Skip to content
aicoolies logo
Braintrust logo

Braintrust

LLM evaluation and prompt engineering platform

Braintrust is an AI observability and evaluation platform for tracing LLM applications, building datasets, running prompt/model experiments, scoring outputs and turning production feedback into regression tests. It fits teams that need repeatable quality gates for AI releases rather than one-off prompt demos.

About Braintrust

Braintrust is an AI observability and evaluation platform for teams building production LLM applications. It brings traces, datasets, prompts, scorers, experiments, dashboards, Topics and human review into one workflow so teams can compare model, prompt and retrieval changes against real examples instead of relying on anecdotal demos.

The current pricing page lists a Starter plan at $0/month with included credits, 1 GB processed data, 10,000 scores and 14-day retention, a Pro plan at $249/month with larger included usage and 30-day retention, and custom Enterprise options. Usage, data volume and score limits should be modeled before large rollouts.

Braintrust is strongest when AI quality is part of release engineering: support agents, retrieval systems, copilots and internal AI tools that change frequently. It is less useful for prototypes with no datasets or recurring regression checks, because the platform still depends on teams defining representative examples and useful scorers.

Pricing & Platform Specs

Pricing Summary

Starter plan is free with unlimited users, $10 in credits, 1 GB data ingestion, 10,000 scores, and 14-day retention. Pro plan is $249/month including $249 in credits, 5 GB data ingestion, 50,000 scores, 30-day retention, and RBAC ($3/GB data and $1.50/1k score overages). Enterprise plan offers custom data retention, VPC/on-premise self-hosted options, and dedicated SLAs.

full pricing breakdown →

Supported Platforms

Web app, API, Python SDK, JavaScript/TypeScript SDK, tracing integrations, eval workflows, dashboards, human review and hosted or on-premise Enterprise options.

Explore categories, tags & use cases

Lightweight server monitoring with Docker stats and alerts

Beszel is a lightweight, self-hosted server monitoring platform built in Go that tracks CPU, memory, disk, network, GPU, temperature, and Docker container metrics with historical data visualization and configurable alerts. Its simple hub-and-agent architecture deploys in minutes and consumes minimal resources compared to traditional monitoring stacks like Prometheus and Grafana.

freeOpen Source

Open-source LLM gateway with built-in optimization and A/B testing

TensorZero is an open-source LLMOps platform in Rust that unifies an LLM gateway, observability, prompt optimization, and A/B experimentation in a single binary. It routes requests across providers with sub-millisecond P99 latency at 10K+ QPS while capturing structured data for continuous improvement. Supports dynamic in-context learning, fine-tuning workflows, and production feedback loops. Backed by $7.3M seed funding, 11K+ GitHub stars.

freeOpen Source

Open-source LLM engineering platform for observability

Langfuse is an open-source LLM engineering platform with 29K+ GitHub stars for tracing, evaluating, and monitoring AI applications. Acquired by ClickHouse, it provides detailed traces of LLM calls, prompt management with versioning, dataset-based evaluation, user feedback collection, and cost tracking. Framework-agnostic with native integrations for LangChain, LlamaIndex, OpenAI SDK, and Vercel AI SDK. Offers both self-hosted deployment and a managed cloud service.

freemiumOpen Source

LLM application observability and evaluation platform

LangSmith is LangChain's platform for debugging, testing, evaluating, and monitoring LLM applications in production. Provides detailed tracing of every step in LLM chains and agent workflows, dataset management for regression testing, prompt versioning, and automated evaluation with custom metrics. Features an annotation queue for human feedback, online monitoring dashboards, and integration with LangChain, LangGraph, and any LLM framework via the Python/JS SDK. Essential for production LLM ops.

freemium

Open-source platform for the complete machine learning lifecycle.

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. Covers experiment tracking, model packaging, model registry, and deployment. Created by Databricks and now a Linux Foundation project. Integrates with TensorFlow, PyTorch, scikit-learn, Hugging Face, and all major ML frameworks.

Open Source

Open-source LLM observability through a single-line proxy

Helicone is an open-source LLM observability and AI gateway platform with proxy-based request logging, cost tracking, latency monitoring, caching, rate limits, user analytics, prompt tools, and HQL. It supports OpenAI, Anthropic, Azure, LiteLLM, Anyscale, Together AI, and OpenRouter integrations, and now presents itself as part of Mintlify while continuing managed and self-hosted gateway/observability workflows.

freemiumOpen Source

Side-by-Side Comparisons

Braintrust logo
Braintrust
vs
Langfuse logo
Langfuse

Braintrust vs Langfuse: Managed Eval Workflow or Open LLM Platform?

Braintrust and Langfuse both connect tracing, datasets, experiments, scoring, and production feedback, but they make different platform tradeoffs. Braintrust emphasizes a polished managed workflow for evaluation-heavy AI teams, while Langfuse combines observability, prompt management, online and offline evaluation, and a free MIT-licensed self-hosted deployment. Langfuse provides the more cohesive complete solution for most teams because it offers a credible managed cloud path without surrendering deployment control or core features. Braintrust remains attractive when a team prioritizes its integrated experiment and annotation workflow and is comfortable standardizing on the managed product.

BraintrustLangfuse
Langfuse logo
Langfuse
vs
Braintrust logo
Braintrust
vs
Monte Carlo logo
Monte Carlo

Monte Carlo vs Langfuse vs Braintrust — AI Observability & Data Quality Platforms Compared

AI observability spans two distinct domains: monitoring the quality of data flowing into AI systems and monitoring the quality of AI outputs themselves. This comparison examines three platforms covering different parts of this spectrum: Monte Carlo as the enterprise leader in data observability that has expanded into AI monitoring, Langfuse as an open-source LLM engineering platform focused on tracing and evaluation, and Braintrust as a modern AI product quality platform with evaluation and prompt management.

Community experience

Sources & verification

Sources checked
Content verified

Verification dates are editorial checks. Routine CMS saves and automatic updatedAt timestamps do not advance them.

FAQ

What is Braintrust?

Braintrust is an AI observability and evaluation platform for tracing LLM applications, building datasets, running prompt/model experiments, scoring outputs and turning production feedback into regression tests. It fits teams that need repeatable quality gates for AI releases rather than one-off prompt demos.

Is Braintrust free?

Braintrust offers a free tier alongside paid plans. Starter plan is free with unlimited users, $10 in credits, 1 GB data ingestion, 10,000 scores, and 14-day retention. Pro plan is $249/month including $249 in credits, 5 GB data ingestion, 50,000 scores, 30-day retention, and RBAC ($3/GB data and $1.50/1k score overages). Enterprise plan offers custom data retention, VPC/on-premise self-hosted options, and dedicated SLAs.

Is Braintrust still maintained?

Yes — Braintrust is active. Its listing was last verified on August 29, 2026.

What are the best Braintrust alternatives?

The first editor-selected Braintrust alternatives are Beszel, TensorZero, Langfuse, and more.

How does Braintrust score in our review?

The published editorial review lists Braintrust at 86/100 overall across speed, privacy, and developer experience. Check the review's evidence status and test metadata for its verification level.