Langfuse vs LangSmith

Overview

Langfuse and LangSmith are both LLM observability platforms that help teams trace, debug, and evaluate their LLM applications. However, they take fundamentally different approaches: Langfuse is open-source and self-hostable, while LangSmith is a commercial SaaS platform from LangChain.

This comparison helps you choose the right observability platform based on your infrastructure preferences, budget, and feature requirements.

Comparison Table

Aspect	Langfuse	LangSmith
License	Open-source (Apache 2.0)	Commercial (SaaS)
Hosting	Self-hosted or Cloud	SaaS only
Cost	Free (self-hosted) or paid Cloud	Paid subscription
Tracing	✅ Full request tracing	✅ Full request tracing
Evaluation	✅ Custom evaluators	✅ Built-in evaluators
Datasets	✅ Dataset management	✅ Dataset management
Prompt Management	✅ Versioning and A/B testing	✅ Prompt hub
Cost Tracking	✅ Per-request cost tracking	✅ Detailed cost analytics
Self-Hosting	✅ Docker deployment	❌ Not available
Integrations	LangChain, LlamaIndex, OpenAI	LangChain, many more
Support	Community + paid plans	Enterprise support

Deep Dive

Langfuse: Open-Source Observability

Philosophy: Open-source LLM engineering platform with self-hosting option.

Strengths:

• Open-source with no vendor lock-in
• Self-hostable for data privacy and compliance
• Comprehensive tracing and debugging
• Strong evaluation and dataset features
• Active community and frequent updates
• Good documentation and examples

Weaknesses:

• Requires infrastructure to self-host
• Some advanced features on paid plan
• Smaller ecosystem than LangSmith
• Learning curve for evaluation setup

Best Use Cases:

• Teams with data privacy requirements
• Organizations wanting full data control
• Budget-conscious projects
• Companies with existing DevOps infrastructure

LangSmith: Commercial Observability

Philosophy: Enterprise-grade LLM observability from the LangChain team.

Strengths:

• Seamless LangChain integration
• Polished, user-friendly interface
• Extensive built-in evaluators
• Large ecosystem and integrations
• Enterprise support and SLAs
• Regular feature updates

Weaknesses:

• Paid subscription (can be expensive)
• Data stored on LangChain servers
• Vendor lock-in concerns
• Limited self-hosting options

Best Use Cases:

• Teams already using LangChain
• Enterprises needing support and SLAs
• Projects with budget for SaaS tools
• Teams wanting polished UX out of the box

Feature Comparison

Tracing

Both platforms provide comprehensive tracing:

Feature	Langfuse	LangSmith
Request tracing	✅	✅
LLM call visualization	✅	✅
Token usage tracking	✅	✅
Latency metrics	✅	✅
Error tracking	✅	✅
Custom metadata	✅	✅

Evaluation

Feature	Langfuse	LangSmith
Custom evaluators	✅	✅
Built-in metrics	✅	✅
Dataset testing	✅	✅
A/B testing	✅	✅
Human feedback	✅	✅
Automated evals	⚠️ Limited	✅ Extensive

Deployment

Feature	Langfuse	LangSmith
Self-hosted	✅ Docker	❌
Cloud SaaS	✅ Paid	✅ Included
On-premise	✅	❌
Air-gapped	✅	❌

Pricing

Langfuse

Plan	Price	Features
Self-hosted	Free	Full features, your infrastructure
Cloud Free	$0	Limited usage, cloud-hosted
Cloud Pro	$49/user/month	Unlimited usage, priority support
Enterprise	Custom	SSO, SLA, dedicated support

LangSmith

Plan	Price	Features
Hobby	Free	Limited traces, 1 user
Pro	$49/user/month	Unlimited traces, team features
Team	$99/user/month	Advanced features, SSO
Enterprise	Custom	SLA, dedicated support

Integration Example

Langfuse

from langfuse import Langfuse

langfuse = Langfuse(
    public_key="pk-lf-...",
    secret_key="sk-lf-...",
    host="https://cloud.langfuse.com"
)

trace = langfuse.trace(name="chat-session")
trace.generation(
    name="gpt-4o-response",
    model="gpt-4o",
    input={"message": "Hello"},
    output={"message": "Hi there!"}
)

LangSmith

from langsmith import Client

client = Client(api_key="lsv2_pt_...")

run = client.create_run(
    name="chat-response",
    inputs={"message": "Hello"},
    outputs={"message": "Hi there!"},
    execution_order=1,
    traceable=True
)

Verdict

Scenario	Recommendation
Need data privacy / self-hosting	Langfuse
Already using LangChain	LangSmith (easier integration)
Budget-conscious	Langfuse (self-hosted is free)
Enterprise with support needs	LangSmith
Compliance requirements (HIPAA, etc.)	Langfuse (self-hosted)
Small team, quick setup	LangSmith (SaaS is easier)
Want to avoid vendor lock-in	Langfuse

Migration Path

You can use both platforms simultaneously during migration:

# Use both for comparison
from langfuse import Langfuse
from langsmith import Client

langfuse = Langfuse()
langsmith = Client()

# Trace to both
langfuse.trace(name="test")
langsmith.create_run(name="test", ...)

Resources

• Langfuse Documentation
• LangSmith Documentation
• Langfuse GitHub
• LangSmith Pricing