KAITO vs KServe vs kube-llmops
If you are running LLMs on Kubernetes in 2026, you have probably encountered three main options: KAITO (Microsoft/CNCF Sandbox), KServe (CNCF Incubating), and kube-llmops.
TL;DR Comparison
| Feature | kube-llmops | KAITO | KServe |
|---|---|---|---|
| AI Gateway | Built-in (LiteLLM) | No | No |
| LLM Tracing | Langfuse v3 | No | No |
| Grafana Dashboards | 11 pre-built | No | No |
| KEDA Autoscaling | Yes | No | Partial |
| SSO | Keycloak OIDC | No | No |
| RAG | Dify + pgvector + TEI | No | No |
| Fine-tuning | LLaMA-Factory + Argo | Basic | No |
| Cloud-Agnostic | Yes | Azure-only | Yes |
kube-llmops is the only platform that gives you a complete LLM operations stack in one Helm install: model serving (vLLM, llama.cpp), AI gateway, observability, RAG, fine-tuning, SSO, and autoscaling.
https://github.com/GaeaRuiW/kube-llmops
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