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Kubernetes GPU Scheduling Patterns for AI Workloads at Scale

Kubernetes with Naveen — Tue, 28 Apr 2026 14:24:42 +0000

Designing GPU scheduling in Kubernetes requires more than assigning one pod per GPU. Learn production-grade patterns for AI and ML workloads, including job queues, batching strategies, GPU sharing, and throughput-optimized scheduling.

From Waste to Design: Where We’re Picking Up

By now, the pattern should be clear.

We started this series by uncovering how Kubernetes clusters quietly waste CPU and memory due to inflated requests. Then we saw how requests and limits distort scheduling behavior, and how autoscaling — instead of fixing the issue — often amplifies it when the inputs are wrong.

In Part 4, things escalated. GPU clusters took all of those inefficiencies and turned them into direct financial impact. Idle time became expensive. Allocation without utilization became the default. And the traditional “one pod per resource” model started to fall apart under real AI workloads.

So now we’re at the point where theory isn’t enough.

If you’re running GPU workloads in Kubernetes, the question is no longer why is this inefficient?

The real question is:

What does a well-designed GPU scheduling system actually look like?

The First Mental Shift: You’re Not Scheduling Pods — You’re Scheduling Work

Kubernetes is built around pods, but GPU platforms are built around work units. That difference matters.

A long-running deployment holding a GPU is almost always the wrong abstraction for machine learning workloads. Training jobs, inference batches, data processing pipelines — these are all finite pieces of work with a clear start and end.

When you treat them as services, you inherit all the inefficiencies of service-style scheduling:

GPUs stay allocated between tasks
Idle time accumulates silently
Scaling becomes reactive instead of intentional

The first step toward efficiency is to model workloads as jobs, not services. This alone changes how resources flow through the system.

Queue-Based Scheduling: The Backbone of Efficient GPU Platforms

Once workloads are modeled as jobs, the next step is introducing a queue. Instead of immediately scheduling pods when they are created, jobs enter a queue and are scheduled only when resources are available and it makes sense to run them. This might feel counterintuitive at first. Engineers are used to immediate execution. But queues introduce something critical: control over contention and utilization.

A queue allows you to:

Avoid fragmenting GPU resources
Prioritize important workloads
Batch compatible jobs together
Maintain high utilization without overcommitting

Without a queue, Kubernetes will try to schedule everything immediately, often leading to inefficient placement and unnecessary scaling.

With a queue, you move from reactive scheduling to intentional scheduling.

Throughput vs Latency: The Trade-Off Most Teams Ignore

One of the biggest design decisions in GPU scheduling is choosing between throughput optimization and latency optimization.

Service-oriented thinking prioritizes latency. You want requests to start immediately and complete as fast as possible. This works for APIs and user-facing systems.

GPU workloads are different.

Most AI training and batch inference jobs are not latency-sensitive. They are throughput-sensitive. What matters is how much work gets done over time, not how quickly an individual job starts.

When you optimize for throughput:

Jobs may wait in a queue briefly
GPUs stay consistently busy
Overall system efficiency increases

When you optimize for latency:

Jobs start immediately
GPUs may sit idle between tasks
Utilization drops significantly

Mature platforms make this trade-off explicit. They don’t accidentally drift into a latency-first model — they choose their priorities based on workload characteristics.

GPU Packing: Breaking the “One Pod = One GPU” Model

The default Kubernetes GPU model assumes exclusive allocation. One pod requests one GPU, and that GPU is reserved entirely. This is simple, but often wasteful.

Many workloads don’t need a full GPU continuously. Some use only a fraction of memory or compute capacity. Others are bursty, alternating between active and idle phases.

This opens the door to GPU packing — running multiple workloads on the same GPU.

There are several approaches to this:

Running multiple containers sharing a GPU
Using frameworks that allow partial GPU allocation
Structuring workloads to interleave compute phases

Each approach comes with trade-offs in isolation, performance predictability, and operational complexity.

The key is not to force packing everywhere, but to identify workloads that can safely share without impacting correctness or performance. Even modest improvements in packing efficiency can lead to significant cost savings.

Job Lifecycle Discipline: Where Most Savings Come From

One of the most overlooked areas in GPU platforms is job lifecycle management.

A GPU is only useful while it’s actively executing work. The moment a job finishes — or effectively stops doing useful computation — that GPU should be released. In practice, this doesn’t always happen.

Common issues include:

Jobs that linger after completion
Processes waiting indefinitely on external dependencies
Cleanup steps that unnecessarily hold GPU resources
Orchestrations that don’t terminate cleanly

These small inefficiencies accumulate quickly.

The most effective platforms enforce strict lifecycle discipline:

Jobs have clear completion criteria
Resources are released immediately after completion
Idle states are minimized or eliminated

This is not glamorous work, but it often delivers the highest return on investment.

Scheduling Policies: Turning Infrastructure into a Platform

At scale, GPU scheduling is no longer just about placing workloads — it becomes about defining policies. These policies answer questions like:

Which jobs get priority during contention?
Can lower-priority jobs be preempted?
How are resources shared across teams?
What happens when demand exceeds supply?

Without explicit policies, the system defaults to first come, first served, which is rarely optimal. With policies, you can align infrastructure behavior with business priorities. For example, production inference workloads might take precedence over experimental training jobs. High-priority research might preempt lower-value batch processing. Teams might be allocated quotas to prevent resource monopolization. These decisions are not purely technical. They reflect how the organization values different types of work.

Why Kubernetes Alone Is Not Enough

Kubernetes provides the primitives for scheduling, but it does not provide a complete GPU scheduling system out of the box. This is where many teams get stuck.

They expect Kubernetes to solve higher-level scheduling problems that it was never designed to handle:

Queue management
Fairness across teams
Workload prioritization
Efficient batching

To address these gaps, teams often introduce additional layers:

Job schedulers
Queueing systems
Custom controllers
Workflow orchestration tools

The goal is not to replace Kubernetes, but to build on top of it with a system that understands the semantics of AI workloads.

The Most Important Metric: GPU Busy Time

If you had to track one metric to evaluate your GPU platform, it wouldn’t be raw utilization. It would be GPU busy time as a percentage of allocation time.

This captures the real efficiency of your system:

How long GPUs are allocated
How much of that time is spent doing useful work

Everything in this post — queues, packing, lifecycle management, policies — ultimately aims to improve this metric.

When GPU busy time increases, costs stabilize and throughput improves.

What a Mature GPU Platform Looks Like

In well-designed systems, things feel very different.

Workloads don’t immediately grab GPUs — they enter a queue and are scheduled intentionally. GPUs rarely sit idle because jobs are batched and packed efficiently. Resource allocation reflects priority and business value, not just timing.

Engineers understand that GPUs are shared infrastructure, not personal resources. Jobs are designed to release resources quickly. Metrics are trusted, and inefficiencies are visible.

Most importantly, the system behaves predictably. And just like we discussed in earlier parts of this series, predictability is what allows efficiency to emerge.

Closing Thoughts

Efficient GPU scheduling is not about squeezing every last percentage point of utilization. It’s about designing a system where waste is hard to hide and easy to correct.

Kubernetes gives you the foundation, but it’s not the full solution. The real work lies in how you model workloads, how you control scheduling, and how you align infrastructure with organizational priorities.

If you treat GPUs like CPU, you will overspend.
If you treat GPU scheduling as a first-class system, you will gain control.

Key Takeaways

GPU scheduling must be job-oriented, not pod-oriented, to eliminate idle allocation and improve utilization.
Queues and scheduling policies are essential, enabling intentional resource allocation and higher throughput.
Lifecycle discipline and GPU packing drive the biggest efficiency gains, not just better configuration.

So, what coming next?

Next up, in Part 6, we’ll tackle something equally important and often ignored: How to make Kubernetes cost visible — without turning it into a political battle between teams.

From Campus to Big Tech: The Unfiltered, Deep-Dive Playbook for Indian CS Students to Crack FAANG+ (2026 Edition)

Kubernetes with Naveen — Sun, 26 Apr 2026 14:14:46 +0000

A no-BS, deeply detailed guide—built from real recruiter and engineer insights—on exactly how Indian CS freshers can prepare, stand out, and land offers from top tech companies in 2026.

I Didn’t Just Research This — I Went Straight to the Source

Over the last few years, I’ve gone beyond blog posts and YouTube advice and spent time speaking directly with recruiters, hiring committee members, and engineers working at companies like Google, Microsoft, Meta, Uber, Airbnb, and Oracle. These weren’t motivational chats—they were brutally honest discussions about rejection patterns, hiring signals, and what separates a selected candidate from the thousands who never hear back.

One insight stood out across all of them: most Indian CS students are not failing because they’re incapable—they’re failing because they’re preparing in the wrong direction. This guide is designed to correct that trajectory.

Step 1: Stop Dreaming Vaguely — Start Targeting Precisely

A vague ambition like I want to work at Google is emotionally satisfying but strategically useless. Big tech hiring is highly role-specific, and your preparation must align with the exact expectations of that role. A backend engineer is evaluated very differently from a machine learning engineer, and even within backend, expectations differ across companies.

You need to clearly define your path early: backend engineering is the most accessible and structured route for freshers, while frontend requires deeper understanding of performance and UX trade-offs, and ML roles demand strong mathematical foundations along with practical exposure.

Practical tip: Study 20–30 LinkedIn profiles of engineers who joined these companies as freshers. Reverse-engineer their journey—what skills they built, what projects they did, and how early they started. This gives you a realistic blueprint instead of a fantasy roadmap.

Step 2: Understand How Big Tech Actually Hires

Most students misunderstand the hiring process because they rely on second-hand stories. In reality, companies like Google and Microsoft follow a structured and signal-driven process where each stage evaluates specific competencies.

Resume screening is not about fancy formatting—it’s about signal strength. Online assessments are designed to eliminate weak problem solvers quickly. Technical interviews go deeper, focusing not just on correctness but on thinking patterns. At companies like Google, the hiring committee evaluates consistency across interviews rather than a single strong performance.

Critical insight: Interviewers are trained to look for repeatable signals. One lucky solution won’t get you selected—but consistent structured thinking will.

Cool tip: Practice solving problems with a timer and simulate interview pressure. Most candidates fail not because they don’t know the solution, but because they can’t perform under time constraints.

Step 3: Build the Only Skill That Truly Matters — Problem Solving

Data Structures and Algorithms (DSA) are not just a filtering mechanism—they are the foundation of how these companies evaluate your ability to think. Every recruiter I spoke to emphasized that strong DSA skills are non-negotiable, especially for freshers.

Your preparation should not be random. Platforms like LeetCode, Codeforces, and GeeksforGeeks are tools—but what matters is how you use them.

Instead of solving hundreds of problems superficially, focus on pattern recognition. For example, once you understand sliding window or two-pointer techniques deeply, you should be able to identify them across different problems instantly.

Advanced tip: Maintain a mistake journal. Every time you fail a problem, write down why you failed—was it logic, edge cases, or misunderstanding the problem? Reviewing this journal weekly accelerates improvement dramatically.

Step 4: Projects Matter—But Only If They Show Depth

Projects are often misunderstood. Recruiters are not impressed by the number of projects—they are impressed by depth, ownership, and clarity of thought. A single well-executed project can outperform five shallow ones.

A strong project demonstrates your ability to think beyond code—how systems scale, how failures are handled, and how performance is optimized. For example, building a URL shortener is valuable only if you can discuss database sharding, caching strategies, and rate limiting.

Cool tip: Record a short 2–3 minute video explaining your project architecture and host it with your GitHub repository. This is rare—and it instantly differentiates you.

Step 5: Resume — The Brutal Truth Recruiters Won’t Sugarcoat

Your resume is not a document—it’s a marketing pitch. Recruiters scan it in seconds, looking for proof of competence. If your resume does not communicate impact clearly, it will be ignored.

