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.
The wave of AI-powered Site Reliability Engineering (SRE) tools is redefining cloud native infrastructure, promising to cut downtime and free SRE, DevOps and Kubernetes admins from operational toil. But as vendors, open source projects, and observability giants flood the market with “AI SRE,” a critical question remains: Can you actually trust them?
This benchmarking guide cuts through the noise to provide a technical, evidence-based framework for evaluating AI SRE tools. It dissects the transition from simple chatbots to autonomous agentic architectures and establishes the standards required for safe, large-scale, production-grade AI SRE.
Transparency
Why engineers reject “black box” automation and why trust depends on an AI’s ability to provide evidence, timelines, and change history alongside every recommendation.
Evaluation Framework
How to benchmark AI tools against realistic production failure scenarios, from cascading service failures to complex dependency issues, using the “LLM-as-a-Judge” methodology.
From Copilot to Fully Autonomous
Understanding the architectural shift from reactive 2023-era LLMs to 2025’s agentic workflows that anticipate and prevent downtime.
Maintaining a Standard for Accuracy
How to ensure your AI SRE doesn’t hallucinate. We define the rigorous testing cycles and closed feedback loops required to achieve 95% RCA precision.
Using an AI SRE is not about letting an AI loose on your cluster; it is about building a system of guardrails and verified knowledge. This guide covers the evolving AI SRE landscape and defines the evaluation criteria you need to distinguish between tools that simply chat and platforms that can safely resolve incidents at scale.
Gain instant visibility into your clusters and resolve issues faster.
