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.
Here’s what they’re saying about Komodor in the news.
Join the Komodor partner program and accelerate growth.
For teams with strong engineering talent, creating a DIY AI SRE seems like a straightforward challenge. But the decision to build or buy is a critical strategic choice.
Komodor partnered with a leading AI Cloud Provider to tackle their operational hurdles. Here's how our AI SRE, Klaudia, successfully bridged the visibility gaps in their highly customized CAPI infrastructure.
At KubeCon Europe 2026, Komodor is unveiling a new extensible multi-agent architecture for Klaudia AI. To understand why it matters, it helps to start with why building AI for infrastructure is so fundamentally hard.
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.
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.
Gain instant visibility into your clusters and resolve issues faster.
May 12 · 9:00EST / 15:00 CET · Live & Online
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