The Real Cost of Kubernetes: It's More Than You Think
Discover the real cost of Kubernetes adoption. It's not just about the technology—it's about the labor and operational burden that can quickly exceed your budget. Our latest blog post breaks down the hidden costs, from engineering effort to high SLA targets, and provides strategic recommendations to save your team from burnout. Learn why a "DIY" approach can be more expensive in the long run and how to budget effectively for your Kubernetes journey.
8/21/20252 min read
Kubernetes has become a core component of enterprise infrastructure, offering powerful orchestration for modern applications. While the software is open source and widely supported, the reality of operating it at scale is far from simple. The illusion of a low or no-cost solution is quickly shattered once you begin to factor in the operational burden. This is a critical point that many organizations underestimate when they begin their Kubernetes journey. The biggest cost driver isn't cloud infrastructure or licensing—it's the people required to design, build, and run the platform.
The Hidden Labor Costs of Kubernetes Adoption
Running a production-grade Kubernetes environment requires a significant investment in labor and engineering effort. Even a small-scale, single-cluster platform incurs a first-year labor cost of nearly $100,000, with annual operational overhead exceeding $35,000. As the platform grows, these costs escalate dramatically. For a large enterprise with 50 clusters and 500 applications, annual run costs can routinely exceed $1 million, not including infrastructure spend.
These costs are driven by the need to design, build, and maintain a functional platform. This includes implementing complex systems like GitOps-based deployment pipelines, authentication, RBAC, and observability stacks. Furthermore, teams must continuously manage semi-annual Kubernetes version upgrades and respond to security vulnerabilities (CVEs). These efforts introduce significant and recurring operational complexity and cost.
The Impact of Service Level Agreements (SLAs)
The desired level of platform availability also plays a decisive role in cost. Increasing platform availability from 95% to 99.9% is not a marginal exercise. Each increase in uptime expectation demands proportionally more labor for things like redundancy validation, automated failover, and on-call support. Higher SLA targets can increase operational overhead by 20% to 30% per "nine". These are not optional costs for platforms that support critical business services.
Why "DIY" Isn't Always the Best Option
The decision to "build versus buy" is often driven by engineering teams who believe they can assemble and maintain the required platform using open-source components easily and at low cost. However, this approach frequently underestimates the long-term cost of labor, coordination, and support. What begins as a seemingly low-cost build often becomes a long-term operational burden with significant delivery risk.
To minimize these costs and risks, organizations should consider a few strategic recommendations:
Budget for human effort, not just tooling.
Align SLA targets with business value to avoid unnecessary labor costs.
Consider integrated or commercially supported platforms when internal bandwidth is limited. These platforms can reduce the engineering burden by 40% to 60% compared to building everything in-house.
By approaching Kubernetes with clear insight and realistic expectations, teams are better positioned to build platforms that are stable, scalable, and sustainable.