Comparison

Langfuse: Open Source Agent Evals & Observability vs LangSmith

Langfuse: Open Source Agent Evals & Observability and LangSmith both land on Depends.
langfuse.com
Buy if you ship LLM apps to production and need tracing, prompt management, and evals in one place; skip if you don't build AI features.
Read the full report
smith.langchain.com
Buy if your team runs real LLM agents in production and needs tracing, evals, and failure analysis at scale.
Read the full report
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Langfuse: Open Source Agent Evals & Observability versus LangSmith| Compare | Langfuse: Open Source Agent Evals & Observability | LangSmith |
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| Verdict | Depends | Depends |
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| Best for | Teams shipping LLM apps to production | Teams shipping LLM agents to production |
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| Who it's not for | Teams not building LLM-powered products | Hobby projects or simple chatbots . heavy overkill |
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| Privacy | No known public vulnerabilities found in the sources reviewed. | Two critical 2026 vulnerabilities disclosed: an auth bypass (CVE-2026-25750) and an SDK deserialization flaw (CVE-2026-40190). |
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| Support quality | No support-quality evidence found | No support evidence in sources |
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| Public sentiment | No independent user reviews surfaced in reviewed sources; all claims are vendor-published adoption and feature statements. | Users praise detailed tracing and debugging but grumble about new pricing, reliability, and ecosystem lock-in. |
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| Biggest gotcha | Free tier caps at 50k observations/month; high-volume agents outgrow it fast, costs scale with volume | Reddit users report Plus tier in Europe makes you non-compliant |
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Pick Langfuse: Open Source Agent Evals & Observability when
- Teams shipping LLM apps to production
- Agent debugging and eval workflows
- LLM cost and latency tracking
- Regulated teams needing HIPAA-ready hosting
When Langfuse: Open Source Agent Evals & Observability is not a fit
- Teams not building LLM-powered products
- Anyone wanting plug-and-play without code changes
- Buyers needing published paid-tier pricing upfront
- Small projects fine with plain log files
Pick LangSmith when
- Teams shipping LLM agents to production
- LangChain/LangGraph developers
- Agent debugging and eval workflows
- AI platform teams at scale
When LangSmith is not a fit
- Hobby projects or simple chatbots . heavy overkill
- EU teams on Plus tier with strict compliance needs
- Budget-constrained teams wanting self-hosted open source
- Apps with negligible agent traffic or no eval needs
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