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Braintrust

Eval-driven dev platform combining traces, datasets, scorers, and a playground in one product.

score9.1
LLM evalsobservabilityprompt managementfreemiumwww.braintrust.dev

Verdict

The best end-to-end platform for LLM eval and observability we've tested. Braintrust covers datasets, experiments, traces, online scoring, prompt management, and CI in one product — and does each piece better than the standalone tools that focus on just one. If you're shipping LLM features and the question is "what should we use," this is the answer for almost every team.

What it is

Braintrust is an end-to-end platform for building, evaluating, and monitoring LLM apps. The core loop: define a dataset, write or generate scorers, run experiments, compare across prompts and models, and ship to production where the same scorers run continuously on traces. The seam between "dev-time eval" and "prod monitoring" — where most teams lose information — is invisible here. That continuity is the single most underrated property in this category, and Braintrust is the tool that nails it.

Pricing starts free with 1M trace spans; the Pro plan is $249/month with unlimited spans.

Developer experience

Small, well-typed SDKs. A first eval takes about fifteen minutes from an empty repo:

import { Eval } from "braintrust";
 
await Eval("triage", {
  data: () => [{ input: "ticket text", expected: "billing" }],
  task: async (input) => classify(input),
  scores: [ExactMatch],
});

The TS and Python SDKs feel like they were written by the same people in the same week — rare in this space. The AI Proxy is also worth noting: route LLM calls through a Braintrust-hosted base URL and you get logging, caching, and provider fallbacks without touching application code.

Where it shines

  • Playground. Side-by-side prompt comparison with diffing, model switching, and inline scoring beats every alternative we've tried, full stop.
  • One product, full lifecycle. Datasets, experiments, traces, online evals, and prompt management in one place. Most competitors do one or two of these well; Braintrust does all of them well.
  • Loop. The AI assistant for generating scorers and datasets from production logs is a real productivity unlock — the kind of feature that changes how teams work, not just a marketing bullet.
  • CI integration. GitHub Actions support that fails builds on quality regressions, with confidence intervals and significance tests. Not a webhook to Slack — actual eval-driven release gates.
  • Customer signal. Notion, Stripe, Vercel, Airtable, Instacart, Zapier. That's not a polite trial list; that's production AI at companies whose engineering teams have looked at every alternative.

Where it falls short

  • No real OSS story. A few helpers are open, the platform isn't. If self-hosting on your own infrastructure is non-negotiable, Langfuse is the answer instead.
  • Pricing past the free tier. The free tier is generous, but the jump to paid is steep at production volume. Worth it for almost every team that gets there — but it's the one place a CFO might push back.

Bottom line

If you're shipping LLM features and not legally required to self-host, this is the tool. The honest competitive picture is that Langfuse is the OSS alternative, Galileo wins on online-eval cost at extreme scale, and a few specialists (Fiddler for governance, Vellum for low-code) win in narrow lanes — but for the central question of "what should our product engineering team use," Braintrust is the answer.

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