This is what production teams see happening. A vertical pod autoscaler recommendation gets applied automatically. Resource requests come down a notch across a namespace. The cost dashboard registers a small cost savings win. A few minutes later, health checks start failing. Pods enter crash loops. The on-call engineer is now the person responsible for unwinding […]
Read our complete guide to CrashLoopBackOff error: what it is, what causes it, and how to fix it.
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.
Virtual event will explore what it takes to use AI in production SRE, from incident response and observability to platform design, cost control and self-healing operations TEL AVIV and SAN FRANCISCO, April 22, 2026 — Komodor, the autonomous AI SRE company, today announced it will host AI SRE Summit 2026, a free live virtual event […]
Platform Engineering leaders are caught between two competing imperatives. You’re under pressure to flatten cloud spend but your team is still provisioning defensively because nobody wants to be the person who causes a production incident. You try to optimize, but six months later, when someone pulls a report, nothing has changed. Industry estimates consistently put […]
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.
Out-of-the-box and bring-your-own AI agents that encode operational knowledge boost troubleshooting speed and accuracy across cloud native infrastructure
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.