Strong resumes quantify everything—performance improvements, scale, efficiency gains. Weak resumes list technologies without context.

Insider advice: Many big tech recruiters use internal tools that highlight keywords and signals. If your resume doesn’t clearly show DSA proficiency or project depth, it may never even reach a human reviewer.

Cool tip: Get your resume reviewed by someone who already works in big tech—not your college placement cell.

Step 6: How to Actually Get Interview Calls

This is where most students fail—not because they lack skills, but because they rely on ineffective strategies. Applying blindly through portals has a very low success rate due to sheer competition.

Referrals significantly increase your chances, but they are not magic. A weak resume with a referral still gets rejected.

Platforms like LinkedIn are powerful if used correctly. Instead of sending generic messages, personalize your outreach. Show that you’ve done your research and explain why you’re a strong candidate.

Cool tip: Participate in hackathons and coding contests. Many companies use these as alternative hiring funnels, and performance here can directly lead to interview calls.

Step 7: Interview Preparation — What Really Happens Inside

Technical interviews are designed to evaluate how you think under pressure. Interviewers are less interested in whether you arrive at the correct solution immediately and more interested in how you approach the problem.

Strong candidates communicate their thought process clearly, consider edge cases, and iterate on their approach. Weak candidates either stay silent or jump straight into coding without planning.

**Insider tip: Interviewers often give subtle hints. Your ability to pick up and act on these hints is a major evaluation signal.

Cool tip: Practice mock interviews with peers or platforms and record yourself. Watching your own interview performance is uncomfortable—but incredibly effective.

Step 8: System Design — The Early Differentiator

While traditionally reserved for experienced roles, basic system design is increasingly being tested even for freshers, especially in top-tier companies.

You are not expected to design large-scale systems like a senior engineer, but you should understand fundamentals—how APIs work, how databases scale, and how systems handle traffic.

Cool tip: Learn to explain system design using simple analogies. If you can explain caching using a real-world example, you automatically stand out.

Step 9: Soft Skills — The Silent Deal Breaker

Soft skills are often underestimated, but they are critical. Many candidates with strong technical skills get rejected because they fail to communicate effectively.

Interviewers evaluate clarity, confidence, and collaboration mindset. They are essentially asking: “Would I want to work with this person?”

Cool tip: Practice explaining complex problems in simple language. If you can teach something clearly, you can definitely explain it in an interview.

Step 10: AI Skills — The 2026 Game Changer

This is the part most guides still ignore.

In 2026, having basic AI awareness is no longer optional—it’s a differentiator.

You don’t need to become a machine learning expert, but you should:

Understand how models work conceptually
Use APIs from tools like OpenAI
Build small AI-powered features (chatbots, recommendation systems)

Companies increasingly value engineers who can integrate AI into products.

Practical tip: Build one AI-powered project—for example, a resume analyzer or smart search system. This shows you can work with modern tools.

Advanced tip: Learn prompt engineering and understand how LLMs behave. Engineers who can effectively leverage AI tools are becoming significantly more productive—and companies notice that.

Step 11: The Timeline That Actually Works

Your journey should be structured, not chaotic. Early years should focus on fundamentals, while later years should emphasize depth and interview readiness.

The biggest mistake students make is delaying serious preparation until the final year. By then, it’s often too late to build strong fundamentals.

Cool tip: Treat your preparation like a long-term investment. Even 2–3 focused hours daily over two years can outperform last-minute cramming.

What Nobody Tells You (But You Must Accept)

The hiring process is not always fair. You may get rejected despite strong performance. You may face tougher questions than others. But over time, consistent preparation outweighs randomness.

Another hard truth: most students quit too early. They solve 100 problems, face a few rejections, and assume they’re not good enough. The ones who succeed are simply the ones who keep going longer.

Final Words: This Is a Discipline Game, Not a Talent Game

Big tech hiring is not about brilliance—it’s about consistency, clarity, and preparation. If you commit to this process seriously for the next 12–18 months, you will transform into a candidate these companies actively want to hire.

And once you reach that level, something powerful happens:

You stop chasing opportunities—opportunities start chasing you.

The Platform Beneath the Platform: Building an Internal Developer Platform That Actually Works

Kubernetes with Naveen — Thu, 23 Apr 2026 11:53:00 +0000

A real-world, deeply practical guide to understanding Platform Engineering and Internal Developer Platforms (IDPs)—why they matter, where teams go wrong, and how to build a Kubernetes-centered ecosystem that developers actually want to use.

Introduction: The Problem We Pretend Doesn’t Exist

Let’s get one thing straight—most organizations that say they have a platform… don’t. They have a collection of tools, a few pipelines, maybe a Kubernetes cluster or two, and a lot of tribal knowledge stitched together with Slack threads and outdated documentation. That’s not a platform. That’s controlled chaos.

I’ve been in enough war rooms to see this pattern repeat. A team proudly claims standardization, yet every service is deployed differently, onboarding is still painful, and debugging an issue feels like archaeology. The uncomfortable truth is that Kubernetes didn’t simplify things—it amplified the need for structure. It gave us power, but not clarity.

And that’s exactly why Platform Engineering exists. Not as a trend, not as a rebranding of DevOps, but as a response to a very real scaling problem—how do you enable hundreds of engineers to move fast without breaking everything?

What Is Platform Engineering (Really)?

Platform Engineering is often misunderstood because people approach it as an infrastructure initiative. It’s not. At its core, it is a product discipline applied to internal systems. The moment you start treating your platform as something developers consume, rather than something ops teams maintain, your entire mindset shifts.

You begin to think in terms of usability, discoverability, and consistency. You start asking whether a new engineer can deploy a service on day one without asking for help. You question whether your abstractions actually reduce cognitive load or just move it around.

An Internal Developer Platform (IDP) is simply the manifestation of this thinking. It is the interface between developers and the underlying complexity of cloud-native systems. And like any good product, its success is not measured by how sophisticated it is, but by how effortlessly it is adopted.

The IDP Is Not a Tool—It’s an Experience

One of the biggest misconceptions I see is teams equating tooling with platform maturity. They install Kubernetes, layer on GitOps, integrate observability stacks, and assume the job is done. But what they’ve really built is a toolkit, not an experience.

A true IDP is defined by how it feels to use it. When a developer wants to ship a service, the process should be intuitive, almost boring in its predictability. There should be no ambiguity about how things are done, no need to reverse-engineer another team’s setup, and no dependency on a platform engineer to unblock progress.

If developers are still navigating YAML files they don’t fully understand, or relying on institutional knowledge to get things running, then the platform has failed its primary purpose. The goal is not to expose power—it is to abstract complexity without hiding capability.

The Core Building Blocks (And Why They Matter Together)

The modern platform ecosystem is often described in terms of components—Kubernetes, GitOps, observability—but their real value only emerges when they operate as a cohesive system. Individually, they solve problems. Together, they define a workflow.

1. Kubernetes: The Substrate, Not the Solution

Kubernetes is often treated as the end goal, but in reality, it is just the foundation. It provides a powerful control plane that standardizes how workloads are scheduled, scaled, and managed. However, its raw form is far too granular for most developers.

When developers are forced to interact directly with Kubernetes primitives, they inherit its complexity. Concepts like deployments, services, ingress rules, and resource limits become part of their daily workflow, which increases cognitive load and slows down development.

A well-designed platform acknowledges this and builds abstractions on top. Developers shouldn’t need to think in terms of pods or replica sets. They should think in terms of services, APIs, and environments. Kubernetes should exist beneath the surface, doing its job quietly, without demanding attention.

2. GitOps: The Backbone of Consistency

GitOps introduces a level of discipline that most organizations desperately need. By making Git the single source of truth, it transforms deployments from procedural tasks into declarative states. This shift is subtle but powerful.

Instead of executing commands to achieve a desired outcome, you define the outcome and let the system reconcile toward it. This creates a consistent, auditable, and reversible workflow that scales naturally with team size.

More importantly, GitOps eliminates ambiguity. What is running in production is exactly what is defined in Git—nothing more, nothing less. This alignment reduces drift, simplifies debugging, and builds trust in the system.

But GitOps alone is not enough. Without proper abstractions, it can still expose too much complexity. The platform’s role is to ensure that interacting with GitOps feels natural, not burdensome.

3. Observability: Your Platform’s Nervous System

Observability is often added as an afterthought, but in a mature platform, it is a first-class concern. It is not just about collecting metrics or storing logs—it is about enabling understanding.

When something goes wrong, developers should be able to trace a request across services, inspect logs in context, and correlate metrics without switching between tools or waiting for access. Observability should not be a separate system; it should be embedded into the platform experience.

The real power of observability lies in its ability to reduce uncertainty. It turns guesswork into insight, and incidents into learning opportunities. Without it, even the most well-designed platform becomes fragile under pressure.

4. Developer Self-Service: The End Goal

All of these components ultimately serve one purpose—enabling self-service. But self-service is often misunderstood as unrestricted access. In reality, effective self-service is carefully designed.

It provides developers with the ability to perform common tasks independently, while ensuring that those actions are safe, compliant, and consistent. It removes bottlenecks without introducing chaos.

A good platform feels like a well-designed system of roads. Developers can move quickly and independently, but the paths are clearly defined, and guardrails are built in. They don’t need to understand the entire infrastructure—they just need to know how to navigate it.

The Missing Layer: Abstractions That Make It Usable

This is where most platforms either succeed or fail. The missing layer is not another tool, but a set of abstractions that translate developer intent into platform operations.

When a developer says, “I need a backend service,” the platform should understand what that means. It should provision the necessary infrastructure, configure pipelines, enable observability, and enforce policies—all without requiring the developer to orchestrate these steps manually.

This layer often manifests as templates, CLIs, or developer portals, but its true value lies in how well it encapsulates complexity. It defines the contract between developers and the platform, and it determines whether the platform feels empowering or obstructive.

Golden Paths: The Secret Sauce Nobody Talks About Enough

Golden paths are where theory meets reality. They represent the most efficient and supported way to accomplish common tasks within the platform.

A well-designed golden path removes decision fatigue. It answers questions before they are asked and provides a clear, reliable route from idea to production. It does not eliminate flexibility, but it makes the default path so effective that most developers have no reason to deviate.

This is where platform engineering becomes an exercise in empathy. You are not just defining workflows—you are shaping how developers experience their daily work. When golden paths are done right, they fade into the background, enabling focus rather than demanding attention.

Standardization Without Killing Innovation

Standardization is often perceived as a constraint, but in reality, it is an enabler. By standardizing the repetitive and operational aspects of development, you free up mental space for creativity and problem-solving.

The key is knowing where to draw the line. Infrastructure, deployment patterns, and observability should be consistent across the organization. These are the areas where variability introduces risk without adding value.

At the same time, developers should retain the freedom to choose the tools and approaches that best suit their domain. A platform should guide, not dictate. It should provide a strong foundation while allowing room for innovation on top.

What Most Teams Get Wrong

The most common mistake teams make is starting with tools instead of problems. They adopt technologies because they are popular, not because they address a specific need. This leads to platforms that are technically impressive but practically unusable.

Another frequent issue is neglecting developer experience. A platform that is difficult to use will simply be bypassed, no matter how well it is designed. Adoption is not automatic—it must be earned.

There is also a tendency to over-engineer early on, building complex systems before understanding real-world requirements. And perhaps most critically, many teams fail to treat the platform as a product. Without feedback loops and continuous iteration, even the best intentions fall short.

How to Actually Build an IDP (Practical Approach)

Building an effective IDP is less about following a predefined blueprint and more about responding to real challenges. It begins with identifying areas of friction—those moments where developers are slowed down, confused, or blocked.

