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.
Highly Accurate, Always on Troubleshooting
Komodor’s AI SRE Platform works like a team of specialized engineers that continuously detect, investigate, and resolve real-time issues – reducing the time to identify and remediate cloud native infrastructure problems at scale.
Investigating Kubernetes incidents often leads to a wild goose chase. Komodor automatically detects issues and delivers accurate root cause analysis that explains failures in seconds. It continuously analyzes and correlates logs, events, configurations, metrics, and deployment history across all workloads, add-ons, CRDs, and nodes, showing you what failed, its impact, what triggered it, and what to do next. From simple image pull errors to complex cascading failures, conflicting configs or unhealthy dependencies, Komodor finds the root cause. Connect Komodor to your internal runbooks and knowledgebases to customize the analysis to your organization analysis. This entire, end-to-end process is production-proven, delivering >95% RCA accuracy to reduce incident resolution time by 70%.
Komodor turns troubleshooting into an interactive experience, allowing teams to ask follow-up questions and get deeper context for any incident. Ask questions like “Why is this pod stuck in crashloop?” or “Which deployment triggered this CPU spike?”, and Klaudia Chat Agent will analyze your data, trace dependencies, and respond with a clear, structured explanation, accelerating MTTR and eliminating the guesswork.
When self-healing is enabled, Komodor automatically detects, troubleshoots, and remediates incidents – ensuring continuous reliability and allowing teams to focus on innovation instead of firefighting. Our remediation agents can automatically execute safe, policy-driven actions like restarting workloads, reverting bad configs, draining unhealthy nodes, or rolling back failed releases. For added control, teams can apply a human-in-the-loop workflow to review and approve remediation actions. Every automated action is logged, auditable, and compliant with built-in policy guardrails, ensuring speed never comes at the cost of safety. Once an issue is resolved, Klaudia automatically validates the fix to confirm system stability before closing the loop.
Komodor continuously monitors configurations, patterns, and behavioral signals across every cluster and resource to recognize emerging risks before they lead to outages. It detects early indicators of instability, such as throttling, frequent restarts, resource pressure, or scaling failures, and connects them to their underlying causes, whether in code, infrastructure, or configuration.
“Komodor has improved the user experience for engineers, who were previously relying on the Kubernetes dashboard. After Komodor was introduced, we (the platform team) started providing links to Komodor when helping engineers, which led to a reduction in the number of queries we received, as the engineers were able to self-serve more using Komodor.”
Michael B
Staff Site Reliability Engineering Manager OpenTable
Kubernetes errors often affect multiple services due to complex interdependencies, like an expired TLS certificate in cert-manager that disrupts every dependent service. Komodor maps interdependencies between services, infrastructure, and controllers – so when an issue starts in one layer, you immediately see how it cascades through the rest. All correlated data is presented in a single timeline view, helping pinpoint not only what failed but also the original root cause and its downstream impact, significantly reducing troubleshooting time.
Technical Product Management, Smarsh
Director of DevOps, Lusha
Cloud Infrastructure Manager
Director of Platform Engineering
Principal Cloud Engineer, Priceline
Priceline
Senior DevOps Engineer
Balyasny Asset Management
Data Operations Manager, Lusha
Staff Software Engineer, Priceline
Director of Software Engineering, Digibee
DevOps
Staff Software Engineer
Faster troubleshooting through our AI SRE platform helps teams find the root cause FAST, reducing the impact of incidents.
Operational friction is a hidden tax on your development teams. Komodor provides developers with self-service needed to resolve issues. The result is a sharp reduction in ‘TicketOps’ for the SRE and Platform teams.
Continuous reliability and uptime helps protect the bottom line and maintain optimal customer trust.
Gain instant visibility into your clusters and resolve issues faster.
