DeepEval vs Promptfoo
A side-by-side comparison of DeepEval and Promptfoo, two Eval tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | DeepEval | Promptfoo |
|---|---|---|
| Category | Eval | Eval |
| Pricing (differs) | FREEMIUM | FREE |
| License (differs) | Open core | Open source |
| Deployment (differs) | Hybrid | — |
| Platforms (differs) | CLI, API | CLI, macOS, Windows, Linux |
| Model support | BYO key / model | BYO key / model |
| Vendor (differs) | Confident AI | Promptfoo |
| Capabilities (differs) |
|
|
The honest brief
DeepEval
Write LLM evals as Pytest-style assertions and run them in CI, backed by 50+ metrics across RAG, agents, and safety.
- Assertions run in your CI pipeline
- Metrics for RAG, agents, and safety
- Bring any judge model (BYO key)
- Integrates LangChain/CrewAI/OpenAI
- LLM-as-judge adds cost
- Dashboards need paid Confident AI
- Judge metrics can be noisy
Promptfoo
Define evals in plain YAML and run one goldset across models in CI — a prompt regression fails the build like any other test.
- YAML-driven, version-controllable evals
- Runs in CI, model-agnostic
- Goldsets and rubric scoring
- Also does red-teaming/security scans
- CLI-first, less of a hosted UI
- Teams may want managed dashboards
- Config sprawl on large eval suites
When to pick which
Both cover LLM evaluation.
Pick DeepEval if you need LLM observability.
- LLM observability (secondary capability)
Pick Promptfoo if you need Red-teaming and Guardrails.
- Red-teaming (primary capability)
- Guardrails (secondary capability)
They also differ on:
- Pricing
- FREEMIUM · FREE
- License
- Open core · Open source
- Platforms
- CLI, API · CLI, macOS, Windows, Linux