Langfuse alternative: Braintrust vs. Langfuse for LLM observability

Langfuse and Braintrust both provide LLM observability and tracing. However, they differ significantly in scope and approach.

Langfuse is an open-source observability platform focused on tracing, monitoring, and analytics. It provides building blocks for LLM development that teams assemble into custom workflows.

Braintrust is an end-to-end AI development platform that connects observability directly to systematic improvement. Production traces become evaluation cases with one click. Eval results appear on every pull request through CI/CD. PMs and engineers iterate together in a unified workspace without handoffs.

The core difference: Langfuse shows you what happened in production. Braintrust shows you what happened and helps you fix it to prevent regressions before they ship.

Langfuse

Langfuse is an open-source LLM observability platform that provides comprehensive tracing and monitoring for LLM applications. It helps teams understand what their AI systems are doing in production through detailed traces and analytics dashboards.

What Langfuse is used for

• Observability and tracing: Detailed tracking of LLM calls and application behavior in production
• Analytics: Usage monitoring and cost tracking across your LLM infrastructure
• Open source flexibility: MIT licensed with extensive self-hosting documentation and control over your infrastructure
• Building blocks approach: Provides core observability primitives that teams customize and extend
• Best for: Teams with DevOps resources who want to build custom evaluation and CI/CD infrastructure on top of observability

Key consideration

Langfuse stops at observability. Converting production insights into systematic improvements requires custom engineering: building scripts to transform traces into eval datasets, writing evaluation code, configuring CI/CD pipelines, and creating collaboration workflows between PMs and engineers.

Braintrust

Braintrust is an AI development platform built around systematic improvement. It provides the complete workflow from observability to evaluation, with production data driving continuous quality gains.

What Braintrust is used for

• Observability to improvement: Production traces become eval cases with one click. Your next eval run checks if you fixed the issue.
• CI/CD integration: Eval results on every pull request via turnkey GitHub Action. Set gates that block regressions from merging.
• Unified PM/engineering workflow: PMs iterate on prompts in the Playground with real eval results, then engineers pull production-ready code directly. No handoffs.
• AI proxy: Access multiple models from OpenAI, Anthropic, Google, and more through one OpenAI-compatible API. Every call traced and cached.
• End-to-end agent simulation: Evaluate complete multi-step workflows, not just individual prompts.
• Production scale: Sub-second queries on millions of traces.
• Proven results: Notion achieved 10x productivity gain. Zapier went from sub-50% to 90%+ accuracy in 2-3 months. Coursera maintains 90% satisfaction at 45x scale.

Best for

Teams shipping AI products to real users who need quality improvements, not just visibility. Companies that want to catch regressions before users do and iterate in minutes instead of days.

Core feature comparison

Workflow comparison

Langfuse workflow for production issues:

1. See failure in production traces
2. Manually copy data to eval dataset
3. Write evaluation code
4. Build custom CI/CD integration
5. Configure quality gates yourself

Braintrust workflow for production issues:

1. See failure in production traces
2. Click to add to eval dataset
3. Next eval run shows if fixed
4. PR shows eval results
5. Regression blocked if quality drops

The practical difference: Braintrust eliminates the custom engineering required to connect observability to improvement. What takes days of infrastructure work with Langfuse is built into Braintrust.

Conclusion

Choose Braintrust when:

• You ship AI products to real users and need systematic quality improvement, not just visibility
• You want production failures to become eval cases instantly without manual data wrangling
• You need eval results on every PR to catch regressions before users see them
• PMs and engineers should iterate together without handoffs between tools
• You want to test multiple models without rewriting integration code
• You're scaling to millions of traces and need sub-second query performance
• Time to market matters—you ship iterations in minutes, not days

Choose Langfuse when:

• You primarily need observability and trace logging
• You have DevOps resources dedicated to building evaluation and CI/CD infrastructure
• Open-source transparency is a hard requirement
• Engineering owns the entire AI workflow end-to-end with no PM collaboration needed
• You're comfortable self-hosting ClickHouse, Redis, and S3
• You're in research/experimentation phase without immediate production needs

For most teams shipping production AI, Braintrust provides the complete workflow that Langfuse requires you to build yourself.

Frequently asked questions

Which tool is best for CI/CD integration?

Braintrust. It provides a turnkey GitHub Action that runs evals and displays results on every pull request. Configure it once in a few lines of YAML, and every code change includes eval results. You can set quality gates that block merges if performance degrades. Langfuse requires building this integration yourself—writing custom scripts to run evaluations, connecting them to your CI/CD pipeline, and configuring quality gates. This typically takes weeks of engineering work.

Is Braintrust or Langfuse better for product managers?

Braintrust. PMs can iterate on prompts in the Playground with live evaluation results, compare variations side-by-side, and share results via URL. When they find a winning prompt, engineers pull that exact code into production. No handoffs or translation required. Langfuse is developer-focused. PMs prototype ideas in a basic playground, then hand requirements to engineering who rebuild the solution in code. Cross-functional collaboration requires more engineering involvement.

Can I self-host Braintrust?

Hybrid deployment is available for enterprise customers. Langfuse provides documented self-hosting for all users with control over ClickHouse, Redis, and S3 infrastructure. If self-hosting is a requirement and you're not at enterprise scale, Langfuse is better suited.

How does pricing compare?

Braintrust includes 1M free spans vs. Langfuse's 50k free units. At Pro tier, Braintrust is $249/mo vs. Langfuse's $199/mo. Braintrust's price includes CI/CD integration, AI proxy access to multiple models, unified PM/engineering workspace, and tools for creating evals from production traces. With Langfuse, you build these capabilities yourself. Factor in engineering time when comparing—most teams spend weeks building what Braintrust includes.

Get started

Ready to ship AI products with confidence? Get started with Braintrust for free—1 million trace spans included, no credit card required.