MLflow vs LiteLLM
Two of the top projects in Gateways and observability, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
MLflow
Tracing, evals, prompt registry and AI gateway plus classic ML tracking
LiteLLM
OpenAI-format gateway and Python SDK for calling 100+ LLM providers
Verdict
MLflow ranks higher, 89 to 86. Of the 4 score parts we measure for both, MLflow leads on 2, LiteLLM on 1 and 1 is even.
- AdoptionLiteLLM leads by 15 points, 95 to 80: a wide gap.
- FreshnessEven at 100.
- MaintenanceMLflow leads by 4 points, 87 to 83: close to even.
- Agent-readyMLflow leads by 55 points, 85 to 30: a wide gap.
Pick MLflow if you are
Teams wanting one platform for GenAI tracing and ML tracking
- One-line autolog for 60+ frameworks in Python, TypeScript and Java; MCP and OTel native
- Starts with uvx mlflow server; no separate database needed to begin
Pick LiteLLM if you are
Teams routing many LLM providers through one OpenAI-compatible endpoint
- Covers chat, responses, embeddings, images, audio, batches and rerank endpoints across many providers
- Works as an in-process Python SDK or a standalone proxy server
| What we compare | MLflow | LiteLLM |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 80, widely used | 95, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 87, healthy | 83, healthy |
| Agent-ready | 85, ready | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 28.5k | 61.9k |
| License | Apache-2.0 (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Mentioned |
MLflow
Single mlflow server (port 5000) that records OpenTelemetry traces from 60+ frameworks via one-line autolog, runs evaluations with 50+ metrics and LLM judges, versions and optimizes prompts, and fronts providers through an OpenAI-compatible AI Gateway with rate limits, fallbacks and traffic splitting. Keeps the original experiment tracking, model registry and deployment tooling. For teams wanting one platform for GenAI and ML.
Who it is for: Teams wanting one platform for GenAI tracing and ML tracking
Strengths
- One-line autolog for 60+ frameworks in Python, TypeScript and Java; MCP and OTel native
- Starts with uvx mlflow server; no separate database needed to begin
- AI Gateway adds credential management, guardrails and A/B traffic splitting
- Setup wizard lets Claude Code, Codex or OpenCode add tracing to a project
Weaknesses
- README covers the quickstart; production backend store and auth setup live in docs
- Broad scope (ML tracking plus GenAI) means a large install and UI surface
- No Dockerfile or compose file at the repo root
- TypeScript and Java coverage is smaller than Python (5 TS and 2 Java frameworks listed)
- no GPU
- Docker + Compose
- Models: any LLM provider via autolog or the AI Gateway
- port 5000
LiteLLM
LiteLLM translates requests in the OpenAI format to more than 100 LLM providers, and runs either as a Python SDK or as a proxy server. The proxy adds virtual keys, spend tracking, guardrails, load balancing and an admin dashboard. It also fronts A2A agents and MCP servers, and the README cites 8ms P95 latency at 1k RPS.
Who it is for: Teams routing many LLM providers through one OpenAI-compatible endpoint
Strengths
- Covers chat, responses, embeddings, images, audio, batches and rerank endpoints across many providers
- Works as an in-process Python SDK or a standalone proxy server
- Proxy includes virtual keys, spend tracking, guardrails and load balancing
- Also fronts MCP servers and A2A agents through the same gateway
Weaknesses
- Endpoint support varies per provider; the README table shows many gaps
- License is detected as NOASSERTION; check the repo's license files before use
- An Enterprise tier exists; the README excerpt does not say which features are paid
- Two SDK packages (litellm, litellm-core) overlap and cannot be installed together
- no GPU
- Docker + Compose
- Compose runs PostgreSQL
- Models: OpenAI, Anthropic, Gemini, AWS Bedrock, Azure
- port 4000
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