Startup profile · funding coverage
Komodor funding, valuation and investors
Kubernetes troubleshooting and operations platform with AI assistant Klaudia for cluster and workload incidents.
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Funding, valuation & investors
Answer-first snapshotLatest funding
Series B
$42M; cumulative about $67M · May 11, 2022
Latest known valuation
Not publicly disclosed
Total disclosed equity funding
$42M
Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.
Current status
private
growth · Tel Aviv, Israel
Overview
Komodor offers Kubernetes operations visibility, changes, troubleshooting and developer self-service. Its AI assistant Klaudia aims to explain cluster-level and workload faults and suggest corrective steps from Kubernetes state and related operational context. It reported a $42M Series B in May 2022 and cumulative funding of about $67M at that time.
Unlike general AI SRE vendors, it is strongest when the source of failure is primarily Kubernetes-native; app-layer data correctness or distributed business workflows may need additional telemetry.
Why Komodor is interesting
Kubernetes-specific insight can solve production incidents more directly than general-purpose LLM agents for teams whose failures center on scheduling, deployments, workloads and resource contention.
Product & use cases
Kubernetes incident analysis, workload change history, cluster health, policy and AI-powered troubleshooting via Klaudia.
- Diagnose CrashLoopBackOff, OOMKills, scheduling failures and deployment errors
- Investigate cluster and pod changes across environments
- Provide developers self-service operational context
Funding history (newest first)
Series B
2022-05-11 $42M; cumulative about $67MSource: Read source · komodor.com
Competitive landscape
Edge: Deep Kubernetes-specific event context and workflow, more targeted than broad incident agents.
The closest alternatives differ by whether they investigate existing telemetry, orchestrate incidents, evaluate AI agent behavior, or prevent adversarial exploitation. Selection should be based on representative task-level tests, deployment permissions, data control and total ownership cost.
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Traversal adjacent
Broader multi-stack AI SRE
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Causely direct
Kubernetes and causality
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CAST AI adjacent
Kubernetes efficiency and infrastructure optimization
Industries
Market / IPO context
Editorial / static context — not a live quote.
FAQs about Komodor
Practical answers founders, operators, and investors typically search for.
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