Braintrust

What Braintrust does

Braintrust is an evaluation and observability platform for LLM applications and agents. It joins experiments, datasets, scorers, prompt management, and production traces so real failures can become regression tests. That closed loop is the differentiator: inspect a bad production span, add its input to a dataset, compare prompt or model changes, and promote only a measured improvement. An official MCP server can let authorized agents query logs, run evaluations, and update prompts.

Key features

1. Agent traces. Confirm the exact behavior, limits, permissions, and plan entitlement with a representative task.
2. Evaluations. Confirm the exact behavior, limits, permissions, and plan entitlement with a representative task.
3. Quality scoring. Confirm the exact behavior, limits, permissions, and plan entitlement with a representative task.
4. Prompt management. Confirm the exact behavior, limits, permissions, and plan entitlement with a representative task.

Pricing and cost

The vendor page positions pricing as usage-scaled. The catalog records Starter at $0 per month, Pro at $249 per month, and Enterprise at custom pricing. Verify included trace volume, evaluation runs, data retention, seats, overages, support, SSO, and data-residency options. Model-provider charges remain separate, and repeated evaluation suites multiply token spend. Budget observability and model usage together instead of comparing only the fixed plan fee.

How to evaluate it

Start with 50 to 200 representative examples, including known failures. Define scorers for schema validity, citation accuracy, tool selection, groundedness, latency, and cost; one generic quality score conceals failure modes. Compare the current prompt against one proposed change and manually review scorer disagreements. In production, attach model, prompt version, tool call, latency, and token metadata without collecting unnecessary sensitive content. Alert on rates such as failed tool calls or low groundedness, not raw trace count.

Pros

• Links production traces to datasets, experiments, scorers, and prompts
• Supports repeatable regression tests across models and versions
• Official MCP server can bring evaluation operations into agent workflows

Cons

• $249 monthly Pro price can be high for small teams beyond Starter
• High-value evals require domain datasets and carefully designed scorers
• Prompt and output logging creates privacy and access-control obligations

Practical use cases

• Preventing regressions after model or prompt changes
• Tracing multi-step agent failures and expensive loops
• Comparing quality, latency, and cost on a stable dataset

Alternatives and buying checklist

Relevant alternatives are Langfuse, AgentOps, DeepEval, Datadog LLM Observability. Compare each with identical inputs, acceptance criteria, and security constraints. Track successful outcomes, setup time, human correction time, latency, failed actions, and monthly cost. Run edge cases, revoke a test user's access, inspect exports and audit logs, and simulate an integration outage. A polished demo is not evidence of production reliability.

This profile reflects vendor homepage and pricing research performed September 14, 2026. Pricing and entitlements can change, so confirm billing interval, included usage, overage rates, retention, support, and cancellation terms before signing. For agents that can take actions, begin read-only, grant least privilege, require approval for consequential writes, and keep a rollback path. Select based on measured quality after review time and operating cost are included—not the most impressive first prompt.