Braintrust

What is Braintrust?

Braintrust is an AI observability and evaluation platform founded by Ankur Goyal, focused on dataset-driven eval workflows for AI engineers. It helps teams log traces, compare prompts and models, and measure output quality before and after release. (braintrust.dev)

Understanding Braintrust

In practice, Braintrust sits in the middle of the modern LLM stack, between your application code and the feedback loop that tells you whether the system is getting better. The platform is built around traces, evals, and annotations, so teams can inspect what happened in production, turn real failures into datasets, and use those datasets to run repeatable experiments. (braintrust.dev)

That workflow matters because LLM systems are probabilistic. A prompt change, retrieval tweak, or model upgrade can improve one slice of behavior while hurting another, so Braintrust emphasizes versioned datasets, scorers, and continuous monitoring. The result is a structured loop for shipping AI with more confidence, especially when teams need to compare outputs across releases or catch regressions in CI. (braintrust.dev)

Key aspects of Braintrust include:

1. Observability: capture traces from production and inspect prompts, tool calls, latency, cost, and quality.
2. Datasets: build versioned test sets from production logs, feedback, imports, or manual curation.
3. Evals: run experiments against real datasets to compare prompts, models, and scoring approaches.
4. Scorers: evaluate outputs with code, LLM judges, or human review.
5. CI workflows: catch regressions automatically before changes reach users.

Common use cases

Braintrust is especially useful for teams that treat evals as part of the release process, not a one-time benchmark exercise.

1. Prompt iteration: test prompt changes side by side and keep the best-performing version.
2. Regression testing: run fixed datasets after model or code changes to spot quality drops early.
3. Production monitoring: review live traces to understand where failures start and how often they happen.
4. Dataset curation: turn real user interactions into labeled examples for future evals.
5. Team collaboration: let engineers, product teams, and reviewers work from the same quality signal.

Things to consider when choosing Braintrust

If you are evaluating Braintrust for your stack, these are the main fit questions to look at.

1. Workflow fit: check whether your team wants a dataset-first eval loop or a lighter-weight prompt testing setup.
2. Instrumentation depth: make sure the trace and annotation model matches your application complexity.
3. Scoring approach: confirm how well your use case works with code-based, LLM-based, or human scoring.
4. Deployment needs: review whether your org prefers cloud, hybrid, or self-hosted options.
5. Ecosystem alignment: verify how easily it connects to your framework, CI, and data sources.

Example of Braintrust in a stack

Scenario: a team ships a customer support agent that uses retrieval, tool calls, and a final response step.

They log production traces into Braintrust, then pull the most important failures into a versioned dataset. From there, they compare a new prompt against the previous one, score answers for accuracy and format, and run the eval in CI before deployment.

If the new release regresses on a subset of edge cases, the team can inspect the traces, update the dataset, and rerun the experiment. That makes the quality loop much easier to repeat than manual spot checks.

PromptLayer as an alternative to Braintrust

PromptLayer gives teams a prompt management and observability workflow for tracking prompt versions, reviewing changes, and connecting prompt iteration to production behavior. For teams that want a clear prompt registry and an operational layer around experimentation, PromptLayer fits naturally alongside the rest of the LLM stack, while Braintrust is often chosen for a dataset-heavy eval and observability workflow.

Ready to try it yourself? Sign up for PromptLayer and start managing your prompts in minutes.