Komodor is an autonomous AI SRE platform for Kubernetes. Powered by Klaudia, it’s an agentic AI solution for visualizing, troubleshooting and optimizing cloud-native infrastructure, allowing enterprises to operate Kubernetes at scale.
Proactively detect & remediate issues in your clusters & workloads.
Easily operate & manage K8s clusters at scale.
Reduce costs without compromising on performance.
Guides, blogs, webinars & tools to help you troubleshoot and scale Kubernetes.
Tips, trends, and lessons from the field.
Practical guides for real-world K8s ops.
How it works, how to run it, and how not to break it.
Short, clear articles on Kubernetes concepts, best practices, and troubleshooting.
Infra stories from teams like yours, brief, honest, and right to the point.
Product-focused clips showing Komodor in action, from drift detection to add‑on support.
Live demos, real use cases, and expert Q&A, all up-to-date.
The missing UI for Helm – a simplified way of working with Helm.
Visualize Crossplane resources and speed up troubleshooting.
Validate, clean & secure your K8s YAMLs.
Navigate the community-driven K8s ecosystem map.
Who we are, and our promise for the future of K8s.
Have a question for us? Write us.
Come aboard the K8s ship – we’re hiring!
Here’s what they’re saying about Komodor in the news.
Part 6 of our AI SRE in Practice Series. In this scenario we walk through an AWS CNI IP exhaustion incident where 15 services experienced outages before platform teams identified the root cause.
Part 5 of our AI SRE in Practice Series. This scenario walks through a policy enforcement incident where a seemingly minor configuration change caused widespread pod failures that required deep investigation across the cluster to understand the scope and root cause.
When a new, competing open-source Kubernetes troubleshooting agent was launched, we thought it would be a good idea to put both tools through identical real-world failure scenarios our customers typically encounter. The objective was to benchmark Klaudia Agentic AI and the open-source AI agent, and compare their performance across common Kubernetes failure scenarios.
Part 4 of our AI SRE in Practice Series. In this part we examine what happens when a node terminates unexpectedly, and dealing with the harder question of why it happened and how to prevent it from happening in the future.
Part 3 of our AI SRE in Practice Series. In this part we cover how an AI SRE helps diagnose configuration drift in deployment failures.
Part 2 of the AI SRE in Practice Series. In this post we discuss: Resolving GPU Hardware Failures in Seconds
This series demonstrates what AI SRE trained on real workloads actually looks like in practice. We're going to walk through real troubleshooting scenarios that our customers encounter daily, showing the before and after of AI-powered investigations.
SRE teams are about to feel even more pressure. GPU-heavy computing is breaking the assumptions today's clusters were built on, while enterprises are beginning to trust autonomous operations and cost pressure is pushing consolidation across the cloud-infrastructure stack. Based on these forces, here are my 2026 Kubernetes predictions as well as some best practice recommendations to help platform teams prepare for what reliable operations will mean next year.
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