DeepEval vs LangSmith
A side-by-side comparison of DeepEval and LangSmith, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | DeepEval | LangSmith |
|---|---|---|
| Category (differs) | Eval | Observability |
| Pricing | FREEMIUM | FREEMIUM |
| License (differs) | Open core | Proprietary |
| Deployment (differs) | Hybrid | Cloud |
| Platforms (differs) | CLI, API | API, Web |
| Model support (differs) | BYO key / model | Model-agnostic |
| Vendor (differs) | Confident AI | LangChain |
| 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
LangSmith
Deepest native LangChain/LangGraph tracing — but cloud-only, where Langfuse lets you self-host the same.
- Native LangChain/LangGraph tracing
- Works standalone via SDKs
- Datasets + eval orchestration
- Prompt playground built in
- Closed source, cloud-only
- Self-host is Enterprise-only
- Best value inside LangChain stack
When to pick which
Both cover LLM evaluation and LLM observability.
Pick LangSmith if you need Prompt management.
- Prompt management (secondary capability)
They share capabilities, but each leads with different ones as a headline job:
DeepEval is built around LLM evaluation.
- LLM evaluation (primary capability)
LangSmith is built around LLM observability.
- LLM observability (primary capability)
They also differ on:
- License
- Open core · Proprietary
- Deployment
- Hybrid · Cloud
- Platforms
- CLI, API · API, Web