From there, the focus should be on creating seamless experiences for the most common workflows. This is where golden paths come into play. By simplifying these paths, you create immediate value and build trust in the platform.

Introducing GitOps helps establish consistency, while embedding observability ensures visibility from the start. The addition of a self-service layer then ties everything together, allowing developers to interact with the platform independently.

But the process does not end there. A platform is never finished. It evolves continuously, shaped by feedback, usage patterns, and changing requirements.

The Cultural Shift (This Is the Hard Part)

The technical challenges of building a platform are significant, but they are not the hardest part. The real difficulty lies in changing how teams think and operate.

Platform teams must adopt a product mindset, prioritizing user experience and measuring success through adoption and satisfaction. Developers, in turn, must learn to trust the platform and embrace standardized workflows.

This shift requires alignment, communication, and a willingness to iterate. It is not something that can be enforced—it must be cultivated over time.

Key Takeaways

The first and most important takeaway is that an Internal Developer Platform is not defined by the tools it uses, but by the experience it delivers. Without a focus on usability and developer experience, even the most advanced stack will fail to achieve its purpose.

Secondly, abstraction is the true power of platform engineering. The goal is not to expose infrastructure, but to translate complexity into simple, intuitive interactions that developers can rely on.

Finally, platform engineering is as much a cultural transformation as it is a technical one. Success depends on treating the platform as a product, continuously evolving it based on feedback, and aligning it with the needs of its users.

Closing Thoughts: The Platform You Don’t Notice

A great platform does not announce itself. It does not demand attention or require constant explanation. It simply works, quietly enabling developers to focus on what truly matters.

And here’s the truth most people won’t say out loud—if your developers are still thinking about your platform, you haven’t built it right yet.

Why GPU Clusters Bleed Money in Kubernetes (and How to Stop It)

Kubernetes with Naveen — Tue, 21 Apr 2026 15:00:27 +0000

GPU workloads amplify every Kubernetes resource management mistake. Learn why GPU clusters waste massive amounts of money, how scheduling and allocation really work, and what production-grade strategies reduce idle GPU time in AI/ML platforms.

Before We Talk About GPUs, Let’s Be Honest About What We’ve Been Doing.

In the last three parts of this multi-part series, we’ve been building toward a simple but uncomfortable truth.

We started by looking at why Kubernetes clusters appear full while doing very little actual work. The root cause wasn’t Kubernetes itself, but the way we define resource requests. We treat them as safety buffers instead of realistic baselines, and the scheduler blindly trusts those numbers.

Then we went deeper into requests and limits, and things became clearer. Requests are not estimates — they are reservations. Limits are not safety nets — they are enforcement mechanisms with very different behaviors for CPU and memory. Most teams don’t revisit these values often enough, and over time they drift far away from reality.

So by this point, we already know something important:

We are feeding Kubernetes inaccurate information, and it is making perfectly logical — but very expensive — decisions based on that. Now take all of those problems… and apply them to the most expensive resource in your infrastructure.

That’s your GPU cluster.

GPUs Change the Economics Completely.

CPU waste is frustrating. Memory waste is inefficient. GPU waste is financially brutal.

A single high-end GPU can cost anywhere from hundreds to thousands of dollars per month, depending on the cloud and instance type. Unlike CPU and memory, which can be overcommitted and shared relatively easily, GPUs are typically allocated exclusively.

When a pod requests a GPU, it usually gets the whole device. That means one simple thing: If your GPU is idle, you are still paying full price. There is no graceful degradation here. No partial utilization savings. No background sharing unless you explicitly design for it. And this is where most Kubernetes patterns start to break down.

The Default GPU Model Is Fundamentally Wasteful

Most teams start with a straightforward model:

resources:
  limits:
    nvidia.com/gpu: 1

This looks clean. One pod, one GPU. Isolation is guaranteed. Debugging is easier.

It also creates a silent assumption:

“This workload needs a full GPU all the time.”

In reality, very few workloads behave that way. Machine learning jobs are often bursty. They load data, preprocess it, perform computation, write results, and repeat. Large portions of that lifecycle don’t fully utilize the GPU. In some cases, the GPU is completely idle while the process waits on I/O or CPU-bound steps.

But Kubernetes doesn’t care about utilization. It only cares about allocation. So the GPU stays locked.

The Biggest Lie in GPU Platforms: Utilization Looks Fine

If you’ve ever looked at GPU dashboards, you’ve probably seen utilization numbers that seem reasonable. Maybe 60%, maybe 70%. But those numbers often hide a much more important metric: Allocation time vs actual compute time

A GPU might be allocated to a pod for 10 hours, but actively computing for only 4 of those hours. The remaining time is lost to:

Data loading
Preprocessing
Synchronization
Idle waiting between steps

From a billing perspective, you paid for 10 hours. From a workload perspective, you only used 4. This gap is where most GPU budgets disappear.

And unlike CPU inefficiency, this doesn’t show up clearly unless you’re explicitly looking for it.

Why Traditional Kubernetes Thinking Fails for GPUs

Everything we discussed in earlier parts becomes more dangerous with GPUs. Over-requesting CPU leads to wasted nodes.
Over-requesting GPUs leads to direct financial loss per workload. Inflated requests distort scheduling.
With GPUs, they also block access for other jobs entirely.

Autoscaling helps absorb CPU load. With GPUs, scaling is slower, more expensive, and often constrained by quota.

Even the concept of “baseline usage” becomes harder to define. GPU workloads are not long-running services in the traditional sense. They are often batch jobs, experiments, or pipelines with unpredictable behavior.

Trying to apply service-style Kubernetes patterns to GPU workloads is one of the biggest architectural mistakes teams make.

The Real Problem: Treating GPUs Like CPU

At a fundamental level, most inefficiencies come from treating GPUs like just another resource dimension.

They are not.

CPU and memory are designed for sharing. GPUs are not — at least not by default. CPU workloads tend to be continuous and predictable. GPU workloads are often spiky and pipeline-driven.

When you apply the same assumptions to both, the system behaves poorly.

This is why simply “adding autoscaling” or “tuning requests” is not enough for GPU clusters. The problem is not just configuration — it’s the workload model itself.

What Actually Works in GPU Clusters

The turning point for most organizations comes when they stop thinking in terms of pods and start thinking in terms of jobs and throughput.

Instead of long-running GPU-bound pods, successful platforms move toward:

Short-lived, well-defined jobs
Clear lifecycle boundaries
Aggressive resource release after completion

This shift alone can dramatically reduce idle GPU time.

Another key change is how GPUs are allocated. Rather than defaulting to one pod per GPU, teams begin to explore ways to increase utilization:

Packing multiple lightweight workloads onto a single GPU
Using batching strategies to keep GPUs busy
Scheduling based on queue depth instead of static deployments

These approaches require more sophistication, but the payoff is significant.

Why GPU Scheduling Needs Intentional Design

Unlike CPU scheduling, GPU scheduling cannot be left entirely to default Kubernetes behavior.

You need to answer questions like:

Should jobs wait in a queue or start immediately?
Is throughput more important than latency?
Can workloads share GPUs safely?
How do you prioritize expensive jobs?

These are not just technical decisions — they are platform policies.

Without clear answers, GPU clusters tend to drift toward the simplest model: immediate allocation, full isolation, and minimal coordination. That model is easy to implement, but extremely inefficient at scale.

The Cultural Shift: GPUs Are Not Owned Resources

One of the hardest transitions is not technical — it’s organizational.

In many teams, GPUs are treated as owned resources. A team requests them, holds them, and releases them when they’re done (sometimes much later than necessary).

In efficient platforms, GPUs are treated as shared, high-cost infrastructure. They are borrowed, not owned. Their usage is visible. Their cost is understood. This shift changes behavior more than any scheduler ever will.

When engineers know that idle GPUs are costing real money, they start designing workloads differently. They optimize pipelines, reduce idle time, and release resources faster.

Where Most GPU Optimization Efforts Fail

The biggest mistake teams make is trying to optimize GPU usage without fixing visibility.

If you cannot answer:

How long GPUs are allocated
How much of that time is active compute
Which workloads are wasting the most

Then any optimization effort is guesswork. And guesswork, in GPU environments, is expensive.

Closing Thoughts

GPU clusters don’t introduce new problems — they expose existing ones.

Everything we covered in earlier parts of this series still applies:

Requests must be honest
Autoscaling must be understood
Metrics must reflect reality

But with GPUs, the cost of getting these wrong is immediate and undeniable. Kubernetes gives you the building blocks to manage GPU workloads, but it does not give you a cost-efficient system out of the box. That requires intentional design, better workload patterns, and a shift in how teams think about resource ownership.

If CPU waste is a slow leak, GPU waste is a wide-open valve.

So, what coming next?

A practical look at how mature platforms schedule GPUs intentionally. Learn how batch queues, shared GPUs, and job lifecycle control dramatically improve utilization.

KubeCon + CloudNativeCon EU 2026: The Year Kubernetes Grew Up (Again)

Kubernetes with Naveen — Thu, 09 Apr 2026 12:03:04 +0000

From AI-native infrastructure to platform engineering maturity, KubeCon + CloudNativeCon Europe 2026 in Amsterdam wasn’t about hype—it was about hard truths, real workloads, and where cloud-native is actually heading next.

Walking into Amsterdam: A Different Kind of Energy

I’ve been to more KubeCons than I can count, but KubeCon + CloudNativeCon Europe 2026 genuinely felt different the moment I walked into the venue. It wasn’t the scale—that’s always massive. It wasn’t the crowd—that’s always global, diverse, and buzzing. It was the tone. There was a certain quiet confidence in the air, almost like the ecosystem had collectively stopped trying to prove itself. Kubernetes has already won. That debate is over. What replaced that energy was something far more interesting—introspection.

You could feel it in the keynotes, in the breakout sessions, even in the hallway track conversations. People weren’t trying to impress anymore; they were trying to solve. Engineers spoke less about possibilities and more about consequences. The questions were sharper, the answers more grounded. There was less applause for shiny demos and more attention given to war stories—real production failures, scaling bottlenecks, and organizational friction.

And honestly, that’s what made this KubeCon stand out. It didn’t feel like a conference about technology adoption. It felt like a conference about technology responsibility.

The Big Shift: From Kubernetes Adoption → Kubernetes Optimization

A few years ago, the narrative was dominated by adoption stories—companies proudly talking about their migration journeys, the number of clusters they spun up, and how quickly they “Kubernetized” everything. That narrative is now completely exhausted. At KubeCon EU 2026, nobody cares how fast you adopted Kubernetes. The only thing that matters is how well you’re running it.

What became clear across multiple talks is that organizations are now entering a second phase—post-adoption reality. This is where the real work begins. Teams are dealing with spiraling cloud costs, operational overhead, alert fatigue, and the cognitive burden of managing increasingly complex systems. Kubernetes didn’t create these problems, but it amplified them by making it incredibly easy to scale complexity.

There was a noticeable shift in language. Words like “efficiency,” “right-sizing,” “operational maturity,” and “sustainability” kept coming up. The industry is starting to accept a hard truth: running Kubernetes is not the achievement—it’s the baseline. The real challenge is running it efficiently, predictably, and without burning out your engineers.

What struck me most was how many teams openly admitted they had over-engineered their systems. Kubernetes gave them power, and they used all of it—often unnecessarily. Now they’re paying the price and trying to simplify without breaking everything.

Platform Engineering Took Center Stage (And Finally Grew Up)

Platform engineering has been a buzzword for a while now, but this was the first KubeCon where it felt truly mature. Not in the sense that everyone has figured it out—but in the sense that people are finally asking the right questions.

The biggest shift is philosophical. Teams are no longer building platforms as internal infrastructure projects; they are building them as products. That distinction changes everything. When you think like a product team, you start caring about user experience, adoption, feedback loops, and iterative improvement. And in this case, your users are developers.

