The Hidden Costs of Kubernetes
Kubernetes has become widely recognized for its operational efficiency and cost-effectiveness at scale. It promises over 50% savings in infrastructure costs compared to traditional VMs, shows a strong correlation with operational high performers, and boasts a thriving ecosystem with over 12,000 open source applications.
However, the initial years of implementing Kubernetes can pose difficulties for many organizations. The significant engineering personnel costs required to operate a cluster often hinder the realization of these promised benefits. Some organizations manage to quickly reduce these costs and enhance their capabilities, reliability, and security, while others struggle, leading to what we refer to as the "Kubernetes cost curve."
Our team conducted interviews, surveys, and benchmarks to model the Kubernetes cost curve for various organizations. Factors such as software complexity, cluster size, and the number of application developers were taken into account. We distinguished between "strategic engineering" – tasks related to new capabilities, observability, reliability, and security – and "keep-the-lights-on" engineering, which involves assisting developers and handling alerts.
The findings are not surprising:
- Keep-the-lights-on engineering costs can be up to 5 times larger than total cloud costs, particularly for smaller teams.
- Organizations that prioritize strategic engineering early gain an advantage on the cost curve, while those unable to afford it fall behind, incurring higher cloud and engineering costs.
- The impact of both getting ahead and falling behind is particularly pronounced with Kubernetes.
The significant challenge lies in the fact that Kubernetes provides minimal operational support "out of the box." Its design relies on thousands of operationally-oriented independent software components in its ecosystem, which maximize a team’s productivity but take time to evaluate, implement, and mature.
A few years ago, organizations implementing Kubernetes could offset the cost curve by adding incremental headcount in the initial year. However, in today's economic climate, this approach is no longer feasible.
So, how can engineering leaders make room for strategic engineering now, without additional headcount? How can they reduce keep-the-lights-on engineering without compromising security, reliability, or the developer experience?
Navigating the Cost Curve
Our team embarked on analyzing the time spent on keep-the-lights-on platform engineering, identifying areas that could be addressed with management techniques or new technologies. Two surveys revealed that early Kubernetes adopters often required one Kubernetes expert for every 5-10 application developers. This cost scaled with the number of developers on staff. The second significant cost, triaging noisy alerts, correlated with software complexity and infrastructure volume.
Can modern AI provide a solution?
By empowering developers with AI tools for basic troubleshooting, organizations can immediately reduce keep-the-lights-on platform engineering by 30-60%.
The Role of Modern AI
Modern AI, particularly large language models, may not excel at generating troubleshooting automation scripts. However, it proves effective in adding searchable metadata, sequencing troubleshooting steps written by experts, and summarizing results. The RunWhen platform utilizes this throughTroubleshooting Digital Assistants which lead junior developers and new SREs through complex troubleshooting scenarios with guided next steps and dynamically generated runbooks.
Where To Next?
If you are interested in exploring a live demo and benchmarking your organization's current and future costs, consider booking a session with us. We aim to provide insights into the complexities of Kubernetes adoption and cost management, allowing organizations to make informed decisions tailored to their unique circumstances, with solutions like RunWhen.
