---
title: 'Logfire vs LangSmith: Production-Grade AI Observability'
description: Compare Logfire and LangSmith for AI observability. See why teams choose the Pydantic stack for type-safe, production-grade AI development at a fraction of the cost.
canonical: https://pydantic.dev/logfire/vs-langsmith
last-reviewed: 2026-07-13
---

> Markdown version of [Logfire vs LangSmith](https://pydantic.dev/logfire/vs-langsmith) — the canonical HTML page.
>
> Site index: [/llms.txt](https://pydantic.dev/llms.txt)

---

# Logfire vs LangSmith

Both provide observability for AI applications, but Logfire covers your entire stack: LLM calls alongside database queries, API requests, and background jobs, while LangSmith focuses on the AI layer. At a fraction of the cost, Logfire saves you 40x more than LangSmith at 50 million spans per month with 5 users. The Pydantic stack (Pydantic AI + 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.

[Try Logfire free](https://logfire.pydantic.dev/)

## Quick comparison

| Feature                      | Logfire                                                              | LangSmith                                                                       |
| ---------------------------- | -------------------------------------------------------------------- | ------------------------------------------------------------------------------- |
| Observability scope          | Full-stack (AI + database + APIs + infra)                            | LLM and agent-focused                                                           |
| Foundation                   | Pydantic (560M+ downloads/month), type-safe                          | LangChain, flexible/dynamic                                                     |
| Structured Outputs           | Schema-validated responses                                           | String parsing, partial validation                                              |
| Standards                    | OpenTelemetry native - instrumentation fully portable                | Proprietary-first; OTel supported but some features require LangSmith's own SDK |
| 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                                           |

## Pricing comparison

| Workload                | LangSmith | Logfire        | Savings  |
| ----------------------- | --------- | -------------- | -------- |
| 1 user, 5M spans/mo     | ~$514     | $0 (free tier) | ~$514/mo |
| 5 users, 50M spans/mo   | ~$5,170   | ~$129          | ~40x     |
| 20 users, 500M spans/mo | ~$50,755  | ~$1,229        | ~41x     |

\*Logfire Cloud Team or Growth plans (base + $2/million spans). LangSmith Plus plan pricing ($39/seat + $2.50/1K trace overage).

## 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 an 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 backend that supports OTel — you're not locked into Logfire.

## 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

**How much cheaper is Logfire compared to LangSmith?**
For a 5-person team processing 50M spans/month, Logfire costs ~$129 vs LangSmith's ~$5,170. At 500M spans with 20 users, LangSmith exceeds $125,000 while Logfire is ~$1,229. LangSmith's per-seat plus per-trace pricing model means costs escalate fast — customers routinely sample down to 0.1% of traces to manage bills, which defeats the purpose of observability for probabilistic AI failures.

**Can I use Logfire with LangChain?**
Yes. Logfire works with any AI framework including LangChain, Pydantic AI, Vercel AI SDK, LlamaIndex, and plain OpenAI. Logfire is framework-agnostic — your choice of AI framework doesn't lock you into a specific observability tool.

**What about LangSmith's dataset and eval features?**
LangSmith has more mature web-UI dataset and annotation workflows. Logfire's approach is code-first via Pydantic Evals — evaluate any Python function (not just LLM calls), version control your evals, run locally or in CI/CD, and integrate with any testing framework. Different approaches for different teams.

**We're already on LangChain — is migrating hard?**
LangChain and LangSmith don't have to be a bundle. Many teams run Logfire alongside LangChain. The concepts transfer — both support OTel at their core. The migration path is instrumentation changes, not a full rewrite. And many teams have already moved off LangChain entirely once they hit its complexity ceiling.

**Does LangSmith have better LangGraph support?**
Yes. LangSmith shows full graph state by default for LangGraph pipelines — that's a genuine advantage if deep LangGraph debugging is a day-one requirement. Worth considering how much of your stack actually depends on LangGraph vs other approaches.

## Ready to switch from LangSmith?

Get started with 10 million free spans per month. No credit card required.

Start free: https://logfire.pydantic.dev/ — [View pricing](https://pydantic.dev/pricing)

---

See also: [Logfire alternatives](https://pydantic.dev/logfire/alternatives) ([markdown](https://pydantic.dev/logfire/alternatives.md)), [Pydantic Logfire](https://pydantic.dev/logfire) ([markdown](https://pydantic.dev/logfire.md)), and [pricing](https://pydantic.dev/pricing.md).