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Braintrust

by Braintrust

LLM experiment tracking and prompt management platform — logging APAC LLM experiments, scoring outputs with custom evaluators, managing prompt versions, and tracking quality metrics across production deployments for collaborative APAC AI team workflows.

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AIMenta verdict
Decent fit
4/5

"LLM experiment tracking and prompt management — APAC AI teams use Braintrust to log LLM experiments, compare prompt versions, score outputs, and track APAC production LLM quality over time in a collaborative evaluation platform."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Experiment logging: APAC LLM input/output/latency/cost tracking with lightweight SDK
  • Prompt playground: APAC prompt version comparison against curated test cases
  • Multi-scorer: AI judge + human review + code-based APAC output scoring
  • Production monitoring: APAC live traffic scoring and quality trend tracking
  • Prompt management: versioned APAC system prompts deployable without code changes
  • Team collaboration: APAC shared experiment history and human review workflows
When to reach for it

Best for

  • APAC AI product teams building LLM-powered applications who need systematic experiment tracking and prompt management — particularly APAC teams where prompt iteration is a continuous workflow involving both engineers and non-technical APAC stakeholders.
Don't get burned

Limitations to know

  • ! Cloud-only — APAC data sovereignty teams cannot self-host Braintrust
  • ! Overlap with Langfuse (open-source) for APAC teams preferring self-hosted LLM logging
  • ! Evaluation scoring costs accumulate for APAC high-volume production monitoring
Context

About Braintrust

Braintrust is an LLM experiment tracking and evaluation platform designed for APAC AI product teams — providing experiment logging, prompt version management, output scoring, and production monitoring in a single collaborative platform. APAC teams building LLM-powered products use Braintrust to systematically compare model versions, prompt variations, and evaluation scores rather than tracking results in spreadsheets.

Braintrust's experiment logging captures APAC LLM inputs, outputs, latency, and cost for every model call — APAC teams instrument their LLM applications with a lightweight SDK that logs experiments to Braintrust's cloud storage without changing application logic. The Braintrust dashboard shows experiment history, enabling APAC teams to compare this week's prompt changes against the baseline and understand exactly which APAC model changes improved or degraded quality.

Braintrust's scoring system supports multiple APAC evaluation approaches on logged experiments: AI-based scoring (using an LLM judge to score factuality, relevance, or APAC domain-specific criteria), human scoring (APAC team members label outputs as correct/incorrect in the Braintrust UI), and code-based scoring (exact match, regex, custom Python functions). APAC teams combine multiple scorers — running fast AI scoring automatically, then routing low-confidence APAC outputs to human reviewers.

Braintrust's prompt playground provides an APAC team workspace for iterating on prompts — testing APAC prompt variations against curated test cases, comparing outputs side-by-side, and promoting successful APAC prompts to production with version tracking. APAC teams manage system prompts as versioned artifacts in Braintrust rather than hardcoding them in APAC application repositories, enabling non-engineer APAC stakeholders to iterate on prompt language without code deployments.

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