Arize Phoenix vs Promptfoo

Arize Phoenix is free, with no paid plan attached. Promptfoo is freemium — a usable free tier with paid plans above it. Both are listed under LLM Eval & Observability, so this is a like-for-like comparison. Neither is ranked above the other — Flocci carries no sponsored placement.

Arize Phoenix vs Promptfoo — straight answers

Arize Phoenix vs Promptfoo: what is the difference?

Arize Phoenix is free, with no paid plan attached and is listed for fully open-source, self-hostable with zero setup via `uvx` (pip/conda also supported). Promptfoo is freemium — a usable free tier with paid plans above it and is listed for open-source CLI/library for evaluating prompts, models and RAG pipelines side by side, runs in CI. Both sit in LLM Eval & Observability.

Is Arize Phoenix or Promptfoo cheaper to start with?

Arize Phoenix is the cheaper starting point: it is free, with no paid plan attached, while Promptfoo is freemium — a usable free tier with paid plans above it. Pricing tiers here come from the catalog, not from a promotional page.

Which should I choose, Arize Phoenix or Promptfoo?

Choose Arize Phoenix if you need fully open-source, self-hostable with zero setup via `uvx` (pip/conda also supported); choose Promptfoo if you need open-source CLI/library for evaluating prompts, models and RAG pipelines side by side, runs in CI. Flocci AI Tools does not rank one above the other — it shows both feature sets side by side and lets the requirement decide.

Arize Phoenix compared with Promptfoo: pricing tier, category, listed capabilities and links.
 Arize PhoenixPromptfoo
Pricing tierFreeFree tier + paid plans
Free to startYesYes
CategoryAI Models & Local ExecutionAI Models & Local Execution
TypeLLM Eval & ObservabilityLLM Eval & Observability
Listed capabilities
  • Fully open-source, self-hostable with zero setup via `uvx` (pip/conda also supported)
  • Combines tracing, evals, datasets, experiments and prompt playground in one local-first tool
  • AI engineering agent (PXI) built in for automated troubleshooting of traces
  • Open-source CLI/library for evaluating prompts, models and RAG pipelines side by side, runs in CI
  • Automated red-teaming to surface prompt injection, jailbreak and data-leak vulnerabilities
  • 300,000+ user community with an enterprise tier for teams needing hosted guardrails
Tagsarize phoenix, llm observability open source, tracing, evaluation, rag debugging, freepromptfoo, prompt testing, llm red teaming, eval framework, open source, free, ci for prompts
Websitephoenix.arize.compromptfoo.dev
Full pageArize Phoenix details →Promptfoo details →
AlternativesArize Phoenix alternatives →Promptfoo alternatives →

Google Stax

LLM Eval & Observability
freeNew
  • Build evals with human raters or LLM autoraters on your own data
  • Compare models and prompts on quality, latency and token cost
  • Prebuilt and custom evaluators

Laminar

LLM Eval & Observability
freeNew
  • Open-source observability and evals platform for AI agents (Apache-2.0)
  • Traces, session replay for browser agents and evaluations
  • Self-hostable

DeepEval

LLM Eval & Observability
freeNew
  • Open-source LLM evaluation framework with pytest-style tests and 50+ metrics
  • RAG, agent and conversational metrics; red-teaming module
  • Apache-2.0; optional Confident AI cloud has a free tier

EleutherAI LM Evaluation Harness

LLM Eval & Observability
freeNew
  • Standard open framework for running hundreds of benchmarks (MMLU, GSM8K and more) against any LLM
  • Backs the Hugging Face Open LLM evaluations
  • MIT licensed CLI and Python library

OpenLLMetry (Traceloop)

LLM Eval & Observability
freeNew
  • OpenTelemetry-based instrumentation for LLM calls, vector DBs and frameworks
  • Ships traces to Datadog, Grafana, Honeycomb or any OTel backend
  • Apache-2.0

Braintrust

LLM Eval & Observability
freemium
  • Free Starter plan ships model credits and scored evals with no card required
  • Unlimited users/projects/datasets/playgrounds even on the free tier
  • Built-in playground for side-by-side prompt/model comparison against production traces