Braintrust

What is Braintrust?

Braintrust is an AI evaluation and observability platform for teams that are already shipping AI features, or are trying to get there without flying blind. It sits in the messy middle of modern AI development: prompts change, models change, retrieval quality drifts, agents call the wrong tools, and a demo that looked good on Friday quietly gets worse in production by Tuesday. Braintrust was built to give teams a way to trace what happened, score output quality, compare versions, and turn production failures into test cases instead of anecdotes.

The company was founded by Ankur Goyal, formerly the CEO of Impira, which Figma acquired, and later head of AI at Figma. That background matters because Braintrust feels less like a research toy and more like infrastructure for product teams. The company has raised significant venture backing, including an $80 million Series B announced in 2026, with investors including ICONIQ, Andreessen Horowitz, and Greylock. We also found SOC 2 Type II certification and HIPAA support in the company materials, which helps explain why Braintrust is courting serious enterprise buyers, not just early-stage AI startups.

In practice, Braintrust is used by teams building LLM apps, RAG systems, and agents that need more than basic logs. It combines tracing, experiments, prompt iteration, datasets, scoring, CI checks, and production monitoring in one system. That is the real pitch here. Not just “see your logs,” but “use the same data and quality checks from development through production.”

Key Features

• Tracing and observability: Braintrust captures detailed traces for prompts, model calls, tool invocations, retrieval steps, latency, tokens, and cost. This matters because AI failures rarely show up as clean error codes. A response can look plausible and still be wrong, and Braintrust gives teams the full execution path needed to figure out where that happened.

• Brainstore data engine: Braintrust says its Brainstore database is built specifically for AI-shaped trace data and delivers median queries up to 80x faster than traditional approaches. For users, that means dashboards and investigations stay usable even when datasets grow into the terabyte range, instead of turning into slow forensic work.

• Offline experiments: Teams can run experiments against curated datasets or real production traces before shipping changes. This is one of the most important parts of the product because it turns “I think this prompt is better” into a measurable comparison with score breakdowns and side-by-side outputs.

• Playground for prompt iteration: Braintrust includes a playground for testing prompt variants quickly, then promoting promising versions into formal experiments. That shortens the loop between idea and evidence, especially for teams that would otherwise need code changes and redeploys to test every prompt tweak.

• Online scoring in production: Braintrust can score live traffic asynchronously, without blocking the user request. That gives teams a way to keep measuring quality after launch, which is where many AI tools fall apart. A model upgrade or retrieval change can quietly hurt answer quality, and online scoring is how teams catch that before support tickets pile up.

• Custom and built-in evaluators: The platform includes more than 25 scorers in its autoevals library, including factuality, faithfulness, moderation, context precision, answer correctness, exact match, JSON diff, and embedding similarity. This matters because AI quality is not one metric. A support bot, a coding assistant, and a legal workflow each need different definitions of “good.”

• RAG evaluation tools: Braintrust includes metrics such as Context Precision, Context Recall, Context Relevancy, Faithfulness, and Answer Correctness. For teams building retrieval-heavy products, these metrics help separate retrieval problems from generation problems, which saves a lot of wasted debugging time.

• Agent evaluation support: Braintrust supports both end-to-end and step-level evaluation for agents, including tool call verification, parameter checks, intermediate reasoning inspection, and goal completion. This matters because agents often fail in the middle, not just at the end, and a final bad result does not tell you whether the real issue was planning, tool selection, or tool interpretation.

• Prompt and function versioning: Prompts, tools, and scorers can be versioned and deployed across environments. Users care about this because it creates an audit trail of what changed and when, and it makes rollback much less painful when a “better” prompt turns out to be worse in production.

• CI/CD quality gates: Braintrust integrates with GitHub workflows so teams can run evaluations on pull requests and block merges when quality drops below thresholds. This is useful for engineering teams trying to treat AI changes with the same discipline as application code, instead of relying on ad hoc manual review.

• Human feedback and review: The platform supports thumbs up/down, ratings, comments, and structured human review workflows. This matters because automated scorers are useful, but many teams still need human judgment to label edge cases, create gold datasets, and check whether the metrics actually match what users care about.

• Loop AI assistant: Braintrust includes Loop, an AI assistant that can query logs and traces in natural language. Instead of writing filters from scratch, teams can ask questions like which traces hallucinated product features or which tool calls failed most often. It is a practical feature for product teams and PMs who need answers but do not want to live in SQL.

• Polyglot SDK support: Braintrust offers SDKs for Python, TypeScript, Java, Go, Ruby, and C#, plus a REST API. That matters for larger companies with mixed stacks, since many AI platforms still feel built mainly for Python teams.