There were multiple sessions where companies shared how their first attempt at an internal platform failed—not because of technical limitations, but because of poor developer experience. They built abstractions on top of Kubernetes, but those abstractions still leaked complexity. Developers were forced to understand YAML, CRDs, and cluster behavior just to deploy a simple service. That’s not a platform—that’s just Kubernetes with extra steps.

The more successful stories had something in common: they embraced opinionation. Instead of offering infinite flexibility, they provided curated paths—golden paths—that solved 80% of use cases extremely well. They reduced decision fatigue, enforced best practices by default, and made the “right way” the easiest way.

Another important evolution was cultural. Platform teams are starting to measure success not by how many features they build, but by how little developers need to think about infrastructure. That’s a subtle but powerful shift.

AI + Kubernetes: Less Hype, More Reality

AI was everywhere at the conference, but interestingly, the tone was far more grounded than the industry hype we’ve been seeing elsewhere. There were no grand claims about Kubernetes magically solving AI infrastructure. Instead, what we saw was a deep, sometimes uncomfortable exploration of how Kubernetes struggles under the weight of AI workloads.

Cost Is Now a First-Class Concern

If there was one topic that carried a sense of urgency across the conference, it was cost. Not in a theoretical sense, but in a very real, “this is getting out of hand” kind of way.

For years, the focus was on scalability and resilience. Cost was often treated as a secondary concern—something to optimize later. That “later” has arrived. Organizations are now facing cloud bills that are difficult to justify, and Kubernetes is often at the center of that conversation.

One of the recurring themes was invisibility of waste. Kubernetes abstracts away infrastructure so effectively that it becomes easy to lose track of how resources are being used. Idle workloads, over-provisioned containers, inefficient scheduling—all of these contribute to unnecessary costs, but they’re not always obvious.

FinOps is no longer a separate function. It’s being integrated directly into platform engineering. Engineers are now expected to understand the cost implications of their architectural decisions. Tools are evolving to provide better visibility, but more importantly, teams are adopting practices that prioritize efficiency from the start.

There’s also a growing acceptance that not every workload needs to run at peak performance all the time. The idea of dynamically adjusting resource allocation based on actual demand is gaining traction, and spot instances—once considered risky—are becoming more widely adopted with better safeguards in place.

The Multi-Cluster Reality Check

Multi-cluster strategies have been discussed for years, often in aspirational terms. At this KubeCon, the conversation shifted from aspiration to reality—and reality, as it turns out, is messy.

arge organizations are now operating dozens, sometimes hundreds, of clusters across different environments. Managing this at scale introduces a level of complexity that most tools and practices were not originally designed to handle.

One of the biggest challenges is consistency. Ensuring that policies, configurations, and security standards are applied uniformly across clusters is non-trivial. Drift becomes inevitable, and debugging issues across clusters can feel like chasing ghosts.

Another challenge is visibility. Observability tools often struggle to provide a cohesive view across multiple clusters, making it harder to understand system-wide behavior.

What’s emerging is a shift in perspective. Instead of treating each cluster as an independent unit, teams are starting to think in terms of cluster fleets. This involves centralized control planes, standardized configurations, and stronger governance models.

But perhaps the most important takeaway is this: multi-cluster is not just a technical problem. It’s an operational discipline that requires careful planning, clear ownership, and continuous investment.

Backstage Pass: What People Said Off the Record

The most valuable insights didn’t come from the stage—they came from conversations in hallways, over coffee, and during late evening meetups. This is where people drop the polished narratives and speak candidly.

There was a surprising level of humility in these conversations. Engineers openly admitted mistakes, shared lessons learned, and questioned long-held assumptions. There was a collective recognition that, in many cases, the industry has been chasing complexity for its own sake.

One recurring sentiment was frustration with tool sprawl. Many teams feel overwhelmed by the sheer number of tools in the cloud-native ecosystem, each solving a narrow problem but adding to the overall cognitive load.

Another common theme was burnout. Managing Kubernetes at scale is not trivial, and the operational burden can be significant. Teams are starting to push back, advocating for simpler architectures and more sustainable practices.

What stood out to me was not just what people said, but how they said it. There was less ego, more honesty, and a genuine desire to learn from each other. That, more than anything, felt like a sign of maturity in the ecosystem.

What Will Trend After KubeCon 2026

Looking ahead, the trends emerging from this conference are not about new technologies, but about new priorities. The focus is shifting from expansion to refinement.

We’re likely to see a rise in more opinionated platform solutions that prioritize developer experience over flexibility. These platforms will aim to reduce cognitive load and provide clear, well-defined paths for common tasks.

AI infrastructure will continue to influence Kubernetes development, particularly in areas like scheduling and resource management. As AI workloads become more prevalent, the pressure to optimize for them will increase.

Cost optimization will remain a key focus, driving innovation in both tooling and practices. Organizations will invest more in understanding and controlling their cloud spending.

There will also be a stronger emphasis on simplicity. Teams that can reduce complexity without sacrificing capability will have a significant advantage.

And finally, multi-cluster management will evolve into a more structured discipline, with better tools, practices, and frameworks to support it.

Where You Should Really Focus (If You’re a Platform/DevOps Engineer)

If you’re working in this space, the temptation is to keep up with every new project and trend. But what this KubeCon made clear is that success doesn’t come from knowing more tools—it comes from making better decisions.

Your focus should be on improving developer experience. If your platform makes it harder for developers to do their job, it’s not working, no matter how technically advanced it is.

You should also invest time in understanding cost. This doesn’t mean memorizing pricing models, but developing an intuition for how architectural choices impact resource usage and spending.

Adopting a workload-centric mindset can also be transformative. Instead of thinking in terms of clusters and infrastructure, focus on what your applications actually need to run efficiently.

Observability should move beyond dashboards. The goal is not to collect more data, but to extract meaningful insights that can drive action.

And perhaps most importantly, learn to say no. Not every tool is worth adopting, and not every problem requires a new solution. Sometimes, the best decision is to do less.

The Real Takeaway

If I had to distill everything from KubeCon + CloudNativeCon Europe 2026 into a single idea, it would be this: the Kubernetes ecosystem is entering a phase of self-reflection.

We’re no longer in the phase of rapid expansion and experimentation. We’re in the phase of consolidation and optimization. The focus is shifting from what Kubernetes can do to how we should use it.

This shift is not driven by technology, but by experience. Teams have learned what works and what doesn’t, often the hard way. And they’re now applying those lessons to build systems that are not just powerful, but sustainable.

Kubernetes didn’t suddenly change this year. But the way we think about it did. And that shift, subtle as it may seem, is what will define the next chapter of cloud-native computing.

Kubernetes for HPC: The Quiet Convergence Reshaping High-Performance Computing

Kubernetes with Naveen — Fri, 27 Mar 2026 14:09:42 +0000

A practical, human-centered deep dive into why HPC and Kubernetes are finally converging, what this means for DevOps and platform engineers, and how Kubernetes can modernize and streamline high-performance computing services.

Top Three Takeaways

HPC’s traditional operational model is unsustainable today; Kubernetes provides the automation and reproducibility it has always lacked.
Kubernetes doesn’t try to replace HPC schedulers—it simply brings modern engineering discipline around them.
When Kubernetes becomes the service layer for HPC, everything from provisioning to monitoring becomes more scalable, more observable, and dramatically easier to operate.

The Core Issues That Made Kubernetes + HPC Inevitable

For a long time, HPC clusters lived in a completely different world from modern cloud-native engineering. They were built with specialized schedulers, custom interconnects, handcrafted modules, and a fair amount of “tribal knowledge” shared among a small group of administrators. This approach was workable in the early 2000s when scientific teams operated within predictable boundaries, when library versions changed slowly, and when the majority of HPC workloads were tightly controlled.

But the industry changed. Research teams began adopting fast-moving software stacks. Machine learning workloads arrived with their complex GPU requirements. Data volumes exploded. The pace of innovation increased, and entirely new programming ecosystems began emerging and evolving monthly. HPC clusters, once built around the idea of stability and slow change, suddenly needed to host workloads whose world was anything but stable.

At the same time, operating an HPC cluster became increasingly complex. Installing or upgrading system-wide libraries involved carefully choreographed downtime windows. Keeping user environments consistent across nodes required manual scripting. Monitoring was scattered, and logs were often available only in fragments. Expanding a cluster meant provisioning bare-metal machines manually and wiring them into the scheduler by hand. It was predictable, but fragile. Powerful, but painfully slow.

This combination of pressure points—fast-moving user demands, slow-moving cluster operations, and the rise of containerized environments—created the perfect storm. Kubernetes didn’t “enter” the HPC world because it wanted to. HPC administrators pulled it in because they needed a better way to manage complexity.

A DevOps-Friendly Introduction to HPC

To a platform engineer, HPC is simply a massive, tightly controlled batch computing engine designed to squeeze every ounce of performance from hardware resources. Instead of microservices that run indefinitely, HPC runs large, resource-hungry jobs that often span multiple nodes, consume large parts of the cluster, and run for hours or days. MPI workloads, GPU-bound training pipelines, large graph computations, simulation models—these jobs rely on low-latency interconnects, specific CPU/GPU topologies, and predictable runtime behavior.

An HPC cluster is traditionally built around a scheduler such as Slurm, PBS, or LSF. The scheduler orchestrates who gets what resources, when, and for how long. It ensures fairness, utilization, and job prioritization. But the scheduler itself doesn’t solve day-to-day operational pain. It doesn’t provide a clean way to manage software environments or isolate workloads. It doesn’t automatically scale services. It doesn’t offer standardized deployment practices. It doesn’t unify monitoring. It certainly doesn’t integrate with CI/CD or modern DevOps workflows.

From a DevOps perspective, HPC is an incredibly powerful engine that has always lacked a modern platform layer. Kubernetes steps into this void, not to compete with the scheduler but to bring discipline, reproducibility, and automation to the environment around it.

How Kubernetes Transforms the HPC Service Layer

One of the most misunderstood ideas in this space is the belief that Kubernetes is here to replace traditional HPC schedulers. In reality, the opposite is true. Kubernetes is increasingly used to run the services that support the HPC ecosystem—not the HPC jobs themselves.

Consider the traditional HPC environment: login nodes, head nodes, cluster management tools, monitoring dashboards, exporters, databases, visualization servers, license managers, user environment services, job-submission portals, and storage orchestrators. Each of these components requires careful installation, versioning, security patches, and monitoring. Historically, all of this lived on dedicated machines managed manually or with fragile scripts.

Moving these services to Kubernetes changes the HPC experience in a profound way. Suddenly, operating an HPC cluster feels like operating a modern cloud platform. Services become declarative. Deployments can be upgraded without downtime. User-facing portals and job submission interfaces can be rolled out with CI/CD pipelines. GPU-aware container runtimes can enforce consistent environments. Logs and metrics flow naturally into centralized systems.

And perhaps the biggest shift—user environments finally become portable.

Researchers no longer need to rely on heavily curated system modules or beg administrators to install yet another Python build. Instead, they use container images, pushing environment reproducibility to the foreground. For HPC administrators, this is nothing short of a liberation. It reduces friction, it improves security, and it eliminates the long-standing “dependency chaos” that has haunted HPC for decades.

Management, Provisioning, and Scaling—All Reimagined

The true value of Kubernetes appears when you look at the broader operational lifecycle. Provisioning HPC services, once a manual activity involving configuration files and service restarts, becomes as simple as applying a GitOps change. Monitoring—long a patchwork of scripts, log collectors, and homegrown dashboards—becomes unified through Kubernetes-native observability stacks like Prometheus, Loki, and Grafana. Even integrating GPUs, historically a tedious process, becomes cleaner through device plugins and container runtimes optimized for HPC workloads.

