About
Braintrust is a comprehensive AI observability and evaluation platform purpose-built for teams running large language models and AI agents in production. It enables engineers and product teams to trace every prompt, response, and tool call in real time, measure output quality with LLM-based or human scoring, and run automated evaluations in CI pipelines to catch regressions before they reach users. At its core, Braintrust offers three pillars: Observability for live production monitoring with cost, latency, and quality metrics; Evals for running experiments on versioned datasets with side-by-side prompt comparison; and Loop, an AI-powered agent that automatically generates improved prompts, scorers, and datasets. Users can turn production traces into eval datasets with a single click, creating regression tests from real-world failures rather than synthetic examples. Braintrust supports native SDKs for Python, TypeScript, Go, Ruby, C#, and more, making it framework-agnostic with no vendor lock-in. Its proprietary Brainstore database is optimized for AI trace data at scale, offering faster full-text search, write latency, and span loading than traditional databases. The platform includes an MCP server to integrate directly with IDEs and coding agents. Designed for enterprise use, Braintrust is SOC 2 Type II certified, GDPR and HIPAA compliant, supports SSO/SAML, granular RBAC, and hybrid deployment options. It's trusted by top AI teams and backed by an $80M Series B.
Key Features
- Real-Time Production Tracing: Inspect every prompt, response, and tool call as it happens in production, with live monitoring of latency, cost, and quality metrics and automated alerts.
- Automated Evaluations & CI Integration: Run experiments on versioned datasets, compare prompts side-by-side, and block bad releases automatically in your CI pipeline before they reach users.
- Loop AI Optimization Agent: Describe your optimization goal and Loop automatically generates better prompts, scoring functions, and datasets to continuously improve your AI outputs.
- Trace-to-Dataset Conversion: Convert production traces into eval datasets with one click, enabling regression tests built from real failures and edge cases rather than synthetic data.
- Brainstore — Purpose-Built AI Database: A proprietary database optimized for nested AI trace data at scale, delivering significantly faster full-text search, write latency, and span load times than traditional databases.
Use Cases
- Monitoring LLM-powered applications in production to track latency, cost, and output quality in real time.
- Running automated regression tests on AI outputs in CI/CD pipelines to prevent quality degradation between releases.
- Building eval datasets from real production failures and edge cases to create more representative test suites.
- Comparing prompt variants and model configurations side-by-side to identify the highest-quality, most cost-efficient setup.
- Enabling cross-functional AI teams — engineers, product managers, and domain experts — to collaboratively review and annotate AI outputs through customizable annotation interfaces.
Pros
- Framework Agnostic: Works with any AI stack via native SDKs for Python, TypeScript, Go, Ruby, C#, and more — no rewrites or vendor lock-in required.
- End-to-End Workflow: Covers the full AI development lifecycle from production observability to evaluation, dataset management, and automated quality improvement in one platform.
- Enterprise-Grade Security: SOC 2 Type II, GDPR, HIPAA compliance plus SSO/SAML, granular RBAC, and hybrid deployment options make it suitable for regulated industries.
- IDE Integration via MCP: The MCP server lets coding agents query logs, run evals, and update prompts directly from the IDE, tightening the AI development feedback loop.
Cons
- Pricing Complexity at Scale: Enterprise pricing requires contacting sales, which can make cost estimation difficult for growing teams before committing to a plan.
- Learning Curve for Eval Design: Setting up meaningful evaluations, scorers, and datasets requires upfront investment in understanding eval best practices and tooling configuration.
- Primarily Developer-Focused: While it aims to serve full teams, the platform is heavily engineering-oriented and may require technical setup before non-engineers can leverage it fully.
Frequently Asked Questions
What is Braintrust used for?
Braintrust is used to monitor AI applications in production, run automated evaluations on LLM outputs, compare prompt variants, build regression test datasets, and continuously improve AI quality — all from a single platform.
Which programming languages does Braintrust support?
Braintrust offers native SDKs for Python, TypeScript, Go, Ruby, C#, and more, making it easy to integrate into most AI development stacks.
How does Braintrust handle data security and compliance?
Braintrust is SOC 2 Type II certified, GDPR and HIPAA compliant, and supports SSO/SAML, role-based access control, and hybrid deployment where the Brainstore data plane runs on your own infrastructure.
What is the Loop feature in Braintrust?
Loop is an AI agent within Braintrust that automatically generates improved prompts, scoring functions, and datasets based on your optimization goals, helping teams improve AI quality without manual trial-and-error.
Can Braintrust integrate with my existing CI/CD pipeline?
Yes. Braintrust supports automated evaluation runs within CI pipelines so you can catch quality regressions and block bad releases before they reach production.
