Braintrust
End-to-end platform for building AI products. Evals, logging, prompt management.
Braintrust is an end-to-end AI product engineering platform designed to help teams ship and maintain high-quality AI applications in production. It addresses a fundamental challenge in modern AI development: AI systems fail differently than traditional software — they drift, hallucinate, and regress silently — which makes conventional monitoring and debugging tools inadequate.
At its core, Braintrust is built around three pillars: observability, evaluation, and continuous improvement. The observability layer captures every trace in real time, allowing engineers to inspect individual prompts, responses, and tool calls while tracking latency, cost, and quality metrics. Rather than discovering issues after users complain, teams can configure alerts that surface problems before they reach production.
The evaluation system lets teams define quality criteria before shipping. Engineers run experiments against versioned datasets, compare prompts and models side-by-side, and catch regressions automatically within CI pipelines. Scoring can be done via LLMs, custom code, or human annotators, giving teams flexibility depending on the task domain. A standout feature is the ability to convert production traces into eval datasets with a single click — turning real failures and edge cases into regression tests rather than relying on synthetic examples.
Braintrust also ships Loop, an AI agent that assists with AI improvement. Given a description of what to optimize, Loop generates better prompts, scorers, and datasets automatically, closing the feedback loop between production observations and evaluation improvements.
Underpinning the platform is Brainstore, a proprietary database built specifically for AI trace data. Traditional databases struggle with the large, deeply nested structure of AI traces; Brainstore is engineered for this workload and delivers significantly faster full-text search, write latency, and span load times compared to general-purpose alternatives.
For enterprise teams, Braintrust provides SOC 2 Type II certification, GDPR and HIPAA compliance, SSO/SAML integration, granular RBAC, and hybrid deployment options where the Brainstore data plane runs on the customer's own infrastructure. This positions it directly against platforms like LangSmith, Arize Phoenix, and Weights & Biases, though Braintrust's combination of a purpose-built database, native CI integration, and MCP server for IDE connectivity gives it a distinct profile.
The platform supports SDKs across Python, TypeScript, Go, Ruby, C#, and more, and is framework-agnostic — it integrates with existing stacks without requiring rewrites or vendor lock-in. Customers include Notion (deploying new frontier models in under 24 hours), Coursera (45x more feedback with AI grading), Dropbox, Vercel, and Replit, reflecting adoption across both product companies and infrastructure teams running AI at scale.
Key Features
- Real-time trace inspection with visibility into prompts, responses, tool calls, latency, cost, and quality metrics
- Automated evaluation pipelines with LLM-based, code-based, and human scoring against versioned datasets
- CI integration to catch regressions before deployment, with side-by-side prompt and model comparison
- One-click conversion of production traces into eval datasets for regression testing
- Loop agent that auto-generates improved prompts, scorers, and datasets based on optimization goals
- Brainstore, a purpose-built database optimized for querying large, nested AI trace data at scale
- MCP server integration enabling engineers to query logs, run evals, and update prompts directly from their IDE
- Enterprise-grade security with SOC 2 Type II, HIPAA, GDPR, SSO/SAML, RBAC, and hybrid deployment support
Pros & Cons
Pros
- Purpose-built database (Brainstore) handles the scale and complexity of AI trace data better than general-purpose alternatives
- Covers the full eval lifecycle — from dataset management to CI regression detection — in a single platform
- Framework-agnostic with SDKs for six languages, minimizing adoption friction
- Loop agent adds an AI-assisted layer for improving prompts and scorers without manual iteration
- Strong enterprise compliance posture with hybrid deployment for data-sensitive organizations
Cons
- As an enterprise-focused platform, pricing and full feature access likely require contacting sales, which may not suit individual developers or small teams
- The breadth of the platform (observability, evals, datasets, prompt management, Loop) introduces a learning curve
- Teams with simple AI integrations may find the platform more infrastructure than their use case requires
Pricing
Visit the official website for current pricing details. Braintrust offers a sign-up path for self-serve access and a separate contact-sales flow for enterprise arrangements.
Who Is This For?
Braintrust is best suited for engineering and product teams building AI-powered features or agents in production who need systematic quality control beyond ad hoc testing. It is particularly well-matched for organizations running multiple models or prompt variants in parallel, teams operating in regulated industries requiring compliance and audit trails, and companies with dedicated AI quality or evaluation workflows that span engineering, product, and domain experts.
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