Phoenix vs promptfoo
Two of the top observability, side by side: score, setup, license, activity and what each review found.
Phoenix
LLM tracing, evals, datasets and prompt playground built on OpenTelemetry
promptfoo
CLI for evaluating and red-teaming prompts, agents and RAG
| What we compare | Phoenix | promptfoo |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 52, popular | 70, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 79, fair | 82, healthy |
| Easy to run | 67, easy | 50, easy |
| Agent-ready | 85, ready | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 11.8k | 25.9k |
| License | custom license (read the license) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
Phoenix
Phoenix collects traces from LLM applications through OpenTelemetry/OpenInference instrumentation and shows them in a web UI. It also covers LLM-based evals, versioned datasets, experiments, prompt management and a prompt playground that can replay traced calls. It runs via pip, uvx, Docker or a Helm chart, and exposes a remote MCP endpoint at /mcp for coding agents.
Who it is for: Teams debugging and evaluating LLM apps who want self-hosted tracing
Strengths
- Install with pip or uvx and run `phoenix serve`; no separate setup shown
- Auto-instrumentation for LangGraph, LlamaIndex, CrewAI, DSPy, Vercel AI SDK and more
- Built-in MCP server at /mcp lets Claude Code and Cursor query traces
- Python and TypeScript packages for OTel, client and evals
Weaknesses
- License reported as NOASSERTION; terms need checking before commercial use
- Managed production workflows are pushed to the paid Arize AX product
- Azure template serves plain HTTP and needs a TLS proxy in front
- RAM, storage backend and default port not stated in the README excerpt
- no GPU
- Docker + Compose
- Compose runs PostgreSQL
- Models: OpenAI, Anthropic, Google GenAI, AWS Bedrock, OpenRouter
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