Ship quality AI at scale

Surface patterns in production, turn them into evals, and improve quality with every release.

Trusted by the best AI teams

Inspect traces in real time

AI fails differently than normal software. You need a new kind of observability to monitor and fix it.

AI drifts and regresses silently. With patterns surfaced automatically, the best teams can evaluate against expectations and iterate continuously.

Trace everything

Inspect prompts, responses, and tool calls in real time

Measure quality with evals

Score outputs with LLMs, code, or humans

Catch issues early

Block bad releases before they hit production

Explore the three pillars of AI observability Take the eval maturity assessment

AI observability and evaluation for the whole team. From engineering to product, in one platform.

Observability

See what actually happened in production. Inspect every trace and tool call, search across millions of logs, and track latency, cost, and quality in real time.

Scalable trace ingestion

Live performance monitoring

Custom views and annotation

Log your first trace

Evals

Define what good looks like before you ship. Run experiments against real datasets, compare prompts and models side-by-side, and score outputs with LLMs, code, or humans.

Fast prompt engineering

Flexible, versioned datasets

Automated and human scoring

Run your first eval

Automation

Turn production signals into improvements automatically. Topics surfaces patterns in real time across task, issues, and sentiment, online scoring catches regressions, and quality gates block bad releases.

Automatic pattern discovery

Continuous online scoring

Quality gates and alerts

Discover patterns with Topics

Everything you need to build smarter, faster

Loop agent

AI that helps you improve AI. Describe what you want to optimize, and Loop generates better prompts, scorers, and datasets automatically.

Optimize your evals

Custom facets

Define the dimensions that matter to your business, like use case, customer segment, compliance, or tone. Topics continuously clusters every trace against them.

Design your own facet

Task-specific trace views

Build annotation interfaces that match your team's workflow. Review support conversations differently than code generation, with no frontend work required.

Build custom views

Trace to dataset

Turn production traces into eval datasets with one click. Build regression tests from real failures and edge cases, not synthetic examples.

Explore datasets

MCP

Query logs, run evals, and update prompts directly from your IDE. Braintrust's MCP server connects your coding agent to your AI stack.

Set up MCP

Framework agnostic

Works with any stack you're already using. No framework lock-in, no rewrites, no vendor dependencies to manage.

View all integrations

Native SDKs

SDKs for Python, TypeScript, Go, Ruby, C#, and more. Start tracing production AI with just a few lines of code.

Read SDK docs

Brainstore, the database built for AI data at scale. Designed for complex AI traces.

AI traces are large and nested. Traditional databases can't handle the complexity. Brainstore is designed specifically for AI observability so you can query millions of traces quickly.

Learn more about Brainstore

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