• Deployment options: Braintrust offers cloud, hybrid, and self-hosted enterprise deployment paths. For teams with strict security or data residency requirements, this can be the difference between “interesting tool” and “approved vendor.”

Use Cases

One of the clearest Braintrust use cases is RAG quality work. Teams building retrieval-based applications often struggle to answer a basic question: is the system failing because retrieval found the wrong context, or because the model used the right context badly? Braintrust’s RAG-specific scorers, like Context Precision and Faithfulness, are designed for exactly this. In the research, Braintrust repeatedly positions this as a core workflow, using production traces to inspect retrieved documents, generated answers, and score patterns over time. For teams with support bots, knowledge assistants, or internal search products, that means less guessing and more targeted fixes.

Another strong use case is agent debugging. Braintrust’s own materials focus heavily on the fact that agents fail in layered ways. An agent might choose the wrong tool, pass bad parameters, retry too many times, or misunderstand a tool result. Braintrust supports both end-to-end tests and step-level evaluation, which is important in real projects where “the task failed” is not enough information to improve the system. Teams can trace the whole workflow, inspect intermediate decisions, and score whether each step matched expectations. That is especially relevant for internal ops agents, support automation, and workflow agents tied to business systems.

We also saw Braintrust framed as a release management tool for AI teams. Instead of treating prompt changes, model swaps, and retrieval tweaks as informal edits, teams can run experiments, compare score distributions, and wire those checks into CI. In practice, that means a pull request can include evidence that a change improved factuality by a measurable amount or reduced JSON formatting failures before it ever reaches users. For organizations that have already been burned by silent regressions, this is one of the strongest reasons to adopt Braintrust.

Finally, Braintrust is used as a production feedback loop. The idea is simple but powerful: real production traces become the source material for better test datasets. If users find edge cases, those cases do not stay buried in logs. They can be converted into evaluations and replayed against new versions. That workflow is one of the most compelling parts of the product because it connects actual user behavior to future quality improvements.

Strengths and Weaknesses

Strengths:

• Braintrust is unusually strong when a team wants one place for tracing, evaluation, prompt iteration, and production monitoring. In our research, this was the recurring advantage over point tools. Teams do not have to stitch together separate products for offline evals, production logs, and deployment history, which reduces both manual work and inconsistency.

• The platform goes deeper on evaluation than many observability-first competitors. Datadog, for example, can help teams watch latency, cost, and error rates, but Braintrust is built around whether outputs are actually correct, safe, or useful. That difference matters if your main problem is quality drift rather than infrastructure uptime.

• Braintrust handles agent evaluation better than simpler prompt testing tools. The support for step-level checks, tool-call inspection, and end-to-end scoring gives teams a way to debug multi-step systems without reducing everything to a single thumbs up or thumbs down.

• The language support is broader than most competitors. Python-only tooling still dominates AI infrastructure, but Braintrust supports Python, TypeScript, Java, Go, Ruby, and C#. For larger engineering organizations, that can remove a lot of friction.

• The free tier is meaningful. It includes 1 GB of processed data, 10,000 scores, 14 days of retention, and unlimited users, projects, datasets, playgrounds, and experiments. For a team evaluating the product seriously, that is enough to learn how it works before involving procurement.

Weaknesses:

• Braintrust is not the simplest tool in this category. The same features that make it powerful also create a steeper onboarding curve than lighter-weight products. Teams with a very basic chatbot and modest monitoring needs may find the full workflow heavier than they need.

• Cost planning can get tricky because pricing is tied to processed data and scores, not just seats. A team running agents with lots of spans, plus aggressive online evaluation, can burn through included usage faster than expected. The pricing itself is transparent, but the eventual bill depends a lot on how deeply you instrument and score.

• If you want the strongest possible LangChain-native experience, LangSmith may feel more natural. Braintrust is broader and more stack-agnostic, but LangSmith has a tighter fit for teams already committed to the LangChain ecosystem.

• Braintrust tracks cost, but it is not primarily a cost optimization platform. Teams focused first on token spend analysis and budget controls may still want another tool alongside it, especially if cost attribution is their main concern.

• The proxy approach has tradeoffs. Braintrust’s AI Proxy can centralize routing and observability, but any extra layer in the request path can introduce latency concerns for sensitive applications. Some teams will prefer direct integrations for that reason.

Pricing

• Free: $0 Includes 1 GB of processed data, 10,000 scores, 14 days of trace retention, and unlimited users, projects, datasets, playgrounds, and experiments.

• Pro: $249/month Includes 5 GB of processed data, 50,000 scores, and 30 days of retention.

• Enterprise: Custom Includes custom deployment, enterprise controls, and pricing through sales.

