promptfoo vs MLflow
Two of the top observability, side by side: score, setup, license, activity and what each review found.
promptfoo
CLI for evaluating and red-teaming prompts, agents and RAG
MLflow
Tracing, evals, prompt registry and AI gateway plus classic ML tracking
| What we compare | promptfoo | MLflow |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 70, popular | 75, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 82, healthy | 87, healthy |
| Easy to run | 50, easy | 33, some setup |
| Agent-ready | 45, minimal | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 25.9k | 28.3k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
promptfoo
Runs prompt and model evaluations from a YAML config via promptfoo eval, compares providers side by side, and generates red-team vulnerability reports; promptfoo view opens a local web viewer. Installs with npm, Homebrew or pip, runs in CI/CD, and can scan pull requests for LLM security issues, for developers testing prompts and agents before release.
Who it is for: Developers testing prompts and agents before release
Strengths
- Evals run locally; prompts stay on your machine
- Red-team scans produce vulnerability reports alongside quality evals
- Live reload and caching for fast iteration; npx usage needs no install
- MIT licensed and still open source after joining OpenAI
Weaknesses
- Primarily a CLI; the web viewer is a local results UI, not a multi-user server
- Most providers require an API key; local use needs Ollama or similar
- README is short; config syntax, assertions and providers are only in the docs
- Dockerfile exists at the root but the README gives no Docker instructions
- no GPU
- Docker
- Needs Node.js (npm) or Python (pip), LLM provider API key or Ollama
- Models: OpenAI, Anthropic, Azure, Bedrock, Ollama
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