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.
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