Braintrust: Agent Evaluations, Scoring and Usage Costs
What does Braintrust provide?
Braintrust combines application tracing, dataset annotation and evaluation experiments. Its documentation also describes tools for investigating trace patterns and creating evaluations. The value of an experiment depends on whether its cases and scoring rules represent the behavior your users need.
Who should consider it?
AI product teams comparing prompts, models or agent changes can use one evaluation workflow to preserve examples and inspect regressions. It is less useful to a team that has not defined what a successful response means. Begin with a small reference dataset rather than importing a large unlabeled log archive.
What is the pricing model?
Starter is listed at $0 per month with $10 model credits, 1 GB of processed data, 10,000 scores and 14-day retention. The pricing page shows additional usage charges. Pro is $249 per month with larger allowances and longer retention. A subscription fee does not cover unlimited model calls, processed logs or scores.
How should you build a first experiment?
Choose twenty synthetic support questions, including unsupported requests and near-duplicate questions with different valid answers. Write reference criteria before comparing two prompt versions. Keep the same inputs and model settings where possible. Label each failure as missing knowledge, wrong reasoning, inappropriate action or formatting failure so the result suggests a concrete next change.
How do you avoid a misleading score?
Review borderline cases by hand. A fluent answer can satisfy a permissive grader while still making a false promise. Store the scoring definition and inspect examples whose scores changed most. Compare failure counts by category, not just the overall mean. Use a separate holdout set before adopting the winning prompt.
What data and cost controls matter?
Trace data may reproduce customer messages and internal tool outputs. Remove unnecessary identifiers before sending logs to a hosted service. Estimate evaluation spend as cases multiplied by candidate versions and judge calls, then add repeat runs. Short retention can make later incident review impossible unless your export process preserves the needed evidence.
Which alternatives should you compare?
Compare Langfuse for integrated tracing and prompt management. Compare Promptfoo for a configuration-driven evaluation workflow that fits a local or CI process. Judge each on reproducibility, useful failure inspection and complete experiment cost.
Sources checked October 27, 2026: Official documentation · Pricing and access conditions.