Braintrust Alternative? Langfuse vs. Braintrust
This article compares Langfuse and Braintrust, two platforms designed to empower developers to build and maintain AI applications.
What is Braintrust?
Braintrust is an LLM logging and experimentation platform. It provides tools for model evaluation, performance insights, real-time monitoring, and human review. They offer an LLM proxy to log application data and an in-UI playground for rapid prototyping.
Read our view on using LLM proxies for LLM application development here.
What is Langfuse?
Example trace of our public demo
Langfuse is an open-source LLM observability platform that offers comprehensive tracing, prompt management, evaluations, and human annotation queues. It empowers teams to understand and debug complex LLM applications, evaluate and iterate them in production, and maintain full control over their data.
How Do Langfuse and Braintrust Compare?
Both platforms offer functionalities to support developers working with LLMs, but they differ in their features and underlying philosophy.
High level overview
One of the biggest differences between Langfuse and Braintrust is that Langfuse is open-source, making it very easy to self-host and customize according to your specific needs. Being open-source provides transparency, flexibility, and full control over the codebase, allowing developers to inspect, modify, and contribute to the platform. Langfuse is built for production use cases, with a focus on reliability, security, and control over infrastructure.
Braintrust offers innovative in-UI features such as playground, single prompt iteration, and functions which makes it good for experimentation. Langfuse focuses on being best-in-class in the LLM engineering core features: tracing, evaluations, prompt management, and open, stable APIs.
Feature Comparison
| Feature | Langfuse | Braintrust |
|---|---|---|
| Open Source | ✅ Yes (GitHub Repository) | ❌ No |
| Customizability | ✅ High (modify and extend as needed) | ⚠️ Limited (proprietary platform) |
| LLM Proxy | ❌ No (direct integrations) | ✅ Yes (provides AI proxy layer) |
| Production Risks via Proxy | ❌ None introduced by Langfuse | ⚠️ Potential risks (latency, downtime, data privacy concerns) |
| Prompt Management | ✅ Comprehensive (Learn more) | ✅ Yes |
| Evaluation Framework | ✅ Extensive (Learn more) | ✅ Yes |
| Human Annotation Queues | ✅ Built-in (Learn more) | ❌ No |
| LLM Playground | ✅ Built-in (Learn more) | ✅ Yes |
| Self-Hosting | ✅ Full control (Deployment options) | ⚠️ Enterprise Plans |
| Integrations | ✅ Yes (Integrations) | ✅ Yes |
| Security Auditing | ✅ Possible (open-source code) | ❌ Limited transparency |
Langfuse Strengths
- Open-Source: Langfuse’s open-source nature allows developers to inspect, modify, and contribute to the codebase, providing transparency and flexibility.
- No LLM Proxy: Langfuse integrates directly with LLMs without introducing an intermediary proxy, reducing potential risks related to latency, downtime, and data privacy.
- Comprehensive Observability: Offers deep insights into model interactions by tracing not only LLM calls, but also related application processes.
- Self-Hosting Flexibility: Provides self-hosting options, ensuring organizations can maintain full control over data residency, compliance, and security (Learn more).
Braintrust Considerations
- Proprietary Platform: Braintrust is not open-source, which limits transparency into its operations and restricts deep customization.
- LLM Proxy: Platform is focussed on an LLM proxy to access models from providers like OpenAI and Anthropic, introducing potential production risks:
- Latency and Uptime Risks: The proxy layer can introduce additional points of failure or performance bottlenecks.
- Data Privacy Concerns: Routing data through an external proxy may raise compliance issues, particularly for sensitive data.
- Dependency on Third-Party Services: Changes in proxy service terms or availability can impact application reliability.
- Self-Hosting Limitations: Although Braintrust offers self-hosting options in their enterprise plan, the platform remains closed-source, which may not fully align with organizations seeking complete control over their infrastructure and data.
Why Choose Langfuse Over Braintrust?
- Production Ready: Langfuse is designed for production use cases, with a focus on reliability, security, and control over infrastructure.
- Transparency and Control: With Langfuse being open-source, you have full transparency into the platform’s operations and can tailor it to your specific needs.
- Flexible Integration: Langfuse integrates directly with popular LLM frameworks and SDKs, fitting seamlessly into your existing workflows.
- Reduced Production Risks: By deciding not to use an LLM proxy, Langfuse eliminates potential points of failure and security risks associated with proxy layers.
Conclusion
Both Langfuse and Braintrust offer valuable solutions for developers building AI applications with large language models.
Langfuse’s open-source nature provides transparency and flexibility, allowing organizations to customize the platform and maintain full control over their data—an essential factor for production environments where security and compliance are critical. Its direct integration with LLMs, without using LLM proxies, minimizes potential risks related to latency, uptime, and data privacy.
Braintrust offers an integrated suite of tools, including a playground for rapid prototyping and A/B testing features, which may appeal to teams during the experimentation phase.
Learn More About Langfuse
- Get Started with Langfuse: Documentation Overview
- Deployment Options: Self-Hosting Guide
- Integrations: Supported SDKs and Frameworks
- Prompt Management: Managing Prompts in Production
- Evaluation Tools: Evaluating Model Outputs
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