Scaling is where Kubernetes makes the most visible difference. Adding more login nodes or monitoring components no longer means provisioning bare-metal machines. Kubernetes replicas, autoscalers, and cluster API-driven expansion allow HPC operators to scale non-compute services as usage grows. Even hybrid HPC—where bursts of high-demand jobs spill into cloud resources—becomes easier to orchestrate because Kubernetes already knows how to speak the language of multi-cluster and multi-provider environments.

None of this replaces the raw power of the scheduler. Instead, it complements it by giving HPC a modern, self-service platform layer that dramatically lightens the operational burden.

A More Modern and Sustainable HPC Future

The convergence of Kubernetes and HPC isn’t a trend—it’s a necessary transition. Scientific teams are moving faster, data is growing larger, and workloads are becoming more diverse than ever before. Without a platform layer capable of handling this complexity, HPC will stay locked in a cycle of manual intervention and operational fragility.

Kubernetes doesn’t solve every HPC problem, and it doesn’t try to. But it solves the problems that have historically slowed HPC down: inconsistent environments, slow provisioning, fragile monitoring, limited scalability, and the lack of modern automation practices.

When Kubernetes runs the service layer and HPC schedulers run the job layer, we finally get a cluster that is powerful enough for research and elegant enough for DevOps—a rare combination in the history of high-performance computing.

In this emerging world, HPC is still the engine. Kubernetes simply ensures that the engine is easier to operate, easier to observe, easier to extend, and ready for the next decade of scientific and computational innovation.

Kubernetes Autoscaling Myths: Why HPA Alone Won’t Fix Your Resource Problems

Kubernetes with Naveen — Mon, 16 Mar 2026 13:54:25 +0000

This is the multi-part blog series in the first part I covered up an operator’s view into the Kubernetes resource paradox. Learn why most clusters waste 40–60% of their capacity, how resource requests really work, and why overprovisioning is a rational response to fear — not incompetence. And in the second part I explained why Kubernetes resource overprovisioning happens, how it quietly inflates cloud costs, and what real-world strategies DevOps teams use to regain control over CPU, memory, and GPU usage.

Horizontal Pod Autoscaler is often treated as Kubernetes’ automatic scaling solution, but in reality it only works when requests, metrics, and workload behavior are understood. This deep dive explains why autoscaling frequently fails in production and how to design scaling strategies that actually work at scale.

By the time most teams adopt autoscaling in Kubernetes, they’ve already run into the limitations of static resource allocation. Traffic fluctuates, workloads behave unpredictably, and the idea of manually adjusting replica counts quickly becomes unrealistic. Autoscaling promises a cleaner solution: let the platform react dynamically to demand.

The Horizontal Pod Autoscaler (HPA) is often introduced as the answer to this problem. Configure a target CPU utilization, set minimum and maximum replicas, and Kubernetes will automatically adjust the number of pods as load changes.

On paper, it sounds like the perfect system.

In reality, autoscaling is one of the most misunderstood parts of Kubernetes. Many teams assume that once HPA is enabled, resource efficiency and scaling problems will take care of themselves. Instead, what often happens is the opposite: autoscaling amplifies bad assumptions about requests, workload behavior, and metrics. Clusters become harder to reason about, scaling events become unpredictable, and the root problems that caused overprovisioning in the first place remain untouched.

Autoscaling is powerful, but only when the underlying signals are trustworthy.

How Horizontal Pod Autoscaling Actually Works

The Horizontal Pod Autoscaler doesn’t measure “load” in the abstract. It calculates scaling decisions based on utilization relative to the container’s requested resources.

For CPU-based scaling, the formula is essentially:

Current Utilization = Actual CPU Usage / CPU Request

If the current utilization exceeds the target threshold, Kubernetes increases the number of replicas. If it falls below the threshold, replicas are reduced.

At first glance, this seems logical. But notice the dependency hidden in that equation: CPU requests are part of the calculation. If requests are inaccurate, the utilization signal becomes distorted.

Imagine a container that consistently uses around 500 millicores of CPU but has a request of 2000 millicores. The autoscaler will see utilization of only 25 percent, even if the application is under significant real-world load. Because the utilization appears low, scaling will not occur when it should.

In effect, the autoscaler becomes blind to demand.

This is why autoscaling often fails quietly in clusters where requests have been inflated as a safety buffer. The autoscaler is working correctly; it’s simply responding to incorrect inputs.

Why Autoscaling Often Makes Overprovisioning Worse

Once teams realize that autoscaling is not reacting quickly enough, they tend to compensate in ways that make the situation worse.

A common response is to increase baseline replica counts. Instead of running two or three pods and letting the autoscaler expand as needed, teams start with ten or fifteen replicas just to avoid scaling delays. While this improves perceived reliability, it eliminates much of the cost benefit autoscaling was meant to provide.

Another reaction is to inflate resource requests further. If scaling triggers depend on utilization percentages, increasing requests might seem like a way to create more headroom. In practice, this makes scaling signals even less accurate and pushes the cluster toward earlier node scale-outs.

Over time, the autoscaler becomes more of a safety mechanism than an efficiency tool. It prevents catastrophic overload but does little to improve resource usage.

Scaling Latency Is the Hidden Constraint

Even when requests are accurate and autoscaling signals are correct, scaling is not instantaneous.

Adding replicas involves several steps: the autoscaler must observe the metric change, compute a new replica count, update the deployment, schedule new pods, and wait for those pods to become ready. In clusters where nodes must also be provisioned by the cluster autoscaler, the delay can be even longer.

These delays are not bugs. They are fundamental properties of distributed systems.

The implication is that autoscaling works best when it responds to gradual changes in demand, not sudden traffic spikes. Workloads that experience abrupt surges often require a different strategy, such as maintaining a slightly higher baseline replica count or scaling based on predictive signals rather than purely reactive metrics.

Teams that assume autoscaling can instantly absorb any spike often discover the limits of that assumption during incidents.

Vertical Scaling: The Quiet Companion to Horizontal Autoscaling

While horizontal scaling adjusts replica counts, vertical scaling focuses on correcting resource requests themselves. This is where the Vertical Pod Autoscaler (VPA) enters the picture.

VPA analyzes historical resource usage and suggests more appropriate requests for CPU and memory. Instead of adding more pods, it attempts to right-size the pods that already exist.

In practice, VPA is most effective when used cautiously. Fully automated vertical scaling can lead to disruptive restarts, which is why many organizations run VPA in “recommendation mode.” In this configuration, the system provides insights about resource usage without automatically applying changes.

This mode turns VPA into something more valuable than automation: it becomes a feedback mechanism. Platform teams can see which workloads are dramatically over-requested and begin the process of gradual correction.

Horizontal scaling handles demand variability, while vertical scaling corrects historical misallocation. The two approaches are complementary, not interchangeable.

Autoscaling Works Only When Metrics Tell the Truth

The quality of autoscaling decisions ultimately depends on the metrics that feed the system.

CPU utilization is easy to measure, but it doesn’t always correlate with user-facing performance. Some applications are bottlenecked by I/O, external APIs, or internal queue depth rather than raw CPU consumption. In those cases, scaling based solely on CPU metrics may miss the signals that actually matter.

Advanced platforms often introduce application-level metrics into scaling decisions. Queue length, request latency, and throughput are frequently better indicators of load than CPU utilization alone. These signals allow scaling behavior to align more closely with real-world demand rather than infrastructure metrics.

However, this approach introduces complexity. Application metrics must be reliable, well-defined, and resistant to noise. Otherwise, autoscaling becomes unstable and oscillates between states.

The challenge is not gathering more metrics, but identifying the ones that genuinely reflect pressure on the system.

The Interaction Between Pod Autoscaling and Cluster Autoscaling

Another dimension of scaling complexity emerges when the Horizontal Pod Autoscaler interacts with the Cluster Autoscaler.

The cluster autoscaler is responsible for adding or removing nodes when pods cannot be scheduled due to insufficient capacity. This interaction creates a chain reaction. When HPA increases replica counts, the scheduler attempts to place those pods on existing nodes. If capacity is unavailable, the cluster autoscaler provisions new nodes.

This sequence introduces additional delay and sometimes surprising behavior. If resource requests are inflated, pods may appear unschedulable even when the node still has unused CPU and memory in reality. The cluster autoscaler then adds nodes unnecessarily, increasing infrastructure costs.

In this sense, inaccurate requests don’t just affect pod scheduling; they propagate all the way up to cluster-level infrastructure decisions.

Autoscaling Is a Feedback System, Not a Magic Switch

Autoscaling systems behave more like control loops than simple triggers. They observe signals, make adjustments, and then observe the effects of those adjustments over time.

Like any feedback system, stability depends on signal quality, response timing, and predictable behavior from the workloads involved. When any of those elements are unreliable, scaling becomes erratic.

Understanding autoscaling in this way helps explain why tuning parameters such as scaling thresholds, cooldown periods, and replica limits can have dramatic effects. These settings control how aggressively the system reacts to perceived changes in demand.

Organizations that operate large Kubernetes environments eventually learn that autoscaling is not something you “enable and forget.” It is an ongoing operational discipline that requires observation, adjustment, and occasionally restraint.

When Autoscaling Actually Works Well

Autoscaling tends to perform best when a few key conditions are met. Resource requests closely match typical usage, ensuring utilization metrics reflect real pressure. Workloads scale horizontally without complex state dependencies. Traffic patterns change gradually enough for scaling decisions to keep up.

When those conditions hold, the system begins to behave predictably. Scaling events become routine rather than surprising, infrastructure usage becomes more efficient, and operational stress decreases.

Ironically, autoscaling becomes almost invisible at that point. It simply does its job in the background.

Closing Thoughts

Autoscaling is often portrayed as Kubernetes’ built-in solution for dynamic workloads. In practice, it is only as effective as the signals and assumptions that feed into it. Inflated resource requests, poorly chosen metrics, and unrealistic expectations about scaling speed can all undermine the system.

The Horizontal Pod Autoscaler is not a replacement for thoughtful resource configuration. Instead, it builds on top of it. When requests reflect reality and metrics reflect meaningful pressure on the system, autoscaling becomes an incredibly powerful tool.

But without those foundations, it simply amplifies existing problems.

In the next part of this series, we’ll explore a domain where these problems become dramatically more expensive: GPU workloads in Kubernetes, where idle capacity can burn thousands of dollars per day.

Key Takeaways

Horizontal Pod Autoscaling depends on resource requests, so inflated requests distort scaling signals and prevent correct scaling behavior.
Vertical scaling complements horizontal scaling by correcting long-term resource misallocation and improving autoscaling accuracy.
Autoscaling is a feedback system, not a one-click feature, and its effectiveness depends on accurate metrics, realistic expectations, and careful tuning.

So, what coming next?

GPU workloads magnify every resource management mistake. This deep dive shows how idle accelerators quietly burn budgets and why traditional Kubernetes patterns don’t work for AI workloads.

Goodbye Ingress, Goodbye Sidecars: The Real Playbook for Moving to Kubernetes Gateway API

Kubernetes with Naveen — Thu, 26 Feb 2026 09:03:45 +0000

The Kubernetes networking stack has always lived with a strange tension. The earliest generations of ingress controllers were never designed for the scale, complexity, or multi-AZ traffic patterns we deal with today. And when service meshes arrived—Envoy sidecars everywhere, per-pod proxies, complex CRDs—the industry gained powerful features but paid for them with operational sweat, extra costs, and more moving parts than anyone really wanted to admit.

Over time, teams started noticing the same problems repeat themselves: sidecars consuming more CPU than the actual business logic, cross-zone hops making latency unpredictable, complicated upgrades that broke at the worst possible moments, and observability pipelines that ballooned until simply scraping metrics became a project of its own. Add multi-cluster networking and AI workloads to the mix, and suddenly everything felt held together with duct tape.

