Braintrust

Braintrust is an AI observability and evaluation platform built by Braintrust Data, Inc. (California, 2023, Ankur Goyal ex-Figma ML head). Positioned as "AI observability platform for building quality AI products" — a comprehensive solution for teams running LLM applications in production who want to control quality, detect regressions, and iterate. Core thesis: AI fails differently than normal software — it drifts and regresses silently, requiring a different kind of observability + continuous evaluation against its own expectations.

Three product pillars: (1) Observability — inspection of every trace, prompt, response, tool call in real time; search across millions of logs; live monitoring of latency, cost, quality; custom views and annotation. (2) Evals — define what good looks like before release; run experiments against real datasets; compare prompts and models side-by-side; score with LLMs, code, humans; fast prompt engineering; versioned datasets. (3) Automation — Topics automatically discovers patterns in production (issue clustering, sentiment); continuous online scoring catches regressions; quality gates block bad releases before they reach production.

Advanced 2026 features: (1) Loop agent — AI that helps the user improve AI: describe what you want to optimise, and Loop generates better prompts, scorers, and datasets automatically. (2) Custom facets — define custom clustering dimensions that matter for your business (use case, customer segment, compliance, tone); Topics continuously classifies every trace. (3) Task-specific trace views — build annotation interfaces tailored to a specific workflow (support conversations vs. code generation). (4) Trace to dataset — one-click conversion of production traces into eval datasets; regression tests from real failures and edge cases. (5) MCP server — query logs, run evals, update prompts directly from the IDE; Braintrust's MCP server integrates coding agents (Cursor, Claude Code, Codex) with the Braintrust stack.

Brainstore — an in-house database purpose-built for AI observability. Traditional databases can't handle the complexity of AI traces (large, nested). Brainstore delivers faster full-text search, write latency, and span load time than the competition (benchmarks on the braintrust.dev blog). Native SDKs: Python, TypeScript, Go, Ruby, C#. Framework agnostic — works with any stack: OpenAI, Anthropic, Vercel AI SDK, LangChain, LlamaIndex, etc. Enterprise customers: Notion (70 engineers, <24h to deploy a new frontier model), Coursera (45× more feedback with AI grading), Vercel (Malte Ubl - CTO), Dropbox (hundreds to thousands of experiments), Replit, Graphite (5% negative-rule reduction), Navan.

Enterprise-grade security (secure by default, compliant from day one): SOC 2 Type II certified (independently audited annually), GDPR compliant, HIPAA compliant, SSO/SAML integration with identity providers, granular permissions (project + resource level), hybrid deployment (Brainstore data plane on the customer's own infrastructure). Competition: DeepEval / Confident AI (open-source alternative), LangSmith, Weights & Biases Weave, Arize AI, Fiddler AI. Differentiation vs. DeepEval: Braintrust is a closed-source hosted SaaS with rich enterprise features, while DeepEval is an open-source framework with an optional SaaS.