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Composo vs Braintrust
Braintrust is an SDK-first evaluation platform that teams integrate into their CI/CD pipelines to run offline evals before shipping. Composo is a deployed quality layer that calibrates to your domain and catches failures in production - the work gets done for you rather than by you.
Braintrust gives you the tool. Composo deploys the system.
At a glance
| Dimension | Composo | Braintrust |
|---|---|---|
| Primary use | Production quality layer with domain-specific failure detection | Developer-first offline evaluation in CI/CD |
| Deployment model | We deploy it - 2 to 4 weeks, hand over a calibrated system | Self-serve SDK, teams write their own evals |
| Scoring approach | Reward-model based, calibrated to your domain, learns from corrections | LLM-as-judge with prompt templates you configure |
| Strength at launch | Ships with a failure taxonomy from 30+ deployments | Ships with developer ergonomics and CI/CD integration |
| Customer profile | Regulated and high-stakes AI (healthcare, fintech, legal, enterprise) | Developer-led teams (Airtable, Brex, Notion, Stripe, Instacart) |
| Pricing signal | Enterprise contract, scoped per deployment | Usage-based SaaS pricing |
Where Braintrust is strong
- Best-in-class CI/CD integration. Braintrust's GitHub Action automatically runs evaluations on every pull request. For developer-led teams that want eval-as-code, the ergonomics are strong.
- Mature SDK. Well-documented, broad language support, good abstractions for offline experimentation and dataset management.
- Offline evaluation workflows. If you want to systematically test prompt and model changes before shipping, Braintrust was built for that.
- Prompt Loop for optimisation. Automated prompt iteration and dataset generation - useful if your problem is primarily prompt engineering.
- Strong brand with developer-led teams. Airtable, Brex, Notion, Stripe, Instacart, Zapier are all customers.
Where Composo is different
- Deployment is the product. A senior engineer works with you for 2 to 4 weeks to deploy Composo into your stack, calibrate it to your specific failure modes, and hand it over. You do not write your own evals.
- Domain-specific calibration. Composo learns what a bad output looks like for your use case - not generic hallucination or tone checks. The failure taxonomy comes from 30+ prior deployments.
- Catches failures your LLM-as-judge misses. Composo reaches 90%+ alignment with human domain experts in most production contexts - the operational bar for catching domain-specific failures reliably. Basic LLM-as-judge typically sits around 70% human-expert alignment.
- Runtime guardrails. Composo can block bad outputs in production, not just score them offline. One customer blocks 50% of tool calls in real time.
- Corrections compound. Domain experts label a handful of edge cases, and the evaluation model generalises those corrections across the whole trace distribution.
When to pick which
Pick Braintrust if
- · You have a developer-led team that wants eval-as-code in CI/CD
- · Offline evaluation of prompts and models is your primary need
- · You are early in the AI lifecycle and want to experiment
- · You prefer SaaS self-service and are comfortable writing your own evals
Pick Composo if
- · You have AI in production and need to catch failures your current evals miss
- · Your domain is specialised (clinical, financial, legal, regulated)
- · You want a deployed system rather than an SDK to configure
- · Speed matters - you want coverage in weeks, not a 6-month internal build
Frequently asked questions
Is Composo a replacement for Braintrust?
For most teams with production AI in regulated or high-stakes domains, yes. Composo replaces both your LLM-as-judge evaluation infrastructure and the manual QA process around it. For teams whose primary need is offline eval-as-code in CI/CD, Braintrust may be a better fit.
Can Composo and Braintrust run together?
Yes. Some teams use Braintrust for offline dev-time evals and Composo for production quality monitoring and failure detection. They are not mutually exclusive, but they solve different problems.
How does Composo's scoring accuracy compare to Braintrust's LLM-as-judge?
Composo uses a reward-model approach calibrated to your domain rather than a generic LLM-as-judge prompt. In production deployments Composo reaches 90%+ alignment with human domain experts across most contexts, compared to roughly 70% for baseline homegrown LLM-as-judge setups.
How long does Composo take to deploy vs setting up Braintrust?
Braintrust can be integrated in a day if you write simple evals. A full production evaluation system typically takes 3 to 6 months for an internal team to build out. Composo deploys a calibrated, domain-specific quality layer in 2 to 4 weeks, including the initial failure taxonomy from your traces.
Does Composo work with LangChain, LangGraph, or custom agent frameworks?
Yes. Composo is framework-agnostic and integrates with any tracing pipeline - LangSmith, Langfuse, Datadog, or custom. If you are on LangGraph specifically, Composo has a dedicated agent evaluation guide.
See what Composo catches on your own AI.
A clinical-quality failure report on your production AI, delivered in under a week.