Braintrust
Evaluation and observability platform for AI products
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What this product helps you do
Braintrust uses a shared data model across experiments and production logs, connecting observability to systematic evaluation. Teams can instrument traces, investigate patterns, compare prompts or models in playgrounds and experiments, run evaluation suites in CI, score live traffic, and move useful production examples into datasets.
Problems it solves
- AI behavior needs measurable quality checks beyond traditional unit tests
- Production traces are difficult to search and connect to development work
- Prompt and model changes can create silent regressions
- Useful production examples need a path into repeatable evaluation datasets
Braintrust is an evaluation and observability platform for AI applications and agents. It captures production requests as searchable traces and connects those logs to datasets, experiments, scorers, dashboards, and human review.
Teams can iterate in playgrounds, preserve comparable experiments, automate evaluations in CI, and score production traffic without adding scoring work to the request path. The Starter plan has no platform fee with defined included usage, while Pro and Enterprise expand limits and platform capabilities.
Information
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Braintrust is an evaluation and observability platform for AI applications and agents. It captures production requests as searchable traces and connects those logs to datasets, experiments, scorers, dashboards, and human review.
Teams can iterate in playgrounds, preserve comparable experiments, automate evaluations in CI, and score production traffic without adding scoring work to the request path. The Starter plan has no platform fee with defined included usage, while Pro and Enterprise expand limits and platform capabilities.
What this product helps you do
Braintrust uses a shared data model across experiments and production logs, connecting observability to systematic evaluation. Teams can instrument traces, investigate patterns, compare prompts or models in playgrounds and experiments, run evaluation suites in CI, score live traffic, and move useful production examples into datasets.
Problems it solves
- AI behavior needs measurable quality checks beyond traditional unit tests
- Production traces are difficult to search and connect to development work
- Prompt and model changes can create silent regressions
- Useful production examples need a path into repeatable evaluation datasets
Information
You are leaving botconomy for the provider’s website. Link verified 9/21/2026.
Related AI products
DeepFace
DeepFace is an MIT-licensed Python framework that wraps multiple face-recognition models and detectors behind functions for verification, search, embeddings, face extraction, attribute analysis, anti-spoofing, streaming, and API service.
Limina
Limina, formerly Private AI, detects and transforms PII, PHI, PCI, and confidential company information across text, documents, images, and audio, with API, private-cloud, and on-premises deployment options.
Coval
Coval helps teams simulate voice and chat agent conversations before launch, evaluate production calls, route failures for human review, and automate regression testing through APIs, SDKs, CLI, MCP, and CI workflows.