About
Braintrust is a comprehensive AI observability and evaluation platform designed to help engineering and product teams build high-quality AI applications at scale. Unlike traditional monitoring tools, Braintrust is purpose-built for the unique challenges of LLM-based systems—including hallucinations, silent regressions, and performance drift. The platform is organized around three pillars: Observability, Evals, and Loop. The Observability layer lets teams inspect every production trace in real time, monitoring latency, cost, and quality with customizable alerting. The Evals layer enables rapid prompt engineering, side-by-side model comparisons, flexible dataset versioning, and automated CI-integrated regression testing scored by LLMs, code, or human reviewers. The Loop agent uses AI to automatically generate better prompts, scorers, and datasets based on your optimization goals. Braintrust also includes Brainstore, a proprietary database engineered for the scale and complexity of AI trace data, offering significantly faster full-text search, write latency, and span load times compared to traditional databases. The platform is framework-agnostic, supports native SDKs in Python, TypeScript, Go, Ruby, C#, and more, and integrates via an MCP server directly into developer IDEs. Enterprise-grade security is built in, including SOC 2 Type II, HIPAA, GDPR compliance, RBAC, SSO/SAML, and hybrid deployment options.
Key Features
- Real-Time Trace Inspection: Inspect every prompt, response, and tool call in production with live performance monitoring for latency, cost, and quality metrics.
- Automated Evals & CI Integration: Run experiments against versioned datasets, compare prompts side-by-side, and automatically catch regressions in your CI/CD pipeline.
- Loop AI Agent: An AI-powered assistant that analyzes your eval goals and automatically generates optimized prompts, scorers, and datasets to improve quality.
- Brainstore: Purpose-Built AI Database: A high-performance database designed specifically for complex, nested AI trace data—offering faster full-text search and write latency than traditional databases.
- Framework-Agnostic SDK Support: Native SDKs for Python, TypeScript, Go, Ruby, C#, and more, plus MCP server integration so developers can query logs and run evals directly from their IDE.
Use Cases
- LLM engineering teams running automated regression testing in CI/CD to catch quality degradations before production releases.
- AI product teams comparing multiple prompt variants and models side-by-side to optimize response quality and reduce costs.
- Enterprises monitoring production AI systems in real time to track latency, token costs, and hallucination rates across millions of traces.
- Data and ML teams building eval datasets from real production failures and edge cases to continuously improve model performance.
- Developers querying AI logs, running evals, and updating prompts directly from their IDE using the Braintrust MCP server integration.
Pros
- End-to-End AI Quality Workflow: Covers the entire loop from production tracing to dataset creation to eval automation, eliminating the need for multiple disconnected tools.
- Enterprise-Grade Security: Ships with SOC 2 Type II, HIPAA, GDPR compliance, RBAC, SSO/SAML, and hybrid deployment options out of the box.
- Framework and Model Agnostic: Works with any LLM stack without vendor lock-in, supporting all major languages and integrating into existing CI/CD pipelines.
- Scalable Infrastructure: Brainstore database is purpose-built for AI data at scale, handling millions of complex traces with significantly faster query performance.
Cons
- Enterprise Pricing Complexity: Full-scale enterprise features require contacting sales, which may slow adoption for smaller teams evaluating the platform.
- Learning Curve for Eval Design: Building robust, meaningful evals requires thoughtful dataset and scorer design, which can be time-consuming for teams new to AI evaluation practices.
- Overkill for Simple Use Cases: Teams running simple, low-volume LLM tasks may find the platform's breadth more than they need in early-stage development.
Frequently Asked Questions
What is Braintrust used for?
Braintrust is used to monitor, evaluate, and improve LLM-powered AI products. It lets teams trace production calls in real time, run automated evals against datasets, compare prompts and models, and catch quality regressions before they reach users.
Does Braintrust work with any LLM framework?
Yes. Braintrust is fully framework-agnostic and works with any AI stack. It offers native SDKs in Python, TypeScript, Go, Ruby, C#, and more, with no required rewrites or vendor lock-in.
What is Brainstore?
Brainstore is Braintrust's proprietary database built specifically for AI observability data. It handles the complexity of large, nested AI traces and delivers significantly faster full-text search, write latency, and span load times compared to traditional databases.
Is Braintrust compliant with HIPAA and GDPR?
Yes. Braintrust is SOC 2 Type II certified, HIPAA compliant, and GDPR compliant. It also supports SSO/SAML, role-based access control (RBAC), and hybrid deployment for teams with strict data residency requirements.
How does Braintrust pricing work?
Braintrust offers a self-serve sign-up for getting started, with enterprise plans available via sales for teams requiring advanced security, scale, and support. Visit braintrustdata.com/pricing for current plan details.