The dissatisfaction wasn’t theoretical. It was emotional. People were tired. And that’s exactly where the shift toward Gateway API and sidecar-less mesh architectures began.

The Shift: A Better Model for How Traffic Should Really Flow

Gateway API wasn’tcreated to be another “Kubernetes thing to learn.” It exists because the community finally admitted that the old model was backward. For years, the idea was to push proxies into every pod and let a mesh handle the magic. But the result was an explosion of complexity—more configuration, more containers, more logs, more surprise outages.

Gateway API flips that thinking. Instead of embedding the data plane in every workload, it elevates traffic control to dedicated, intentional components. Policies become cleaner. Routing becomes programmable. And meshes can finally operate at the node or zone level, not inside your app’s namespace like an uninvited roommate.

With this shift comes the real question: can teams actually migrate from legacy ingress + sidecars to Gateway API and a sidecar-less mesh without downtime, without breaking workloads, and without sacrificing authentication, observability, or resilience?

Surprisingly, the answer is yes—if you approach it the right way.

Zero-Downtime Migration Is Not a Dream

The safest way to make the migration is to treat it as a progressive traffic shift, not a platform rebuild. You don’t uninstall anything on day one. You don’t rip out sidecars. You don’t turn off the ingress controller at midnight and pray.

You start by running Gateway API right next to your existing setup. At this stage, it’s invisible to users. You let it mirror traffic, capture logs, enforce policies quietly, and behave like a backstage understudy. Once you’re confident it sees the world the same way your ingress+mesh stack does, you start shifting traffic a small percentage at a time. A few requests here, a handful there. Today’s tools make it safe—weight-based routing, controlled rollouts, and full rollback paths exist specifically for this moment.

When traffic finally reaches 100% on the Gateway side, the sidecars are no longer doing meaningful work. They can be removed gracefully, one deployment at a time, without causing downtime or disrupting pods. It’s a slow, thoughtful transition rather than the chaotic “big switch-over” that haunts most platform teams.

Locality Finally Becomes a First-Class Citizen

One of the biggest weaknesses of the old sidecar model is that traffic locality was never a true priority. Packets crossed zones freely, often without any awareness of where they were going. That meant higher cloud bills, unpredictable tail latency, and a constant sense that workloads were fighting the network instead of working with it.

Gateway API and modern sidecar-less meshes treat locality as something fundamental. Routing rules can prefer endpoints in the same AZ. Failover becomes smarter and more intentional. AI inference pods—where every millisecond matters—can finally stay within their own zone unless something genuinely fails. Costs drop. User experience improves. And most importantly, the architecture behaves the way you always wished it would.

Observability Doesn’t Disappear—It Actually Gets Better

A lot of engineers hesitate when they realize sidecars are going away. For years, sidecars provided detailed HTTP metrics, latency histograms, tracing spans, and every signal that modern autoscaling systems consume. But one of the best-kept truths of the new model is that you don’t lose any of this.

The observability simply moves upward, closer to the actual gateways or node-level proxies. You still get request-based metrics, per-URL latency, error ratios, and meaningful histograms. And once these metrics feed into systems like Prometheus → KEDA, autoscaling becomes far smarter than the old CPU-based HPA approach. You can scale based on concurrency, queue depth, or p95 latency. You can scale AI workloads when prompt traffic rises instead of waiting for GPU utilization to spike.

The signals become richer. The decisions become cleaner. And your workloads breathe easier.

Authentication and JWT Validation Stay Exactly Where You Need Them

One fear teams often raise during this migration is: what about security? What happens to JWT validation, request authentication, and mTLS? Nothing breaks. Nothing gets lost.

Modern gateways validate JWTs directly at the edge. Meshes enforce mTLS automatically. Policies become centralized rather than spread across sidecar configs. And if anything, security becomes simpler because fewer components have to stay in sync across deployments.

Authentication at the gateway level, combined with a sidecar-less mesh for east-west encryption, ends up being both cleaner and harder to break accidentally.

Why This Matters Even More for AI and LLM Workloads

AI workloads come with their own unique pains: queue spikes, unpredictable throughput, heavy GPU utilization, and cross-zone traffic that can destroy latency. Legacy meshes weren’t built for this world. They didn’t understand queuing semantics or model warmup behaviors. They treated everything like a microservice, which AI workloads simply aren’t.

Gateway API allows smarter shaping of request flows. You can throttle bursts, smooth out spikes, direct traffic toward specific zones based on GPU availability, and apply circuit breaking that avoids expensive retries on large prompts. Combined with richer metrics and locality-aware routing, AI systems become more stable under pressure.

This is one of those rare moments when new Kubernetes features don’t just simplify things—they solve problems you couldn’t reasonably solve any other way.

Key Takeaways

First: migrating from legacy ingress and sidecar-heavy meshes to Gateway API and a sidecar-less architecture is absolutely possible without downtime, as long as you approach it progressively and transparently.

Second: you don’t lose the features you care about—request metrics, JWT auth, mTLS, advanced routing, and observability all remain intact, often in a cleaner form.

Third: this model aligns better with the future, especially for multi-AZ platforms and AI workloads where latency, cost, and traffic control matter far more than they did in early Kubernetes days.

Kubernetes Requests and Limits: The Most Misunderstood Feature in Production

Kubernetes with Naveen — Thu, 12 Feb 2026 12:02:50 +0000

In the last post i explained why Kubernetes resource overprovisioning happens, how it quietly inflates cloud costs, and what real-world strategies DevOps teams use to regain control over CPU, memory, and GPU usage and you can read that right here.

Kubernetes requests and limits look simple, but in production they quietly dictate cost, stability, and scalability. This deep dive explains how they really work, why most teams get them wrong, and how to configure them without risking outages.

If you ask most engineers what Kubernetes requests and limits do, you’ll get a confident answer within seconds. Requests are what the container needs. Limits are the maximum it can use. Simple.

And that’s exactly why this feature causes so much damage in production.

Requests and limits are one of the earliest concepts people learn in Kubernetes, but they’re also one of the least revisited. Teams copy values from old services, cargo-cult them across repositories, and rarely question whether they still reflect reality. Over time, these numbers quietly shape scheduling behavior, autoscaling decisions, node count, and ultimately cloud spend — often without anyone realizing it.

To understand why this goes wrong at scale, you have to stop thinking of requests and limits as “resource settings” and start seeing them for what they cyually are: contracts with the scheduler.

Requests Are Reservations, Not Estimates

The most important thing to internalize is this: when a pod specifies resource requests, Kubernetes treats them as guaranteed reservations.

If a container requests 1 CPU and 4 GiB of memory, the scheduler will only place it on a node that has at least that much allocatable capacity available. From that point on, that capacity is considered consumed, whether the container uses it or not.

It doesn’t matter if the application idles for hours.
It doesn’t matter if average usage is a fraction of the request.
As far as the scheduler is concerned, that resource is gone.

This is why clusters end up in the strange state where they can’t schedule new pods even though node-level metrics show plenty of unused CPU and memory. The scheduler is doing exactly what it was told to do — it’s just working with inflated numbers.

Why Engineers Inflate Requests (And Why It’s Rational)

Over-requesting resources isn’t a sign of poor engineering discipline. It’s a rational response to uncertainty.

Most teams have lived through at least one painful incident where a container was under-provisioned. Maybe a memory spike triggered an OOM kill during peak traffic. Maybe CPU throttling caused latency to creep up just enough to trip timeouts. Those incidents stick.

After that, the thought process changes. Engineers stop asking, “What does this service usually need?” and start asking, “What’s the worst case I’ve ever seen?”

Requests grow to cover edge cases. Limits are pushed far beyond normal operation or removed entirely. Over time, this becomes the default posture, especially for services that are considered critical. Nobody wants to be the person who reduced a request and caused the next outage.

The problem is that Kubernetes has no native way to tell you when that fear is outdated. A service that once needed 8 GiB of memory during a launch might now be stable at 2 GiB — but the request never gets revisited. Multiply that across hundreds of workloads, and the waste compounds quietly.

Limits Are Not a Safety Net (Especially for Memory)

Limits are often described as a “safety boundary,” but that description glosses over some important realities.

CPU limits are enforced through throttling. When a container hits its CPU limit, it doesn’t crash — it just gets slowed down. This can be acceptable for some workloads and disastrous for others, depending on latency sensitivity.

Memory limits are far less forgiving. When a container exceeds its memory limit, it is immediately terminated by the kernel. There’s no graceful degradation. No backpressure. Just a hard stop.

Because of this, many teams choose one of two extremes: either they set memory limits extremely high, or they avoid setting them altogether. Both approaches come with trade-offs. High limits reduce the chance of OOM kills but increase the blast radius if something leaks memory. No limits improve stability for individual pods but shift risk to the node and, by extension, other workloads.

What’s often missing from this decision is an understanding of actual memory usage over time. Without that context, limits become guesswork — and guesswork tends to err on the side of excess.

The Hidden Relationship Between Requests and Autoscaling

Autoscaling is frequently used as a justification for sloppy requests. The logic goes something like this: “We have HPA, so it’ll scale if things get busy.”

What’s overlooked is that horizontal autoscaling relies on requests to calculate utilization. If your CPU request is wildly inflated, your utilization percentage will look low even under real load. The autoscaler won’t trigger when it should, because from its perspective, nothing is wrong.

In this way, over-requesting doesn’t just waste capacity — it actively breaks scaling behavior. Teams then respond by increasing replica counts manually or inflating requests even further, reinforcing the cycle.

Autoscaling works best when requests reflect baseline usage, not peak fear. Without that honesty, the system amplifies bad assumptions instead of correcting them.

A More Honest Way to Configure Requests and Limits

In mature environments, requests are treated as a representation of typical behavior, not worst-case scenarios. They’re based on observed usage over time, not a single incident from six months ago.

Limits, when used, are chosen deliberately based on failure tolerance. For CPU, that might mean allowing bursts while preventing a single pod from monopolizing a core. For memory, it often means accepting that some workloads are better protected by node-level isolation than aggressive per-container limits.

This approach requires trust — not blind trust, but trust built on metrics, slow change, and fast rollback. Teams that succeed with right-sizing don’t aim for perfection. They aim for plausibility.

Why This Misunderstanding Gets More Expensive at Scale

In small clusters, over-requesting mostly results in inefficiency. In large fleets, it reshapes the entire platform.

Inflated requests reduce bin-packing efficiency, which increases node count. Higher node count increases failure domains, upgrade complexity, and operational overhead. Autoscalers react to distorted signals. Scheduling latency increases. GPU pools grow faster than they need to.

At that point, requests and limits are no longer just a configuration detail. They are a major architectural input.

This is why organizations that treat resource configuration as a first-class concern often see dramatic improvements without changing application code at all. They stop feeding the scheduler exaggerated inputs, and the system immediately behaves better.

Closing Thoughts

Requests and limits are simple on the surface, which is exactly why they’re dangerous when misunderstood. They don’t just affect individual pods — they influence how Kubernetes perceives the entire cluster.

When requests are inflated, Kubernetes is forced to plan for a world that doesn’t exist. When limits are misunderstood, teams either accept unnecessary risk or waste massive amounts of capacity trying to avoid it.

Getting this right isn’t about squeezing every last CPU cycle. It’s about giving the scheduler truthful information and letting it do its job. Once that happens, autoscaling becomes predictable, clusters become calmer, and cost optimization stops feeling like a fight.

In the next part of this series, we’ll dig into autoscaling itself — why HPA alone won’t save you, and how bad inputs can turn scaling from a solution into a multiplier of waste.

