Braintrust

Braintrust has become the default for serious LLM eval and experimentation. The learning curve is real, but for teams shipping AI features, it's the most productive tooling in the category.

LLM evals are the dev-test loop for AI features, and Braintrust is the tool that made that loop fast. Datasets, experiments, scorers (code-based, model-graded, human-labeled), and prompt versioning are first-class in a way that no general-purpose observability tool delivers. For teams iterating on prompts, models, and retrieval pipelines, Braintrust compresses "change this, see if it's better" from days to hours.

The Playground — where you can edit a prompt, re-run across a dataset, and diff scores side-by-side — is the killer feature. The cost tracking, model routing, and proxy layer are legitimate operational value on top. The team ships at an unusually fast pace, and the docs and examples are above average.

Honest weaknesses. First, Braintrust expects you to think in datasets and experiments, which is the correct mental model but requires investment — teams without a designated AI engineer or ML-adjacent person struggle to operationalize. Second, pricing scales with both events and seats, and at enterprise scale the annual contracts are serious money ($50k+ not unusual). Third, the observability/tracing side, while functional, is not as polished as Langfuse's — teams doing observability-first often pair Langfuse with Braintrust, which is additive cost.

Buy Braintrust if you're shipping AI features to users and you need to measure whether your prompt changes are actually improvements. For pure tracing-and-monitoring needs, start with Langfuse. The combination is expensive but defensible for serious teams.