DeepEval vs Judgment Labs
A side-by-side comparison of DeepEval and Judgment Labs, two Eval tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | DeepEval | Judgment Labs |
|---|---|---|
| Category | Eval | Eval |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms (differs) | CLI, API | Web, API |
| Model support | BYO key / model | BYO key / model |
| Vendor (differs) | Confident AI | Judgment Labs |
| 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
Judgment Labs
Scores entire agent trajectories — tool calls, memory, long reasoning — and turns that production data into RL/SFT post-training, not just pass/fail evals.
- Open-source judgeval framework (Apache-2.0)
- Trajectory-level, not just output, evals
- Feeds production data into RL/SFT
- MCP integration with coding agents
- Hosted platform pricing not public
- Young company (founded 2026)
- Geared to complex 'deep' agents
When to pick which
Both cover LLM evaluation and LLM observability.
Pick Judgment Labs if you need MCP server.
- MCP server (secondary capability)
They also differ on:
- Platforms
- CLI, API · Web, API