LangSmith vs MLflow (2026)
Updated March 27, 2026
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Overview
Rating
10.0 / 10
Rating
10.0 / 10
Best For
Teams building agents on LangChain or LangGraph who want tracing, evals, and deployment from one vendor
Best For
ML engineers and AI teams, especially those in the Databricks ecosystem
Product Summary
LangSmith is LangChain's platform for tracing, evaluating, and deploying AI agents. Compare its features, pricing, pros and cons, and alternatives.
Product Summary
MLflow is an open-source AI engineering platform for agents, LLMs, and ML models, with tracing, evals, prompt management, and an AI gateway.
Starting Price
Free
Starting Price
Free
Free Trial
Yes
Free Trial
No
Free Version
Yes
Free Version
Yes
Website
Website
Key features
Core capabilities each platform advertises.
- Native tracing for LangChain and LangGraph
- OpenTelemetry and multi-language SDKs
- Offline and online evaluations
- Annotation queues and datasets
- Prompt Hub with versioning
- OpenTelemetry-based tracing for LLMs and agents
- 50+ built-in eval metrics and LLM judges
- Prompt versioning and automatic prompt optimization
- AI Gateway with routing, rate limiting, and fallbacks
- Agent server for deploying agents on FastAPI
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Native LangChain and LangGraph tracing with no extra instrumentation
- Evals, datasets, and annotation queues sit in the same workspace as traces
- Tracing, deployment, and sandboxes from one vendor
- Framework-agnostic SDKs and OpenTelemetry support
Cons
- Per-seat pricing on Plus, and the free plan allows one seat
- 14-day base retention, with longer retention billed at a higher rate
- LLM Gateway is still in beta
- Self-hosting and hybrid deployment are Enterprise only
Pros
- Apache 2.0 license with Linux Foundation governance
- Tracing, evals, prompts, and a gateway in one open-source project
- Covers classic ML and GenAI in the same platform
- Managed options on Databricks, SageMaker AI, and Azure ML
- Auth and role-based access built into the open-source server
Cons
- Self-hosting means running the tracking server, database, and artifact store yourself
- Auth and role-based access are opt-in and need configuring on self-hosted servers
- Managed versions bill through a cloud platform's compute rather than a standalone plan
- The broader ML scope can mean more to learn for teams that only need LLM tooling
LangSmith or MLflow — which should you choose?
Choose LangSmith if you wantChoose if you want
- Debugging LangChain and LangGraph agents
- Evaluating agent output against datasets
- Human review through annotation queues
- Deploying long-running agents
Choose MLflow if you wantChoose if you want
- Tracing and evaluating LLM apps and agents
- Versioning and optimizing prompts
- Routing LLM traffic through a self-hosted gateway
- Tracking classic ML experiments alongside GenAI work
- Running AI tooling inside Databricks
Compare LangSmith and MLflow on your own traffic
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