Key Takeaways

Requests are scheduling contracts, not usage estimates, and inflating them directly leads to wasted capacity.
Limits behave very differently for CPU and memory, and misunderstanding that difference causes both outages and inefficiency.
Autoscaling depends on honest requests, and overprovisioning silently breaks its assumptions.

So What's Next?

In my next blog post, I will cover Kubernetes autoscaling, which is often used to mask bad resource configurations. Learn how horizontal and vertical scaling actually work together — and how to avoid autoscalers amplifying bad inputs. Till then, have fun in reading, help me to share this post to your dear ones for wider outreach.

Kubernetes Resource Management at Scale: Why Your Clusters Are Full, Idle, and Still Starving for Resources

Kubernetes with Naveen — Sat, 31 Jan 2026 11:03:39 +0000

Running Kubernetes at scale often means paying for capacity you don’t use while teams still complain about resource shortages. This deep dive explains why Kubernetes resource overprovisioning happens, how it quietly inflates cloud costs, and what real-world strategies DevOps teams use to regain control over CPU, memory, and GPU usage.

If you’ve been running Kubernetes at scale for a while, this situation will sound painfully familiar. Your clusters appear to be at capacity, your cloud bills keep climbing month after month, and yet when you look closely, a large percentage of CPU and memory is just sitting there unused. Despite that, application teams keep asking for more resources, and any attempt to right-size workloads is met with resistance. Everyone is afraid that the smallest reduction might be the one that brings production down.

This is the reality of Kubernetes resource management in the real world. You’re not dealing with a lack of tooling or incompetent teams. You’re dealing with a system that makes it very easy to reserve far more than you need and very hard to feel safe giving anything back. The result is widespread overprovisioning, often to the tune of forty to sixty percent wasted capacity. In environments running GPU-heavy AI and machine learning workloads, the waste can be even more extreme, with extremely expensive accelerators sitting idle for long stretches of time.

At the heart of the problem is how Kubernetes treats resource requests. Requests are not estimates or guidelines. They are hard reservations. When a pod asks for a certain amount of CPU and memory, the scheduler assumes that capacity must be available at all times, even if the application only uses a fraction of it during normal operation. Across hundreds or thousands of pods, this behavior leads to clusters that are full from the scheduler’s point of view while the underlying nodes are doing surprisingly little work.

Engineers don’t over-request resources because they’re careless. They do it because they’ve been burned before. Almost every team has a story about a pod getting OOM-killed during a traffic spike or a service being throttled at the worst possible moment. Once that happens, the natural response is to add more headroom and never touch it again. Over time, this defensive behavior turns into a pattern where requests are padded just in case, limits are set unreasonably high or removed altogether, and nobody wants to be responsible for tightening things and causing the next incident.

Kubernetes also does very little to help you correct this behavior. While it exposes plenty of metrics, it offers almost no guidance on what is safe to change. You can see CPU and memory usage graphs all day long, but they don’t answer the questions operators actually care about. Which requests are clearly outdated? Which workloads have never come close to their allocated resources? What is the real risk of lowering a particular request? Without a clear feedback loop, most teams choose to do nothing, because doing nothing feels safer than making a change that could backfire.

When GPUs enter the picture, these inefficiencies become dramatically more expensive. Unlike CPU and memory, GPUs are typically allocated exclusively. A single pod can reserve an entire accelerator even if it only uses it intermittently. In many machine learning platforms, GPUs sit idle between training steps, wait on I/O, or remain allocated long after a batch job has effectively finished its work. Each of those idle periods translates directly into money burned, often hundreds of dollars per day per GPU. Because GPU failures are slow to debug and expensive to repeat, teams are especially reluctant to experiment with tighter sizing or sharing models.

The financial cost is only part of the damage. Overprovisioned clusters create artificial pressure to scale. Nodes are added earlier than necessary, autoscalers react to inflated demand signals, and GPU pools grow far beyond what sustained workloads actually require. Scheduling becomes less efficient as large requests fragment available capacity, leading to longer pod startup times and the false impression that Kubernetes itself is struggling to keep up. On top of that, resource discussions turn political. Platform teams push for efficiency, application teams push for safety, and without shared data, neither side fully trusts the other.

Solving these problems requires more than turning on a single feature or installing another dashboard. One of the most important mindset shifts is separating safety from scheduling. Requests should represent realistic baseline usage, not worst-case scenarios. Limits and autoscaling mechanisms exist to handle spikes and protect the system. When requests are inflated to cover every possible edge case, the scheduler is fed bad information, and the entire cluster suffers as a result.

Right-sizing also has to be approached gradually. Aggressive, large-scale reductions almost always lead to incidents and erode trust. Teams that succeed treat right-sizing as an ongoing, incremental process. They make small adjustments, observe real production behavior, and roll back quickly if something looks wrong. The goal isn’t perfect utilization; it’s steady improvement without destabilizing the platform.

Autoscaling plays a critical role here, but only when used thoughtfully. Horizontal scaling helps absorb traffic variability, while vertical adjustments correct historical over-allocation. Vertical recommendations are most effective when they start in advisory mode, are reviewed by humans, and are enforced first on lower-risk workloads. This builds confidence and avoids the perception that the platform team is making dangerous, opaque changes.

GPU clusters demand even more discipline. Treating GPUs as a shared, scarce pool rather than one-per-pod by default can unlock massive savings. That often means embracing batch scheduling, job queues, tighter lifecycle management, and more aggressive release of resources when work is done. Idle GPUs are silent budget killers, and the only way to control them is to make their usage and cost impossible to ignore.

Cost visibility is ultimately what ties all of this together. When teams can clearly see the cost of their namespaces, services, or training jobs, resource conversations change. Right-sizing stops being an abstract efficiency exercise and becomes a concrete business decision. The most successful Kubernetes cost optimization efforts are driven as much by culture and transparency as they are by technical mechanisms.

In mature Kubernetes environments, resource management fades into the background. Requests roughly align with typical usage, autoscalers handle spikes gracefully, GPUs are scheduled intentionally, and engineers trust data more than fear. Most importantly, resource discussions become boring — and boring is exactly what you want in a system that runs critical workloads at scale.

Kubernetes itself isn’t inherently wasteful. The waste comes from how we configure and operate it under uncertainty. Overprovisioning is a rational response to missing feedback and high perceived risk. Fixing it requires better signals, safer ways to experiment, and shared ownership across platform and application teams. You don’t need perfect efficiency. You need predictable behavior, controlled risk, and honest inputs to the scheduler.

Key Takeaways

Kubernetes resource requests are hard reservations, and treating them as safety buffers is the root cause of large-scale waste.
Effective right-sizing is incremental and trust-based, not aggressive or automated without human oversight.
GPU overprovisioning is the fastest way to destroy cloud budgets, and it must be addressed with intentional sharing and scheduling strategies.

So What's Next?

I will come up with next part to explain how does the requests and limits looks simple, but in production they quietly shape cluster cost, reliability, and scaling behavior. This post breaks down what they really mean and how to set them honestly. Till then have fun in reading, help me to share this post to your dear ones for wider outreach.

From Logs to Insights: How to Adopt OpenTelemetry Collectors Without Breaking Your Existing Infrastructure

Kubernetes with Naveen — Wed, 21 Jan 2026 09:05:40 +0000

OpenTelemetry Collectors are quickly becoming the backbone of modern observability. But ripping and replacing your existing logging stack is rarely an option. This guide walks you through a gradual, low-risk approach to adopting OpenTelemetry Collectors in your infrastructure—so you can modernize logging without disrupting what already works.

Why OpenTelemetry Collectors Matter

If you’ve ever worked with logs at scale, you know the story: too many agents, too many formats, too many pipelines, and way too much duct tape. Every new service you spin up comes with another log forwarder or sidecar, and soon enough you’re drowning in a sea of agents, configuration files, and data silos.

Enter OpenTelemetry Collectors. They’re designed to unify your observability data—logs, metrics, traces—into a single, flexible pipeline. Instead of juggling multiple agents, you can deploy one collector that receives, processes, and exports telemetry to the systems you care about (Splunk, Elasticsearch, Loki, Datadog, you name it).

The magic lies in its pluggable architecture: receivers pull in data, processors enrich or transform it, and exporters send it wherever it needs to go. That means less complexity, more consistency, and fewer moving parts.

But here’s the catch: you probably already have a logging setup. Ripping everything out in one go is risky, expensive, and impractical. So how do you modernize without disrupting your current workflows? The answer: adopt OpenTelemetry Collectors gradually.

Step 1: Map Your Current Logging Landscape

Before you deploy anything new, get clear on what you already have.

Which log agents are you running? (Fluentd, Filebeat, Vector, custom shippers?)
Where are the logs stored or analyzed? (Elasticsearch, Loki, Splunk, S3 buckets?)
How do logs flow today? (From apps → agents → storage → dashboards?)
What’s working well, and what’s painful? (Cost? Latency? Reliability?)

This isn’t busywork—it’s your baseline. Knowing your current pipelines helps you identify where OpenTelemetry fits in without causing friction.

Step 2: Start in "Sidecar" Mode (No Disruptions)

The safest way to introduce OpenTelemetry is to start small, in parallel with your existing setup.

Deploy the OpenTelemetry Collector in sidecar mode or as a daemonset (if you’re in Kubernetes).
Configure it to receive a copy of your logs from your current agent.
Export those logs to a test backend (could be a staging Elasticsearch, or even stdout for validation).

At this point, nothing in production has changed—you’re just “teeing off” logs to OTel so you can test the waters.

Why this works: You avoid the risky “big bang” migration. Developers, SREs, and security teams still get the logs they expect while you experiment in the background.

Step 3: Use Processors to Add Value

This is where OpenTelemetry begins to shine. With processors, you can:

Normalize log formats (say goodbye to inconsistent JSON vs plain text nightmares).
Add metadata like Kubernetes pod labels, cloud region, or service name.
Drop noise—filter out health checks or debug logs that nobody reads.
Batch and compress logs before sending them to cut costs.

The key insight: even while running in parallel, you can demonstrate quick wins that existing tools couldn’t provide easily. That makes it easier to get buy-in from stakeholders for the full migration.

Step 4: Migrate Exporters Gradually

Once you’re confident, start moving workloads over step by step:

Pick one service or environment (e.g., staging) and route its logs directly through OpenTelemetry.
Export them to your existing backend (say Elasticsearch).
Validate that nothing breaks—dashboards still work, alerts still fire, developers still debug effectively.

Rinse and repeat, service by service, environment by environment. Over time, you can decommission legacy agents like Fluentd or Filebeat as OTel fully takes over.

This phased rollout gives you control and safety. No scary “flip the switch” moment—just steady, reliable progress.

Step 5: Expand Into Metrics and Traces (Optional, but Powerful)

While you’re modernizing logs, don’t forget that the OpenTelemetry Collector is not just about logs. It’s a multi-signal pipeline.

Add receivers for metrics (Prometheus scrape, host metrics, etc.).
Enable tracing pipelines (Jaeger, Zipkin, or OTLP directly).
Correlate logs, metrics, and traces for true observability instead of three disconnected silos.

This is where the real payoff kicks in. Suddenly, that error log isn’t just a line in Elasticsearch—it’s tied to a trace showing the exact request flow and metrics proving the impact.

Step 6: Optimize for Scale and Cost

Once you’re comfortable, scale the architecture:

Centralize collectors (agent + gateway pattern) for large clusters.
Introduce sampling for high-volume logs to save costs.
Leverage load balancing exporters for HA and resilience.
Send multiple exports (to your SIEM and to S3 for long-term retention).

At this stage, you’ve fully transitioned to a future-proof observability pipeline—without the chaos of a hard cutover.