The headline here is that Braintrust is usage-based, not seat-based. That is usually the right model for AI observability, but it means your actual spend depends on how many traces you ingest and how often you score them. Teams running lots of agent workflows can generate much more data than teams logging a simple single-call chatbot.

There are also overage charges on Pro, with research pointing to $4 per additional GB of processed data and $2.50 per 1,000 additional scores. That is not unusual for this category, but it does mean evaluation discipline matters. If you score every production request with multiple evaluators, costs can climb quickly. Compared with alternatives, Braintrust’s entry point is reasonable for a serious product team, but solo builders and tiny startups may stay on the free tier longer or look for open-source-first options.

Alternatives

LangSmith LangSmith is the most obvious alternative if your team already lives inside LangChain. It offers tracing, evaluation, and prompt workflows, and for LangChain-heavy stacks it often feels like the path of least resistance. We would generally point visitors toward LangSmith when they want a code-first workflow tightly tied to that ecosystem. Braintrust is the stronger fit when you want broader language support, more stack independence, and a workflow that treats evaluation and production observability as one connected system.

Galileo Galileo is a better fit for teams that want packaged evaluators and guardrails without necessarily adopting a full tracing-first platform. It has a reputation for strong out-of-the-box evaluation help, especially around safety and runtime checks. Braintrust pulls ahead when you want deeper production trace visibility and tighter links between experiments, deployment decisions, and live monitoring.

Datadog LLM Observability Datadog makes sense for companies already standardized on Datadog and looking to extend existing monitoring habits into AI systems. It is useful for latency, cost, errors, and operational context across infrastructure and AI services. But if your main concern is whether the answers are correct, grounded, and improving over time, Braintrust is much more purpose-built for that job.

DeepEval / Confident AI DeepEval is attractive for teams that prefer open source and want a large library of evaluation metrics inside a developer workflow. Confident AI adds a commercial layer on top of that style of tooling. These are good options if evaluation is the core need and you are comfortable assembling the rest of the stack yourself. Braintrust is the stronger choice if you want tracing, datasets, experiments, CI checks, and production monitoring under one roof.

RAGAS RAGAS is a focused option for teams evaluating retrieval-augmented systems. If your world is mostly RAG and you want specialized metrics without buying into a broader platform, it is a sensible choice. Braintrust is broader. It covers RAG well, but also supports prompt iteration, agent tracing, production monitoring, and deployment workflows that go beyond retrieval evaluation.

FAQ

What is Braintrust used for?

Braintrust is used to evaluate, trace, and monitor AI applications in development and production. Teams use it for prompt testing, RAG evaluation, agent debugging, and catching quality regressions before or after release.

Is Braintrust an observability tool or an evaluation tool?

It is both. That is one of the main reasons people choose it. Braintrust combines production tracing with offline and online evaluation so teams can connect what they tested to what users actually experienced.

Who is Braintrust best for?

It is best for product and engineering teams building real AI features, especially LLM apps, RAG systems, and agents. If quality issues have business impact, Braintrust starts to make a lot more sense.

How do I get started?

Most teams start by instrumenting one workflow with the SDK, capturing traces, and then creating a small evaluation dataset from real examples. From there, they usually test prompt or model changes in the playground and turn the best versions into formal experiments.

How long does it take to set up?

A basic setup can happen in a day if your app is already calling mainstream model APIs. A more complete rollout, with custom scorers, CI checks, and production monitoring, can take longer depending on how much rigor your team wants.

Does Braintrust support agents?

Yes. Braintrust has specific support for agent evaluation, including end-to-end testing, step-level inspection, tool-call checking, and monitoring of multi-step workflows in production.

Can Braintrust evaluate RAG systems?

Yes. It includes RAG-focused metrics like Context Precision, Context Recall, Faithfulness, and Answer Correctness. Those are useful for figuring out whether failures come from retrieval or generation.

Does Braintrust support multiple programming languages?

Yes. Based on our research, Braintrust supports Python, TypeScript, Java, Go, Ruby, and C#, plus a REST API.

Is there a free plan?

Yes. The free tier includes 1 GB of processed data, 10,000 scores, and 14 days of trace retention, along with unlimited users and projects.

What should I watch out for with pricing?

The main thing is usage. Costs depend on processed trace data and scoring volume, so teams with high-traffic apps or complex agents can exceed included limits faster than expected.

Can I self-host Braintrust?

Yes, for enterprise customers. Braintrust offers cloud, hybrid, and self-hosted deployment options for organizations with stricter security or data residency needs.

How does Braintrust compare to LangSmith?

LangSmith is often the easier choice for teams deeply committed to LangChain. Braintrust is more stack-agnostic and stronger as a unified system for tracing, evaluation, and production quality management across different languages and workflows.