operated as infrastructure suppliers, selling raw computing resources, like giving people access to fully equipped professional kitchens. Businesses had to assemble these resources themselves to create useful services.

Now, providers are evolving into sellers of intelligence itself, “Intelligence as a Service.” Instead of just providing raw resources, cloud companies offer pre-built AI capabilities easily integrated into any application through simple APIs.

Think of this like transitioning from renting a professional kitchen to receiving ready-to-cook gourmet meal kits delivered straight to your door. You no longer need deep culinary skills, similarly, businesses no longer require PhDs in machine learning to integrate AI into their products. Today, with just a few lines of code, developers can effortlessly incorporate advanced features such as image recognition, natural language processing, or sophisticated chatbots into their applications.

This shift truly democratizes AI, empowering domain experts, people deeply familiar with specific business challenges, to harness AI’s power without becoming specialists in AI themselves. It unlocks the potential of the vast amounts of data companies have been collecting for years, finally allowing them to extract tangible value.

The Unbreakable Bond Between Cloud and AI

These three transformations, hardware, economics, and service offerings, have reinvented cloud computing entirely. In this new era, cloud computing and AI are inseparable, each fueling the other’s evolution.

Businesses must now develop unified strategies that integrate cloud and AI seamlessly. Here are key insights to guide that integration:

Integrate, don’t reinvent: Most businesses shouldn’t aim to create foundational AI models from scratch. Instead, the real value lies in effectively integrating powerful, existing AI models via APIs to address specific business needs.
Prioritize user experience: The ultimate goal of AI in business is to dramatically enhance user experiences. Whether through hyper-personalization, automating tedious tasks, or surfacing hidden insights, successful companies will use AI to transform the customer journey profoundly.

Cloud computing today is far more than just servers and storage, it’s becoming a global, distributed brain powering innovation. As businesses move forward, the combined force of cloud and AI isn’t just changing the landscape; it’s rewriting the very rules of competition and innovation.

The future isn’t something distant, it’s here right now, and it’s powered by AI.

June 7, 2025 by Fernando SRE Cloud stuff Computer Science stuff