Production trace observability
See prompts, responses, tool calls, latency, cost, and request metadata for each AI call. The UI is designed for inspecting production behavior and finding issues from real traces.
Braintrust
ReivindicarBraintrust is an AI observability platform for tracing production AI behavior, running evaluations, and catching regressions before users see them.
Overview
Braintrust is an AI observability platform for teams that want to trace production behavior, evaluate model quality, and catch regressions before they reach users. The site positions it as a system for understanding what actually happened in production rather than relying only on offline testing.
The product combines trace inspection, evals, automation, and workflow tooling. Teams can instrument applications, inspect prompts and tool calls, compare models and prompts, build datasets from real failures, and use production signals such as Topics to guide improvements over time.
Features
Evals and scoring
Run experiments against datasets and score outputs with LLMs, code, or humans. The product positions evals as a way to define quality before release and compare prompts or models side by side.
Pattern discovery with Topics
Surface recurring patterns in real traffic with Topics, then convert those patterns into evaluations or alerts. The pricing and homepage copy both describe Topics as a way to classify and discover domain-specific issues in production logs.
Datasets and review workflows
Turn production traces into datasets for regression testing and create human review workflows without building custom frontend code. The site also highlights datasets, playgrounds, and custom annotation interfaces.
CLI, SDKs, and MCP access
Use the CLI, SDKs, and MCP server to instrument apps, query logs, run evals, and update prompts from development tools. The docs emphasize repeatable setup and scripting for coding-agent workflows.
Team and governance controls
Customize trace views, charts, environments, and access controls as your team grows. The pricing page lists saved table views, custom columns, custom charts, environments, RBAC, SSO, MFA, and data retention controls on higher tiers.
Use cases
Trace production AI behavior
Instrument an AI application so every call writes traces to Braintrust, then inspect prompts, outputs, tool calls, token counts, latency, and cost in the Logs view.
Measure prompt and model quality
Build evals from real datasets, compare prompts and models side by side, and score outputs with LLMs, code, or human reviewers before releasing changes.
Find regressions from live traffic
Use Topics to surface recurring patterns in production logs, then turn those patterns into evals, quality gates, or alerts when new regressions appear.
Build regression tests from real failures
Create datasets and custom review flows from actual traces so teams can test against edge cases and review outputs in a workflow that matches their task.
Operate Braintrust from development tools
Use the CLI, SDKs, or MCP server to query logs, run evals, and manage prompts from engineering tools and agent workflows.
Pros and Cons
Pros
- Covers the full loop from tracing and evaluation to regression detection and production monitoring.
- Supports inspection of detailed trace data, including prompts, outputs, metadata, latency, and cost.
- Offers multiple ways to interact with the platform, including CLI, SDKs, MCP, and UI workflows.
- Provides team-oriented controls such as custom views, charts, environments, and access management on higher tiers.
- Has a free Starter plan alongside paid and enterprise options.
Cons
- Some integrations and feature areas are listed in the docs index, but the public pages do not fully enumerate every supported tool or provider.
- Several advanced capabilities and deployment details are described more clearly in docs than on the main marketing pages, so readers may need to consult documentation for implementation specifics.
FAQ
How do you get started with Braintrust?
Braintrust is set up by installing the CLI, instrumenting your app, and then viewing traces in the Braintrust UI. The quickstart shows a setup script that installs `bt` and guides instrumentation, and the docs also describe using the `bt` CLI for repeatable work such as running evals, querying logs, and syncing data.
What do traces show?
The tracing quickstart says that once your app is instrumented, it logs traces for every AI call. In the UI, you can inspect the full input prompt and model output, token counts, latency, cost, model configuration, and request/response metadata.
What workflow does Braintrust support?
The docs present Braintrust as a platform for capturing traces, analyzing logs, adding human feedback, building datasets, testing changes with experiments, and shipping while monitoring production. The workflow is organized around capturing traces, finding patterns, adding feedback, testing changes, and then monitoring production.
Which languages and integrations are supported?
The documentation and pricing page indicate support for Python, TypeScript, Go, Ruby, C#, and more through native SDKs, plus integrations with AI providers, frameworks, and developer tools. The integrations page also points to framework-agnostic usage and a developer-tools workflow through MCP and the CLI.
Does Braintrust have a free plan?
Braintrust offers a free Starter plan, a paid Pro plan, and custom Enterprise pricing. The pricing page also shows options for usage-based fees tied to processed data and scores, along with features such as custom charts, RBAC, and higher retention on higher-tier plans.
Quick Facts
- Category
- AI observability platform
- Primary use
- Tracing, evals, and regression detection for AI products
- Access methods
- Web app, CLI, SDKs, and MCP server
- Supported SDK languages
- Python, TypeScript, Go, Ruby, C#, and more
- Pricing model
- Free Starter plan, paid Pro plan, and custom Enterprise pricing
- Website
- braintrust.dev
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