Braintrust vs Langfuse

Detailed side-by-side comparison to help you choose the right tool

Braintrust

🔴Developer

AI evaluation

AI evals, prompt iteration and observability platform

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Starting Price

Free

Langfuse

🔴Developer

Open-source LLM observability

open-source LLM observability, tracing, prompt and eval platform

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Starting Price

Free

Feature Comparison

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FeatureBraintrustLangfuse
CategoryAI evaluationOpen-source LLM observability
Pricing Plans91 tiers38 tiers
Starting PriceFreeFree
Key Features
  • Workflow Runtime
  • Tool and API Connectivity
  • State and Context Handling
  • Hierarchical Tracing & Agent Debugging
  • Production Prompt Management & Versioning
  • LLM-as-Judge Evaluation Framework

💡 Our Take

Choose Braintrust if you need automated prompt optimization through the Loop agent and have budget for $25/seat/month — the automation pays for itself within 2-3 months for active teams. Choose Langfuse if you're budget-conscious, want full data sovereignty through self-hosting, or only need observability without automated improvement. Langfuse is the better pick for solo developers and open-source-first teams; Braintrust wins for production teams iterating on prompts weekly.

Braintrust - Pros & Cons

Pros

  • Strong fit for production AI teams because traces, datasets and experiments live in one workflow
  • Starter is $0/month with 1 GB processed data, 10k scores and 14-day retention
  • Pro is $249/month with 5 GB processed data, 50k scores, 30-day retention and priority support
  • Framework agnostic with Python, TypeScript, Go, Ruby and C# SDKs

Cons

  • The value shows up after you have real traffic or evaluation datasets; it may be overkill for prototypes
  • Data and score overages require attention on high-volume products
  • Enterprise deployment choices need procurement and security review

Langfuse - Pros & Cons

Pros

  • Open-source and self-hostable, which is valuable for teams that do not want observability locked fully in a SaaS.
  • Clear fit for prompt lifecycle management: versioning, fetching, traces, datasets, and evals in one workflow.
  • MCP support is useful for coding agents that need to inspect or update observability assets safely.
  • Cloud pricing starts low enough for serious prototypes while still offering enterprise controls.

Cons

  • Unit-based pricing requires teams to understand how traces and observations translate into monthly spend.
  • Self-hosting reduces vendor lock-in but adds ClickHouse/database operations and upgrade responsibility.
  • Not a full application monitoring suite; you still need product analytics and infrastructure observability.

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🔒 Security & Compliance Comparison

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Security FeatureBraintrustLangfuse
SOC2✅ Yes✅ Yes
GDPR✅ Yes✅ Yes
HIPAA✅ Yes✅ Yes
SSO✅ Yes✅ Yes
Self-Hosted❌ No
On-Prem❌ No✅ Yes
RBAC✅ Yes✅ Yes
Audit Log✅ Yes
Open Source❌ No✅ Yes
API Key Auth✅ Yes✅ Yes
Encryption at Rest✅ Yes
Encryption in Transit✅ Yes
Data ResidencyUS, EU, SELF-HOSTED
Data Retentionconfigurableconfigurable
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