Subscribe to our newsletter
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
.png)
.png)
(GPUs don’t fail gracefully into inefficiency - they fail expensively.)
As we’ve all seen at the latest KubeCon - the AI craze demands ever more infrastructure and increasingly more organizations are running training and inference workloads on Kubernetes. In fact - 66% of organizations hosting generative AI models have fully integrated their inference workloads into Kubernetes clusters. This means larger clusters, more compute, memory, accelerators.
And the larger and more complex your cluster becomes - the more you need to think about costs. And here is why.
Kubernetes is built for scale and you may have heard about the economies of scale. It’s an economic principle that shows that the more units of something you produce - the lower is the average cost per unit - the so-called rule of diminishing unit cost. This happens because the infrastructure is shared and the processes are perfected for more efficiency. This way the fixed costs are spread over more units. In Kubernetes we achieve this by putting multiple applications on the same cluster, attaching various node types and using smart autoscaling to provision the nodes appropriate for each workload’s specific resource requirements.
But there are limits to the economies of scale. In fact - as a company grows too large - the size and the volume of production start causing inefficiencies resulting in production of goods and services at increased per-unit costs. This is called the diseconomies of scale and is usually caused by increasing communication costs, accidental overprovisioning and general lack of clarity.
And just the same way in Kubernetes - as your clusters grow larger, when you need more of those expensive GPU/TPU/NPU enabled nodes - their efficiency goes down. And here are some reasons why.
1.Fragmentation of Accelerator Resources
Accelerators (GPU, TPU, NPU, etc) are hard to slice efficiently and the technologies for doing that are still in their infancy. Even with the slowly evolving Kubernetes DRA (Dynamic Resource Allocation) and GPU sharing approaches like MIG (Multi-Instance GPU), workloads often don’t perfectly fit the available partitions. Moreover - there’s very little visibility into actual GPU utilization.
As a result:
- You end up with stranded capacity (e.g., 20–40% of a GPU unused).
- Scheduling constraints make it hard to pack workloads tightly.
- Small inefficiencies compound massively at scale.
(And this is where PerfectScale’s GPU visibility for Kubernetes can get you started on your optimization journey.)
2. Bin-Packing Complexity Increases Non-Linearly
Even with traditional workloads - the more of them you have - the less efficient the Kubernetes scheduler becomes. Its bin-packing efficiency is highly dependent on how well your engineers define the resource requests and limits for their applications.
The problem multiplies with the introduction of massively heterogeneous AI workloads dependent on accelerators.
- Matching pods to nodes with the right accelerator type, memory, and topology becomes harder.
- Many times memory gets wasted because there’s no differentiation between the prefill and decode phases
- Leads to suboptimal placement → more nodes than actually needed.
- The larger the cluster, the more pronounced the mismatch.
3. Data Locality and Network Bottlenecks
Increasingly more workloads in this brave new world are data-heavy and stateful. Cache matters, data locality matters, traffic costs grow exponentially.
- Poor pod placement can increase cross-node or cross-zone traffic.
- Accelerators wait on data → underutilization despite high cost.
- Larger clusters amplify network contention and latency issues.
4. Lack of Workload Co-Tenancy
Even for traditional workloads - balancing CPU and memory resources isn’t a walk in the park . One has to be very on point with resource requests and limits (with smart automated allocation provided by PerfectScale being a game changer). GPU jobs are even harder to safely colocate:
- Interference between workloads (memory bandwidth, cache contention).
- As a result: teams isolate workloads bringing us back to one workload per GPU/node.
- This reduced sharing increases waste and undermines economies of scale.
5. Organizational Coordination Costs
At scale, Kubernetes inefficiency isn’t just technical:
- Multiple teams competing for accelerator resources.
- Lack of visibility into usage.
- Duplicate environments, redundant workloads.
Result: human-driven inefficiency becomes a major cost driver.
The Time to Optimize is Now
The AI revolution is changing our whole industry and it’s also making your Kubernetes clusters more costly and less efficient. We’ve reached a level of complexity at which manual or home grown optimization doesn’t cut it anymore. The best engineers use the best tool for the job. Try PerfectScale’s autonomous Kubernetes optimization and governance now and go back to economies of scale.