Key Takeaways

OpenTelemetry Collectors unify and simplify logging pipelines by consolidating agents, formats, and exporters.
You don’t need to rip and replace—adopt them gradually alongside your existing setup.
Start small: run collectors in parallel, demonstrate quick wins, then phase out old agents.
Use processors for filtering, enrichment, and cost optimization.
Once stable, expand to metrics and traces for full-spectrum observability.

Closing Thoughts

Modernizing logging isn’t about flashy new tools—it’s about building a pipeline that scales with your business without breaking what you already have. OpenTelemetry Collectors give you the flexibility to move at your own pace, proving value along the way.

If you’ve ever felt stuck between clunky legacy agents and the promise of modern observability, this gradual approach might just be the bridge you need.

From Stateless to Stateful Royalty: How Kubernetes Conquered the Database Realm

Kubernetes with Naveen — Fri, 02 Jan 2026 11:38:18 +0000

Forget everything you thought you knew about Kubernetes and databases. The era of treating stateful apps as second-class citizens is over. We're diving into how a platform built for the ephemeral learned to embrace the permanent, and why your database's next home might just be a pod.

Remember the early days of Kubernetes? It was a wild west of microservices, a glorious mosh pit of stateless containers that could be spun up, scaled down, or blown away without a second thought. It was agile, it was powerful, and it was... terrified of databases.

To even whisper "PostgreSQL" or "Kafka" in a K8s cluster back then was to invite a chorus of seasoned engineers to clutch their pearls. "It's not safe!" "It's not natural!" "Databases are precious pets, not disposable cattle!" And they were right. Kubernetes was born in the stateless image, and trying to force a stateful, persistent database into its ephemeral world felt like trying to house a wise, old dragon in a tent made of tissue paper. It was a disaster waiting to happen.

But oh, how the times have changed.

What we’re witnessing today isn’t just an incremental improvement; it’s a full-blown paradigm shift. Kubernetes has undergone a profound evolution, growing the necessary muscles and tools to not only host stateful workloads but to manage them with a level of automation and resilience that was once the sole domain of bespoke, hand-crafted infrastructure. The dragon hasn't just been tamed; it's been knighted and put in charge of the kingdom.

The Bad Old Days: Why Databases Were the Square Peg

Let's be real: the initial friction was justified. A traditional database has three core needs that early K8s struggled with:

1. Identity: A database instance isn't just a random number. It needs a stable, predictable identity (like postgres-0, postgres-1). In the early ReplicaSet model, pods were interchangeable, anonymous cogs.

2. Storage: This is the big one. Data must persist forever (or at least until you mess up a DROP TABLE command). Container storage is, by nature, ephemeral. Lose a pod, lose your data. Game over.

3. Ordered Orchestration: You can't just roll out an update to a database cluster all at once. You need a careful, ordered process—often involving primary election, backups, and state checks. The "cattle, not pets" mantra broke down here; these were very important pets that needed individual care.

The Triumphant Trio: The Tools That Changed Everything

Kubernetes didn't just get a minor patch; it acquired a stateful mindset. This transformation was powered by a few killer features that moved from "experimental" to "rock-solid."

1. StatefulSets: The Gift of Identity and Order

Enter StatefulSet. This wasn't just another controller; it was a declaration that stateful applications matter. It gives each pod a unique, stable identity that persists across reschedules. mysql-0 will always be mysql-0. This stable identity is the bedrock upon which everything else is built.

But it goes further. StatefulSets understand sequence. When you scale up, it creates pod-1, then pod-2, waiting for each to be healthy before proceeding. When you roll out an update, it does so in reverse order, gracefully terminating the last pod first to maintain quorum. This isn't cattle herding; it's a meticulously choreographed ballet for your data.

2. Persistent Volumes: The Promise of Permanence

This is the magic that defeats ephemeral storage. The PersistentVolume (PV) and PersistentVolumeClaim (PVC) system decouples storage from the pod's lifecycle. You declare, "I need 100 GB of fast SSD storage," and Kubernetes dynamically provisions it from your cloud provider (or on-prem array).

When a pod in a StatefulSet dies and is resurrected, it simply reclaims the exact same piece of storage. The data is right where it left it. This transforms your database from a temporary resident into a permanent citizen of the cluster with its own immutable piece of real estate.

3. Operators: The Rise of Robotic DBAs

This is the secret sauce, the element that elevates the setup from "possible" to "profoundly excellent." Operators are Kubernetes-native applications that encode human operational knowledge into software.

Think of an Operator (like the excellent ones from Zalando for PostgreSQL, or the etcd Operator) as a robotic, hyper-vigilant DBA that lives inside your cluster. It doesn't just manage the pods; it manages the entire database lifecycle.

What does this look like in practice?

· Automated Backups & Recovery: The Operator can seamlessly stream backups to object storage and perform point-in-time recoveries with a simple YAML configuration change.
· Zero-Downtime Upgrades: It can orchestrate a rolling update of the database engine itself, one pod at a time, ensuring high availability throughout.
· Dynamic Scaling: Need to add a read replica? The Operator can spin it up, clone the data, and add it to the pool automatically.
· Self-Healing: If it detects a primary node failure, it can automatically fail over to a replica, minimizing downtime.

The Operator pattern is the final piece of the puzzle, injecting the crucial "ops" knowledge into the "Dev" platform, creating a truly self-driving database management system.

So, Why Should You Care? What's the Radical Outcome?

Moving your stateful workloads to a mature Kubernetes platform isn't just a technical flex; it's a strategic advantage.

· Unified Operational Model: Your team now has one platform, one set of tools (kubectl, Helm, ArgoCD), and one paradigm for managing everything. The cognitive load plummets.
· Declarative Everything: Your entire database setup—the version, the configuration, the backup policy, the resource limits—is defined in a Git repository. It's version-controlled, auditable, and reproducible. This is GitOps for your most critical data.
· True Elastic Scalability: The same horizontal pod autoscaler that scales your web app can now work in concert with your database layer. While the scaling might be more nuanced, the framework is there, powered by your StatefulSets and Operators.
· Cloud Agnosticism: Your database management logic, defined in YAML and powered by Operators, becomes portable. It can run on AWS, GCP, Azure, or on-prem, reducing vendor lock-in.

The New Truth

The old warning, "Don't run databases on Kubernetes," is now obsolete. It has been replaced with a more nuanced, powerful truth: "Don't run databases on an immature Kubernetes cluster."

The tools are here. They are battle-tested, widely adopted, and incredibly powerful. The platform has grown up. It's no longer just a stateless playground; it's a full-stack application platform ready to host the crown jewels of your business with confidence and grace.

The question is no longer if you should run stateful workloads on Kubernetes, but how quickly you can master the tools to do it right. The realm of stateful royalty is open for business. It's time to claim your throne.

Key Takeaways

· The Paradigm Has Shifted: Kubernetes is no longer just for stateless apps. With core features like StatefulSets and Persistent Volumes, it's now a robust and credible platform for stateful workloads like databases.
· Operators are Game-Changers: They automate complex database operations (backups, failovers, updates) by encoding human SRE knowledge into software, reducing toil and human error.
· Consistency is King: Running everything on K8s provides a unified operational model, simplifying tooling, processes, and cognitive load for development and platform teams.
· It's About Strategy, Not Just Technology: Adopting this approach enables a declarative, GitOps-driven workflow for your most critical data, leading to more reproducible, resilient, and scalable systems.
· The Risk is in the Implementation, Not the Concept: The initial risks of running databases on K8s have been mitigated by mature tools and patterns. The challenge now is learning and applying them correctly.

DEV Community: Kubernetes with Naveen

Kubernetes GPU Scheduling Patterns for AI Workloads at Scale

From Waste to Design: Where We’re Picking Up

The First Mental Shift: You’re Not Scheduling Pods — You’re Scheduling Work

Queue-Based Scheduling: The Backbone of Efficient GPU Platforms

Throughput vs Latency: The Trade-Off Most Teams Ignore

GPU Packing: Breaking the “One Pod = One GPU” Model

Job Lifecycle Discipline: Where Most Savings Come From

Scheduling Policies: Turning Infrastructure into a Platform

Why Kubernetes Alone Is Not Enough

The Most Important Metric: GPU Busy Time

What a Mature GPU Platform Looks Like

Closing Thoughts

Key Takeaways

So, what coming next?

From Campus to Big Tech: The Unfiltered, Deep-Dive Playbook for Indian CS Students to Crack FAANG+ (2026 Edition)

I Didn’t Just Research This — I Went Straight to the Source

Step 1: Stop Dreaming Vaguely — Start Targeting Precisely

Step 2: Understand How Big Tech Actually Hires

Step 3: Build the Only Skill That Truly Matters — Problem Solving

Step 4: Projects Matter—But Only If They Show Depth

Step 5: Resume — The Brutal Truth Recruiters Won’t Sugarcoat

Step 6: How to Actually Get Interview Calls

Step 7: Interview Preparation — What Really Happens Inside

Step 8: System Design — The Early Differentiator

Step 9: Soft Skills — The Silent Deal Breaker

Step 10: AI Skills — The 2026 Game Changer

Step 11: The Timeline That Actually Works

What Nobody Tells You (But You Must Accept)

Final Words: This Is a Discipline Game, Not a Talent Game

The Platform Beneath the Platform: Building an Internal Developer Platform That Actually Works

Introduction: The Problem We Pretend Doesn’t Exist

What Is Platform Engineering (Really)?

The IDP Is Not a Tool—It’s an Experience

The Core Building Blocks (And Why They Matter Together)

1. Kubernetes: The Substrate, Not the Solution

2. GitOps: The Backbone of Consistency

3. Observability: Your Platform’s Nervous System

4. Developer Self-Service: The End Goal

The Missing Layer: Abstractions That Make It Usable

Golden Paths: The Secret Sauce Nobody Talks About Enough

Standardization Without Killing Innovation

What Most Teams Get Wrong

How to Actually Build an IDP (Practical Approach)

The Cultural Shift (This Is the Hard Part)

Key Takeaways

Closing Thoughts: The Platform You Don’t Notice

Why GPU Clusters Bleed Money in Kubernetes (and How to Stop It)

*GPUs Change the Economics Completely. *

The Default GPU Model Is Fundamentally Wasteful

The Biggest Lie in GPU Platforms: Utilization Looks Fine

Why Traditional Kubernetes Thinking Fails for GPUs

The Real Problem: Treating GPUs Like CPU

What Actually Works in GPU Clusters

Why GPU Scheduling Needs Intentional Design

The Cultural Shift: GPUs Are Not Owned Resources

Where Most GPU Optimization Efforts Fail

Closing Thoughts

So, what coming next?

KubeCon + CloudNativeCon EU 2026: The Year Kubernetes Grew Up (Again)

Walking into Amsterdam: A Different Kind of Energy

The Big Shift: From Kubernetes Adoption → Kubernetes Optimization

Platform Engineering Took Center Stage (And Finally Grew Up)

AI + Kubernetes: Less Hype, More Reality

Cost Is Now a First-Class Concern

The Multi-Cluster Reality Check

Backstage Pass: What People Said Off the Record

What Will Trend After KubeCon 2026

Where You Should Really Focus (If You’re a Platform/DevOps Engineer)

The Real Takeaway

Kubernetes for HPC: The Quiet Convergence Reshaping High-Performance Computing

Top Three Takeaways

The Core Issues That Made Kubernetes + HPC Inevitable

A DevOps-Friendly Introduction to HPC

How Kubernetes Transforms the HPC Service Layer

Management, Provisioning, and Scaling—All Reimagined

A More Modern and Sustainable HPC Future

Kubernetes Autoscaling Myths: Why HPA Alone Won’t Fix Your Resource Problems

How Horizontal Pod Autoscaling Actually Works

Why Autoscaling Often Makes Overprovisioning Worse

GPUs Change the Economics Completely.