Cost optimisation in Kubernetes has moved well past “set your requests and turn on the HPA”. Maybe 5 years ago, this might have worked for most projects. But now, the appetites and demands for resources (and their economy) are monstrous.
2024–25 has seen three clear shifts:
Have questions regarding any of these? Here, my fellow DevOps engineers from Dysnix and I will take you on a pleasant trip, explaining where you can save a dollar or two on your Kubernetes (shortly, K8s) infrastructure using best practices, advanced techniques, and special tools from our experience.
At its core, cost optimization in Kubernetes is about cutting everything that is not used and aligning infrastructure consumption with what brings extra business value, workload patterns, and operational efficiency.
It’s not a task of the DevOps engineer only, and it would be strange to expect the effect of Kubernetes cost reduction from just one person. This continuous project requires implementing observability, automation, and making the best-fit architectural choices.
If you've seen your cloud bill and felt uneasy about your future one at least once, you understand why your infrastructure appetite can financially ruin the project. Kubernetes cost optimization is a business-critical method, not just a technical tweak. Overspending, poor visibility, and risks to both financial and operational health can erode even the best projects.
Here we name more reasons that refer to your personal causes for implementing better Kubernetes cost management:
A 2025 Forbes Technology Council report found that 93% of enterprise platform teams struggle with cloud cost management, especially in Kubernetes environments. Nearly half of organizations (49%) actually saw increased cloud spending after adopting Kubernetes, often due to overprovisioning, idle resources, and lack of granular visibility. But it doesn’t make K8s bad.
Cost optimization helps to reveal your technical imperfections. For example, overprovisioned clusters can mask performance issues, while underprovisioned ones risk outages. Poor resource management leads to production downtime, erodes customer trust, and causes revenue loss.
The most successful teams balance cost with performance by continuously monitoring, rightsizing, and automating scaling, never treating optimization as a one-off project or a goal that can be reached at once.
The 2025 Cast AI Kubernetes Cost Benchmark Report, based on data from over 2,100 organizations, found that clusters running automated rightsizing and spot instance automation achieved a cost reduction of up to 50% compared to static, manually managed clusters.
The report also notes that the average CPU utilization in production clusters is only 37%, meaning most organizations are paying for nearly twice the compute they actually use.
The CNCF’s 2024 FinOps for Kubernetes analysis highlights that the biggest challenge is that teams lack clear ownership and cost attribution. Mature organizations are now using cost models that break down spend by team, service, and even feature, enabling targeted optimization and accountability.
Visibility and accountability are not about “suspecting” your colleagues; this approach in Kubernetes optimization provides the most detailed and illuminating helicopter view of your inner money flow, including all those nice nodes and pods of your K8s infrastructure.
Summing up, the trends from both CNCF and Cast AI reports highlight that the most effective cost optimization happens when engineering and finance (FinOps) teams work together.
This means sharing real-time cost data, setting shared goals (like /requestor/request or /requestor/customer), and making cost a first-class metric alongside latency and uptime.
To reach the best results in cost optimization for mature companies, FinOps tooling, scheduling algorithms, and architectural patterns should now interlock:
In the platform dimension, it can be implemented like this:
But not all projects need the described approach, as there’s no single remedy for all. In most cases, you need just:
Having all that on your plate, you’ll be able to make the action plan that will improve your situation.
Microservices vs. monoliths
Choosing between microservices and monolithic architectures affects cost. Microservices can be scaled independently, allowing you to allocate resources precisely where needed, but they may introduce overhead in networking, service mesh, and inter-service communication. Monoliths might be simpler and cheaper to run at a small scale, but can lead to over-provisioning as you must scale the entire application for a single bottleneck.
Micro vs. mono, multi‑tenant vs. silo, managed vs. self‑hosted — the trade‑off space is now quantifiable.
Pro tip: The real lever is the scaling dimension. A well‑partitioned monolith that exposes a single concurrency hotspot is still cheaper to run than a swarm of nanoservices that thrash the network. Architecture, therefore, needs to be tied to variability, not to size.
Multi-tenancy and namespace design
Deciding whether to run multiple teams or applications in a single cluster (multi-tenancy) or to isolate them in separate clusters impacts cost. Multi-tenancy can improve resource utilization and reduce overhead, but requires careful security and quota management. Poorly designed namespaces or a lack of quotas can lead to resource contention and unexpected cost spikes.
Cluster sizing and federation
Architecting for a single large cluster versus multiple smaller clusters (possibly federated across regions or clouds) affects cost. Multiple clusters can help with fault isolation and compliance, but may increase management overhead and reduce resource sharing efficiency. Federation can enable workload placement in the most cost-effective region or cloud.
Pro tip: A single shared cluster with strict network policies stays cheaper up to ~80 nodes. Beyond that, control-plane costs and noisy-neighbor risk tilt the balance toward a hub-and-spoke model: one “cash register” production cluster per P&L unit, plus a shared dev/staging area.
