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!
Discover our events, webinars and other ways to connect.
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Part 8 of our AI SRE in Practice Series. This scenario walks through how AI-augmented troubleshooting enables engineers without Kubernetes expertise to diagnose and resolve complex issues, using a real example from a team onboarding non-experts to platform operations.
We recently wrote about how AI-generated code is overwhelming SRE teams with production complexity they can't manage. Turns out that's only half the problem. The other half shows up on the cloud bill.
The acceleration of AI-assisted development has created an asymmetric problem. Developers got their force multiplier. SREs are still using the same playbook they had five years ago, except now they're responsible for exponentially more code, written by tools that prioritize speed over operational clarity.
Part 7 of our AI SRE in Practice Series. This scenario walks through how AI-augmented knowledge transfer changes the onboarding experience, using a real example from a containers team implementing changes to HiveMQ infrastructure.
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.
For an AI SRE to be safe and effective, it cannot rely on generic training data alone. It needs context. Klaudia solves this through a dual-layer approach to context engineering: the Organization Blueprint and the Knowledge Base Integration.
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.
This article explores the technical realities of building Klaudia, an agentic AI solution for Cloud-Native infrastructure.
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.
Gain instant visibility into your clusters and resolve issues faster.
