Braintrust is an AI observability platform that helps developers build reliable AI products through systematic evaluation and monitoring. It allows teams to test AI applications against real data, compare model performance side-by-side, and track production quality in real-time. Engineers can write code-based tests while product managers prototype in the UI, making it easy for teams to collaborate on improving AI applications.

• Automated Evaluations: Test your AI with real datasets and scoring mechanisms to measure performance improvements or regressions across different prompts and models.
• Production Monitoring: Track live model responses with real-time alerts when quality drops or incorrect outputs increase beyond your thresholds.
• Side-by-Side Comparisons: Compare scores of different prompts and models with visual diffs to understand exactly why one version performs better.
• Dataset Management: Capture rated examples from staging and production, incorporating them into versioned datasets stored in the cloud without breaking existing evaluations.
• Multi-Role Collaboration: Engineers write code-based tests while product managers prototype in the UI, with shared visibility for debugging and reviewing results.
• Real-Time Traces: Visualize model execution traces to understand how your AI system processes requests and identify bottlenecks.
• CI/CD Integration: Run evaluations in your continuous integration pipeline to catch regressions before deployment.
• Role-Based Access Control: Manage team permissions with org-level access controls and project isolation for security compliance.

• Free Tier Available: Offers a free plan with 1 million trace spans per month, making it accessible for small teams and individual developers to get started.
• Code and UI Workflows: Supports both programmatic testing for engineers and visual prototyping for non-technical team members.
• Transparent Pricing: Pro plan at $249/month with clear usage limits and no hidden fees for teams running regular experiments.
• Multi-Framework Support: Works with various AI frameworks and integrates with popular tools like CrewAI for observability across different tech stacks.

• Learning Curve: Setting up comprehensive evaluations requires understanding of scoring mechanisms and dataset structures, which can be complex for teams new to AI testing.
• Limited Documentation: Some users may find the initial setup process challenging without extensive examples for their specific use cases.
• Usage-Based Scaling: Beyond included limits on the Pro plan, additional usage incurs prorated fees which can become expensive for high-volume applications.

• Pre-Deployment Testing: Run your chatbot prompts against 500 real customer queries to understand how changes perform before pushing to production. Compare response quality scores side-by-side to pick the best version.
• RAG System Evaluation: Test your retrieval-augmented generation system with curated question-answer pairs to measure accuracy of document retrieval and answer generation across different embedding models.
• Production Quality Monitoring: Track your AI customer support agent's response quality in real-time, receiving alerts when accuracy drops below 85% so you can investigate and fix issues before customers are impacted.
• Prompt Engineering Iteration: Experiment with different prompt variations for your content generation tool, using automated scoring to identify which prompts produce the most relevant and accurate outputs.
• Regression Prevention: Integrate evaluation tests into your CI/CD pipeline to automatically catch performance drops when updating models or changing system prompts.
• Multi-Agent Workflow Testing: Evaluate complex AI agent systems where multiple models collaborate, tracking each agent's performance and debugging interaction issues with detailed traces.