An honest comparison of features, pricing, self-hosting, and framework compatibility

Two strong tools for the same problem

Both LangSmith and LangFuse solve the same core problem: making LLM applications observable. Both offer tracing, evaluations, and prompt management. But they have different origins, different strengths, and different price points. This guide helps you pick the right one for your situation.

Quick summary

Dimension LangSmith LangFuse
Origin LangChain team product Open-source, independent startup
Self-hostable No (managed only) Yes (MIT license)
Free tier Developer plan (limited) Cloud free tier + unlimited self-hosted
LangChain integration Native, zero-config Via LangChain callback
Framework-agnostic Yes (with @traceable) Yes (OpenAI SDK, etc.)
Prompt management LangSmith Hub Built-in prompt editor + versioning
Evaluations LLM-as-judge + custom LLM-as-judge + custom + human annotation
Datasets Yes Yes
A/B prompt testing Limited First-class feature
Cost tracking Automatic for LangChain Manual pricing config

LangSmith strengths

1. LangChain/LangGraph native integration

If you are already using LangChain or LangGraph, LangSmith is the path of least resistance. Set two environment variables and every chain, agent, retriever, and tool call is automatically traced with full context. No manual instrumentation required.

2. LangSmith Hub for prompt sharing

Hub is a registry of community and private prompts. Pull production-ready prompts, fork them, version them, and pin to specific commits. This workflow is tighter than LangFuse's prompt editor for teams already in the LangChain ecosystem.

3. Integrated playground

Run any traced prompt directly from the trace view, modify the prompt, and compare outputs side-by-side. This tightens the iteration loop when debugging a specific failure.

LangFuse strengths

1. Self-hostable

LangFuse is MIT-licensed and can be deployed on your own infrastructure with a single docker-compose command. For teams with data residency requirements, regulated industries, or large volumes where SaaS pricing is prohibitive, this is a decisive advantage.

# Self-host LangFuse in 2 minutes
git clone https://github.com/langfuse/langfuse
cd langfuse
docker-compose up -d
# Access at http://localhost:3000

2. Framework-agnostic by design

LangFuse has official integrations for OpenAI SDK, Anthropic SDK, LangChain, LlamaIndex, Haystack, DSPy, and more. If your stack is not 100% LangChain, LangFuse fits more naturally.

3. Human annotation and A/B testing

LangFuse has a built-in annotation queue — human reviewers can label traces as good/bad directly in the UI. It also supports prompt A/B testing with statistical significance tracking, which LangSmith does not currently match.

Pricing comparison

Plan LangSmith LangFuse
Free Developer plan, rate limited Hobby cloud + unlimited self-hosted
Paid SaaS From $39/month From $59/month
Self-hosted Not available Free (MIT license)
Enterprise Custom Custom 
LangSmith pricing is based on trace volume. If you trace every LLM call in a high-volume production app, costs can add up quickly. LangFuse self-hosted has no per-trace cost.

Integration code comparison

LangSmith — auto-trace LangChain apps

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=ls__...
# That's it — all LangChain calls are traced

LangFuse — LangChain callback

from langfuse.callback import CallbackHandler
 
handler = CallbackHandler(
    public_key='pk-lf-...',
    secret_key='sk-lf-...',
    host='http://localhost:3000',  # or cloud URL
)
 
# Pass to any LangChain chain
result = chain.invoke({'question': 'Hello'}, config={'callbacks': [handler]})

LangFuse — direct OpenAI tracing

from langfuse.openai import openai  # drop-in replacement
 
# Replace: from openai import OpenAI
# With:    from langfuse.openai import openai
 
client = openai.OpenAI()
response = client.chat.completions.create(
    model='gpt-4o-mini',
    messages=[{'role': 'user', 'content': 'Hello'}],
    # LangFuse metadata
    name='chat-completion',
    user_id='user-123',
)
# Automatically traced in LangFuse — no other changes

Decision guide

Your situation Choose
Your stack is 100% LangChain/LangGraph LangSmith
You need self-hosting for compliance/cost LangFuse
Your stack mixes frameworks (OpenAI + LlamaIndex + custom) LangFuse
You need human annotation workflows LangFuse
You want prompt A/B testing LangFuse
You want integrated playground for debugging LangSmith
You want LangSmith Hub community prompts LangSmith
Budget is a major constraint at scale LangFuse (self-hosted)
Team is new to LLM observability Either — both have good DX

Can you use both?

Yes. Some teams use LangSmith for local development and debugging (the playground is excellent), and LangFuse self-hosted in production where data residency and cost matter. The instrumentation code is different but both support @traceable-style decoration.

Start with whichever one your framework suggests. Migrate only if you hit a specific limitation — both tools have sufficient overlap that switching later is feasible but not trivial.