Use of spot/preemptible instances
Architecting your workloads to tolerate interruptions (stateless, easily restartable jobs) allows you to use spot or preemptible VMs, which are significantly cheaper than on-demand instances. This requires designing for resilience and rapid recovery, often using Kubernetes features like PodDisruptionBudgets and node affinity.
We at Dysnix had a great story involving spot instances that skyrocket the efficiency of a zk-powered project:
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Storage and data management choices
Selecting between persistent volumes, object storage, or ephemeral storage impacts both performance and cost. For example, using object storage for logs or backups can be much cheaper than block storage, but may require changes in application logic.
Service mesh and add-ons
Deciding whether to use a service mesh (like Istio or Linkerd) or other add-ons (monitoring, logging, security) introduces both operational benefits and resource overhead. Over-provisioning these components or running them at a cluster-wide scope can inflate costs.
Pro tip: Recent 2025 benchmarks and official documentation show that Istio’s ambient (sidecarless) mode reduces CPU and memory overhead by over 70–90% compared to sidecar mode, with a single ztunnel proxy consuming as little as 0.06 vCPU at 1000 HTTP requests/sec.
This makes the mesh’s resource consumption negligible relative to application workloads, and ambient mode is now the highest-bandwidth way to achieve secure zero-trust networking in Kubernetes.
Autoscaling strategies
Architectural decisions about how and when to scale—using HPA, VPA, Cluster Autoscaler, or third-party tools—determine how efficiently you match resource supply to demand. For example, event-driven architectures can leverage KEDA for fine-grained, metric-based scaling.
Predictkube is one of the most prominent predictive autoscalers for KEDA made by Dysnix. Enjoy!
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Application design for cloud-native patterns
Designing stateless applications, using sidecar containers judiciously, and leveraging managed services (like cloud databases or queues) can offload operational burden and optimize costs, but may introduce vendor lock-in or data transfer costs.
Network topology and traffic management
Architecting for minimal cross-zone or cross-region traffic, using internal load balancers, and optimizing ingress/egress rules can reduce data transfer and networking costs, which are often overlooked.
Pro tip: Microfrontends + edge rendering for high-traffic global applications. When latency budgets force workloads into multiple points of presence (POPs), the extra cost of additional clusters is dwarfed by egress savings and CDN origin shielding.
Kubernetes allows you to set CPU and memory requests/limits per container. However, deep cost optimization means continuously profiling workloads, using tools like Vertical Pod Autoscaler (VPA) or Goldilocks, to dynamically adjust these values based on real usage patterns, not just static estimates. This prevents both over-provisioning (waste) and under-provisioning (performance issues).
Beyond the basic Horizontal Pod Autoscaler (HPA), advanced setups use custom metrics (like queue length, latency, or business KPIs) to drive scaling decisions. Implementation might sound like this: CPU, latency, and business KPI targets in the same ScaledObject, with queue depth as a hard floor and cost as a soft ceiling.
This ensures that scaling is tightly coupled to actual demand, not just resource consumption, which can be misleading for bursty or event-driven workloads.
Kubernetes’ scheduler tries to fit pods onto nodes efficiently, but deep cost optimization involves using taints, tolerations, and affinity/anti-affinity rules to ensure high-density packing of compatible workloads.
This reduces the number of underutilized nodes, directly lowering costs. More mature organizations can use custom schedulers or third-party tools to optimize node lifecycles and beneficial spot/preemptible instance usage.
Pro tip: Dynamic pod rightsizing loops work well! Goldilocks and VPA are helpful, but the new pattern is “observe → propose → apply during next GitOps run.” Teams export VPA recommendations nightly, surface them in pull‑request comments, and let the app owner approve or override. Fairwinds’ controller and CloudZero’s PR bot are the popular implementations.
By integrating tools like Kubecost or Cloud provider billing APIs, teams can attribute costs down to the namespace, deployment, or even label level. This enables proper cost accountability and empowers teams to make data-driven decisions about architecture and scaling.
Pro tip #1: Make a FinOps show‑back to teams via OpenCost. Labels “team=foo,feature=bar” are propagated by admission webhooks; OpenCost emits per‑team cost reports consumed by Jira Automation to comment “your feature cost $X last sprint”. This cultural tooling turns cost optimization into a daily engineering reflex, rather than an end-of-quarter panic.
Pro tip #2: QoS‑aware logging budgets with custom scripting. Logging price often eclipses compute, especially with high-traffic microservices and verbose logs. Platform teams now inject a Fluent Bit filter that drops DEBUG below a dynamic “budget” expressed in GiB/hour per namespace. When usage exceeds quota, the log level is coerced to WARN. This turns observability into a predictable line item.
For example, non-critical batch jobs might be scheduled on cheaper spot instances or in regions with lower pricing, while latency-sensitive services stay on premium nodes.
Despite the general trend of automation and delegating optimization tasks to AI agents and other innovative tools, in practice, our architects and engineers, along with their expertise and analysis, still bring the most value in most cases.
