Comparison
Logfire vs LangSmith
The Pydantic stack (PydanticAI + Logfire) brings real software engineering practices to AI development. Type safety, validation, structured outputs—and observability that sees your entire application. All at a fraction of the cost.
Feature Comparison
Quick comparison
| Feature | Logfire | LangSmith |
|---|---|---|
| Observability scope | Full-stack (AI + databases, APIs, infra) | LLM-focused |
| Foundation | Pydantic (560M+ downloads/month), type-safe | LangChain, flexible/dynamic |
| Structured Outputs | Schema-validated responses | String parsing, partial validation |
| Standards | OpenTelemetry native | Proprietary-first; OTel supported but not default |
| Query interface | SQL (Postgres-compatible) | Proprietary DSL + UI filters |
| Framework support | Python, JS/TS, Rust + any OTel language | Built for LangChain and LangGraph. Support for OpenLLMetry semantics |
| Free tier | 10M spans/month (1 user) | 5,000 traces/month (1 user) |
| Pricing model | $2/million spans + $25/seat (if more than 5 seats) | $39/seat + $2.50/1K trace overage |
| Data retention | 30 days default; 90 days for Growth plan | 14 days default; 400 days doubles cost |
| Dataset/annotation workflows | Datasets and eval results via UI + Pydantic Evals (code-first) | Mature web UI with annotation queues and human feedback |
| Graph state visibility | Code-first Mermaid diagrams (Pydantic Graph) + full execution traces | UI graph rendering for LangGraph runs |
Cost Savings
Pricing comparison
| Workload | LangSmith | Logfire | Savings |
|---|---|---|---|
| 1 user, 5M spans/mo | ~$1,238 | $0 (free tier) | ~$1,238/mo |
| 5 users, 50M spans/mo | ~$5,170 | ~$129 | ~40x |
| 20 users, 500M spans/mo | ~$125,755 | ~$1,229 | ~100x |
*Logfire Cloud Team or Growth plans (base + $2/million spans). LangSmith Plus plan pricing ($39/seat + $2.50/1K trace overage).
Key Differences
Why teams choose Logfire
Better Economics
All of the above, at a fraction of the cost. At scale, Logfire can be 40-100x less expensive than LangSmith. This isn't about being the “budget option“, it's about a architecture that passes savings to you.
Full-stack observability, not LLM-only
LangSmith shows you what your LLM did. Logfire shows you what your LLM did AND what happened in your databases, APIs, and services. When your AI agent fails, was it the model, the data pipeline, or the downstream API? You need the complete picture in one trace.
Standard SQL, no proprietary query language
Logfire uses standard (PostgreSQL) SQL throughout. SQL is one of the things AI coding assistants do best. Point your agent at your Logfire data via our MCP server and it can answer arbitrary questions about production behavior that no proprietary query language could support.
Open Standards, no lock-in
Logfire is built on OpenTelemetry with 100% GenAI semantic convention alignment. Your instrumentation is portable. Pydantic AI itself works with ANY observability backendthat supports OTel — you're not locked into Logfire.
Decision Guide
Which should you choose?
Choose Logfire if...
- ✓You need type safety, validation, and real software engineering practices
- ✓You want AI observability AND system observability in one tool
- ✓You want standard SQL queries instead of a proprietary interface
- ✓You're scaling and LangSmith costs are forcing trace sampling
- ✓You use (or plan to use) multiple AI frameworks
- ✓You want OpenTelemetry-native instrumentation with no vendor lock-in
Choose LangSmith if...
- •You're deeply invested in LangChain/LangGraph and migration isn't on the table
- •You need native LangGraph graph state visibility for complex pipelines
- •You value LangChain's flexibility for rapid experimentation
FAQ
Common questions
Ready to switch from LangSmith?
Get started with 10 million free spans per month. No credit card required.