LLM Evals Comparison
A PM-authored, vendor-neutral decision guide for choosing between LLM evaluation frameworks.
This is not a benchmarking paper or a feature matrix. It’s a selection guide — structured around real team situations, lifecycle stages, and compliance needs. Skip the 14-metric comparison tables. Answer “which framework should we use?” in under 5 minutes.
Frameworks covered: RAGAS · DeepEval · promptfoo · Langfuse · inspect_ai · OpenAI Evals
Quick Pick
| Your situation | Start here |
|---|---|
| Building a RAG pipeline, need to measure retrieval quality | RAGAS |
| Writing LLM tests in CI/CD, want pytest-style eval assertions | DeepEval |
| Red teaming an LLM, testing adversarial prompts | promptfoo |
| Need production monitoring and observability for LLM traces | Langfuse |
| EU AI Act compliance, safety evaluation, government context | inspect_ai |
| Contributing evals to a shared benchmark registry | OpenAI Evals |
| Need to pick between RAGAS and DeepEval | RAGAS vs DeepEval |
| Need to pick between promptfoo and inspect_ai | promptfoo vs inspect_ai |
| Want all 6 frameworks side-by-side | Full matrix |
| 3-person startup, where do I even start? | By Team Type |
| We need an eval stack, not just one tool | By Lifecycle Stage |
| Building a system subject to EU AI Act | EU AI Act module |
| Need to choose an eval framework for my org | Eval Framework RFP |
| Shipping an AI feature, need a pre-launch eval plan | Pre-Launch Eval Plan |
Why This Exists
Every existing LLM eval comparison is either:
- Vendor-authored (biased toward their product)
- Engineer-authored (feature tables, no decision framing)
- Incomplete (inspect_ai — used by Anthropic, DeepMind, Google for safety evals — is missing from almost every comparison)
- EU AI Act blind (no resource maps framework choice to compliance requirements, despite August 2026 enforcement)
This guide is written from a product team’s perspective. The question isn’t “which framework has the most metrics?” It’s “given our team, our use case, and our risk profile, which framework should we start with on Monday?”
What’s Here
Framework Profiles
Each framework has a consistent profile covering: best-for, not-great-for, setup effort, cost model, EU AI Act relevance, and how it compares to alternatives.
Decision Guides
Three structured guides for choosing:
- By Use Case — RAG, conversational AI, red teaming, production monitoring, safety audit
- By Team Type — startup / ML platform team / regulated enterprise
- By Lifecycle Stage — the insight that you probably need multiple frameworks at different stages
Head-to-Head Comparisons
- RAGAS vs DeepEval — the most common choice question
- promptfoo vs inspect_ai — both do red teaming; which one?
- Full matrix — all 6 × 10 decision criteria
EU AI Act Module
Which frameworks satisfy which Act requirements. Maps Articles 9, 10, 13, 15, 17, Annex IV to concrete framework choices. Includes a 5-phase pre-deployment checklist for Annex III high-risk AI systems.
- EU AI Act overview — enforcement timeline, Annex III categories, articles that create eval obligations
- Framework → Article mapping — master mapping table + red-flag matrix of compliance gaps
- High-risk AI checklist — 5-phase pre-deployment checklist, recommended stacks by risk tier
Templates
Working documents you can copy and use directly.
- Eval Framework RFP — scoring template for choosing eval frameworks for your org; weighted by team type
- Pre-Launch Eval Plan — defines metrics, baselines, thresholds, and sign-off gate before shipping an AI feature
A Note on Scope
This guide covers frameworks for evaluating LLM outputs and behaviors. It does not cover:
- LLM benchmark leaderboards (MMLU, HumanEval, etc.)
- Model selection or comparison
- Data labeling or annotation platforms
Evals move fast. See CHANGELOG.md for framework version tracking.
Contributing
See CONTRIBUTING.md. The most useful contributions are: framework version updates, new use-case examples, and corrections to the EU AI Act mapping.
Maintained by @aadhar-build. Not affiliated with any framework vendor.