AI observability platform with Loop agent that automatically generates better prompts, scorers, and datasets to optimize LLM applications in production.
A platform for testing and improving AI systems by comparing results across different prompts, models, and configurations.
Braintrust is the only AI observability platform that includes an AI optimizer called Loop agent. While competitors like Langfuse and Helicone focus on monitoring, Braintrust monitors AND automatically improves your AI applications.
Loop agent analyzes your LLM performance data and generates optimized prompts, evaluation functions, and training datasets. You describe what you want to improve ("reduce hallucinations in customer support responses") and Loop creates better prompts and scoring mechanisms without manual prompt engineering.
The platform captures every LLM call, tool usage, and decision path in production. You see exactly why your AI agent chose certain actions, how much each call cost, and which prompts are underperforming. This granular tracing works across any model provider with no markup on token costs.
Real example: An e-commerce company's customer service chatbot had inconsistent tone. Instead of manually testing dozens of prompt variations, they described the desired tone to Loop agent. Within 24 hours, Loop generated 12 prompt improvements and 6 custom scoring functions, raising customer satisfaction scores by 23%.
The evaluation framework runs continuously against production traffic. When quality drops, you know immediately which deployment or prompt change caused the regression. Most teams catch issues hours or days faster than with traditional monitoring.
Source: https://www.braintrust.dev/pricing
Building equivalent observability requires multiple tools: Datadog for monitoring ($15/host/month), custom evaluation scripts (40+ engineering hours at $100/hour), and prompt optimization consulting ($5,000+ per project). That's $9,000+ for basic setup.
Braintrust Pro at $249/month includes monitoring, automated evaluation, prompt optimization, and debugging tools. You save $8,751 on initial setup plus ongoing engineering costs for maintenance and optimization.
The Starter plan offers meaningful functionality at $0/month. With 1 GB storage and 10K scores, you can monitor a moderate-traffic chatbot or API service while testing the platform value.
Engineering teams praise the comprehensive tracing: "Braintrust captures every step of an AI model or agent's reasoning process, including prompts, tool calls, retrieved context and metadata on latency and cost." The platform recently secured $80M in Series B funding, indicating strong market validation.
Users note the engineering focus as both strength and weakness: "Powerful all-in-one platform for AI evaluation" but "everything requires code, non-technical teams struggle." The interface is "well-designed and fast" according to Reddit discussions.
The 14-day retention on free tier gets mixed feedback. Some find it limiting for longer analysis, while others appreciate the generous compute allowance compared to competitors charging for basic monitoring.
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Braintrust combines AI observability with automated optimization through its unique Loop agent. At $249/month for Pro features, it costs less than building equivalent monitoring and optimization infrastructure. The evaluation-first approach and recent $80M funding signal strong market position, though the engineering-focused design may limit non-technical team adoption.
Every eval run is diffed against previous runs. The UI shows which examples improved, regressed, or stayed the same, with score deltas per example. This makes it immediately clear whether a change to your prompt, model, or pipeline helped or hurt.
Use Case:
Running an eval after changing your RAG retrieval strategy and seeing that 15 examples improved but 3 regressed, then investigating those specific regressions.
Built-in scorers for factuality, relevance, SQL correctness, and more. Custom scoring via Python/TypeScript functions. LLM-as-judge with configurable prompts. All scores show distributions and per-example breakdowns.
Use Case:
Creating a custom scorer that checks whether generated SQL queries are syntactically valid and semantically correct against a test database.
Unified API gateway that routes LLM requests across providers while providing caching, rate limiting, cost tracking, and experiment routing. Supports OpenAI, Anthropic, Google, and other providers through a single endpoint.
Use Case:
Routing 50% of production traffic to a new prompt variant and comparing quality scores between the control and variant groups.
Captures production LLM calls as traces with full input/output logging. Traces can be automatically scored using the same evaluators you use offline, creating a continuous quality signal without manual intervention.
Use Case:
Automatically scoring every production response for hallucination and routing low-scoring traces to a human review queue.
Version-controlled datasets that can be created from production traces, manual uploads, or programmatic generation. Datasets support rich metadata and can be shared across team members for collaborative evaluation.
Use Case:
Building a golden dataset from the 200 hardest production queries and using it as a regression test suite for every prompt change.
GitHub Actions integration that runs evaluations on every pull request and posts regression reports as PR comments. Supports configurable quality gates that block merges if evaluation scores drop below thresholds.
Use Case:
Blocking a PR that degrades retrieval accuracy by more than 2% before it can merge to the main branch.
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View Pricing Options →AI product teams needing systematic evaluation infrastructure for model testing and optimization
Organizations deploying multi-step AI agents requiring specialized evaluation frameworks
Development teams converting production AI failures into automated regression tests
Companies needing continuous monitoring and evaluation of AI systems in production environments
Teams building custom AI applications that require domain-specific evaluation metrics
Organizations seeking to replace ad-hoc AI testing with systematic evaluation processes
Braintrust works with these platforms and services:
We believe in transparent reviews. Here's what Braintrust doesn't handle well:
Braintrust adds experiment tracking, regression diffing, score distributions, dataset management, and a UI for reviewing results. Pytest tells you pass/fail; Braintrust shows you exactly how quality changed, which examples regressed, and trends over time. It's the difference between a test suite and an evaluation platform.
It works with any model. The SDK captures inputs and outputs regardless of the model source. The Braintrust proxy supports routing to custom endpoints including local models. You can evaluate open-source models the same way you evaluate GPT-4 or Claude.
They have different strengths. Braintrust excels at evaluation and regression testing. Langfuse excels at operational tracing and prompt management. Many teams use Braintrust for evaluation pipelines and Langfuse for production monitoring. If you must pick one, choose based on whether eval or monitoring is your bigger pain point.
Braintrust uses usage-based pricing. Costs scale with the number of logged events (traces, evaluations, scores). For a startup running daily evals against a few hundred examples, expect $100-500/month. Costs grow with dataset size and evaluation frequency.
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Braintrust raised $80M in Series B funding in February 2026, becoming a major player in AI observability. The company launched Loop agent for automated prompt optimization and established itself as 'the observability layer for AI' according to funding announcements.
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