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.
Komodor AI SRE vs. Resolve AI
While Resolve AI offers a conversational AI assistant for root cause analysis, Komodor provides the only complete autonomous AI SRE platform purpose-built for ALL the complexities of cloud native infrastructure.
Resolve AI features conversational root cause analysis together with a Slack integration. But it lacks the platform abilities required by large scale enterprises, as it narrowly focuses on alert investigation. Visualization, troubleshooting and cost optimization capabilities are critical for dealing with real-world production problems.
Beyond reducing operational toil, an AI SRE needs to also help reduce cloud costs. Komodor has capabilities including dynamic right sizing, intelligent pod placement that are proven to save enterprises real money. Resolve AI treats cost as an afterthought (just identifying unnecessary logs).
Komodor can be implemented in minutes with a lightweight and non data-intensive onboarding process. You can get out of the box value in minutes, as opposed to a lengthy POC process that requires months of sensitive, carefully curated data training.
An AI SRE requires an intuitive, “single pane of glass” interface that consolidates multi-cluster, cloud, and hybrid into curated, contextual workspaces. It should feature a unified timeline that automatically correlates metrics, configurations, and events, allowing users to visualize impact and causality without switching between disparate tools.
Cost optimization is a core technical challenge because every reliability decision, such as over-provisioning for “safety by default”, is ultimately an engineering tradeoff that affects your error budget. A cost-aware AI SRE platform allows you to manage these drivers technically, ensuring reliability is delivered efficiently through automated rightsizing and intelligent pod scheduling.
Trust is assessed through transparency and “explainability,” where the AI provides the underlying logs, metrics, and reasoning behind its suggestions rather than acting as a “black box”. Additionally, trust is built incrementally by starting with low-stakes observation modes and manual approvals before progressing to fully autonomous, policy-governed remediation.
Critical technical considerations include ensuring the AI has deep K8s-specific context for accurate reasoning and seamless integration with existing monitoring toolchains and add-ons. From a security standpoint, the platform must never use customer data for model training, operate within a secure VPC, and enforce strict RBAC and Just-In-Time (JIT) permissions to prevent unauthorized access to sensitive cluster data.
See why Dev & Platform teams love Komodor on G2
Mid-Market
Komodor is the only platform that provides a contextual understanding of everything running in your clusters; from workloads and native resources to critical add-ons like service meshes and autoscalers. Battle-tested and purpose-built for demanding large scale enterprise environments.
Powered by Klaudia Agentic AI, Komodor rapidly resolves the most challenging cloud native headaches – from failed containers and cascading errors to faulty add-ons, CRDs, and workload breakdowns. Klaudia’s hundreds of specialized agents, trained on thousands of production environments, have been field-proven to deliver 95% accuracy across real-world incidents.
Gain instant visibility into your clusters and resolve issues faster.
