Komodor 2025 Enterprise Kubernetes Report

div **Operations data from hundreds of customers reveals that platform teams lose 34 workdays per year resolving issues, and consistent over-provisioning escalates unnecessary cloud costs** **TEL AVIV and SAN FRANCISCO, September 17, 2025** – Komodor today announced the findings from its new *Komodor 2025 Enterprise Kubernetes Report * which reveal that most enterprises still struggle to keep production environments stable and costs under control. According to the report, nearly 8 in 10 incidents stem from recent system changes, outages still take close to an hour to detect and resolve, and more than 65% of workloads run under half their requested CPU or memory, fueling chronic overspend. The data paints a consistent picture: complexity is rising faster than operational discipline. Most incidents trace back to changes pushed into multi-cluster, multi-environment estates. Teams split their time almost evenly between hunting the problem and fixing it, and the excess capacity provisioned to “play it safe” quietly taxes business every hour of every day. The report’s key finding is that Kubernetes is mature, but enterprise operations still aren’t. “Organizations have made Kubernetes their standard, but our report shows the real challenge is operational, not architectural,” said Itiel Shwartz, CTO and Co-founder of Komodor. “Even as practices like GitOps and platform engineering gain traction, enterprises still grapple with change management, cost control, and skills gaps. At the same time, the growth of AI/ML workloads and AIOps marks the next frontier, reinforcing Kubernetes as the backbone of enterprise infrastructure.” ### Key Highlights from the Report The *Komodor 2025 Enterprise Kubernetes Report* exposes clear patterns on how enterprises are running Kubernetes at scale. While adoption is nearly universal, the findings demonstrate that recurring issues that slow recovery, inflate cloud bills, and expose customers to outages are driving risk and cost. Highlights from the report include: - **Change is the leading driver of instability**: 79% of production issues originate from a recent system change. - **Slow detection and recovery persist**: Median MTTD is nearly 40 minutes for high-impact outages, while median MTTR is more than 50 minutes. On average, teams lose more than 64 full workdays every year detecting and resolving issues. - **Business impact is costly and frequent**: 38% of companies report high-impact outages weekly, while 62% estimate costs at $1M/hour for major downtime. - **Ops teams are still busy firefighting**: over 60% of their time is spent on troubleshooting issues, while only 20% of incidents are resolved without escalation. - **Overspend is widespread**: More than 82% of Kubernetes workloads are overprovisioned (65% use less than half of the CPU and memory they request) reflecting unnecessary over-provisioning and rightsizing gaps. Meanwhile, 11% are underprovisioned, and only 7% hit accurate requests and limits. - **Scale and complexity compound risk**: A typical enterprise now runs more than 20 clusters, with nearly half operating across more than four environments. - **AI adoption is rising in ops**: Enterprises are rapidly adopting AI in operations, from AI and ML model monitoring to AIOps, and see the greatest impact when these tools are embedded into unified observability and incident response. - **Skills remain a primary constraint**: Kubernetes expertise gaps slow troubleshooting, cost management, and policy enforcement. ### How to Use These Findings The data shows where Kubernetes operations break down: change complexity, slow incident response, and costly over-provisioning. The following best practices offer a roadmap to unify reliability, prevention, and efficiency. … ### FinOps in the Age of Kubernetes: When Everyone Owns the Bill Platform teams find themselves caught in the middle, trying to optimize shared infrastructure while both sides insist their priorities are non-negotiable. This conflict plays out across enterprises constantly, and it reveals a fundamental problem with how cost optimization works in cloud-native environments. The typical FinOps model, where a centralized team identifies savings opportunities and pushes recommendations to engineering, assumes that cost and operations are separate domains that can be optimized independently. In Kubernetes, that assumption breaks down completely.