Braintrust
AI observability for shipping quality AI at scale
For teams shipping AI in production, Braintrust fills a real gap: it treats AI failures as systemic, not just logging. With Topics out of beta and a free Starter plan, it's more accessible than ever. If you're still manually searching logs for drifts, Braintrust will pay for itself quickly.
Verified 17d ago · liveness 95/100 · cite: rightaichoice.com/tools/braintrust
- Engineering teams shipping AI in production needing real-time trace visibility and eval-driven quality gates
- Product managers and AI leads overseeing multi-model pipelines with complex agent loops
- Enterprises requiring HIPAA/GDPR compliance and hybrid deployment for data residency
- Teams scaling from first agent to hundreds of experiments needing automated pattern discovery
- Simple single-prompt applications that don't need multi-step tracing or eval pipelines
- Teams still in early prototype phase without production traffic or compliance requirements
- Users deeply invested in a single framework (e.g., LangChain) who prefer tight integration over agnostic tooling
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Skip Braintrust if you are only building simple single-prompt applications that don't need multi-step tracing or eval pipelines.
Overage: $4/GB for additional processed data beyond included 1 GB (Starter) or 5 GB (Pro).
Braintrust's freemium model is accessible for small teams: Starter is free with 1 GB data and 10K scores. Pro at $249/mo suits growing AI-native teams. Enterprise is custom-priced for high volume and compliance needs. Compared to competitors like LangSmith (usage-based) or Arize (higher entry cost), Braintrust's Starter plan offers a low-risk entry, but high-volume users may find overage costs add up.
In short
Braintrust — AI observability for shipping quality AI at scale. Best for Engineering teams shipping AI in production needing real-time trace visibility and eval-driven quality gates, Product managers and AI leads overseeing multi-model pipelines with complex agent loops, Enterprises requiring HIPAA/GDPR compliance and hybrid deployment for data residency. Free to start; paid plans from $249/mo.
What's new in Braintrust
Checked 5 days agoAcross the latest 10 updates: 9 feature updates and 1 news mention.
How we chose the model behind Topics with Baseten
Braintrust partners with Baseten to choose the model powering Topics.
Evaluating the GPT-5.6 family
Benchmarks and evaluation of GPT-5.6 model family performance.
Evaluating speech-to-text models
Best practices for evaluating speech-to-text models with Braintrust.
Evaluating the USA vs Belgium World Cup matchup
Use case: evaluating a World Cup matchup with Braintrust evals.
From World Cup matchups to research maps: evaluating Parallel's web research agents
How to evaluate web research agents using Braintrust.
Using OSS models to save on inference costs without cutting quality
Guide to using open-source models to reduce inference costs while maintaining quality.
Agent observability for startups
Introducing agent observability features tailored for startup needs.
Benchmarking GLM-5.2 vs Opus 4.8 for real-world long-context retrieval
Engineering deep-dive comparing GLM-5.2 and Opus 4.8 on long-context tasks.
How to eval stateful agents
Best practices for evaluating stateful AI agents with Braintrust.
How we made continuous trace intelligence possible at scale
Engineering details on building continuous trace intelligence at scale.
Viability Score
How likely is Braintrust to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Real-time trace inspection for prompts, responses, tool calls
- Eval-driven quality measurement with LLM, code, or human scoring
- Automatic pattern discovery with Topics (GA)
- Online scoring and quality gates to block bad releases
- Custom annotation interfaces for team-specific workflows
- One-click conversion of production traces to eval datasets
- Loop AI agent for generating prompts, scorers, and datasets
- Custom facets for business-specific dimensions (use case, segment)
- Brainstore database for complex AI traces at scale
- Metrics tracking for latency, cost, and quality
- Versioned datasets for experiment comparison
- Side-by-side prompt and model comparison
- MCP server for IDE integration
- Real-time performance monitoring dashboards
- Built-in support for GLM-5.2 model (through July 31, 2026)
About Braintrust
Braintrust is an AI observability platform built for engineering teams shipping production AI applications. It treats AI failures as systemic—silent drift, regressions, and complex multi-step traces—rather than just logs. The platform combines real-time trace inspection, eval-driven quality measurement, and automated pattern discovery via Topics (GA). Trusted by teams at Coursera, Notion, and Graphite, Braintrust works with any framework and provides native SDKs for Python, TypeScript, Go, Ruby, C#, and Java. At its core, Braintrust's custom Brainstore database handles complex AI traces with superior performance, enabling fast full-text search, low write latency, and quick span load times. Key capabilities include real-time trace inspection for prompts, responses, and tool calls; eval-driven scoring using LLMs, code, or human evaluation; automatic pattern discovery with Topics; online scoring and quality gates to block bad releases; the Loop AI agent for generating prompts and scorers; custom annotation interfaces; and one-click trace-to-dataset conversion. Security features include SOC 2 Type II, GDPR, HIPAA compliance, SSO, RBAC, and hybrid deployment. A free Starter plan offers core evaluation and tracing for small teams with generous included usage. Pro ($249/month) and Enterprise (Custom) tiers add higher data limits, longer retention, and advanced features like custom charts, environments, and RBAC. Usage-based add-ons are available for Topics, processed data, and scores. Compared to alternatives like LangSmith or Weights & Biases, Braintrust offers stronger automated pattern discovery and a purpose-built database, making it a solid choice for teams that need systemic AI observability. Its framework-agnostic approach and flexible deployment options (including hybrid) appeal to enterprises with compliance requirements.
Behind the Verdict
Braintrust is built for teams that have moved past prototype and into production—where AI failures are silent, systemic, and expensive. The platform's automated pattern discovery (Topics) and eval-driven quality gates directly address the drift and regression problems that standard logging tools miss. The free Starter tier makes it easy to evaluate without commitment, and the Pro tier ($249/mo) is competitive for what you get: 5 GB processed data, 50K scores, 30-day retention, RBAC, and priority support. Where it bites: if you're a solo developer or a team still experimenting with prompts, you probably don't need the full observability stack yet. Braintrust is overkill for simple single-turn applications. Also, paid plans beyond Starter require a sales conversation for Enterprise, which can slow down procurement. The pricing page is transparent about usage-based add-ons (Topics, processed data, scores), so you can estimate costs. Compared to LangSmith, Braintrust has a stronger database (Brainstore) and native pattern discovery. It's also framework-agnostic, so you're not locked into LangChain. If you're already on Weights & Biases for ML experiments, Braintrust feels more AI-specific. Overall, if you're managing multiple models, agent loops, and need to catch regressions before they hit users, Braintrust is worth a serious look.
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Real-world workflow fit
Concrete scenarios for the personas Braintrust actually fits — and what changes day-one when you adopt it.
You deploy a new RAG model. Using Braintrust, you set up real-time tracing, configure an online scoring automation with a quality gate, and use Topics to surface unexpected patterns. You then convert a problematic trace into an eval dataset and experiment with a new prompt.
Outcome: Regressions are caught in staging, not production. The new prompt improves accuracy by 5% and is deployed with confidence.
You need to ensure chatbot responses are helpful and safe. You define custom facets for 'sentiment' and 'policy compliance', set up human review for flagged traces, and use Topics to cluster common user queries.
Outcome: You identify a rising issue with off-topic responses, create a dataset, and collaborate with engineers to fix it before user satisfaction drops.
You start with the free Starter plan. You instrument your AI app with the Python SDK, log a few traces, and run a simple eval to compare two models.
Outcome: You gain visibility into cost and latency. After identifying a cheaper model that maintains quality, you switch and reduce expenses.
Use Cases
- Monitor production LLM traces in real time to catch drift and hallucinations.
- Convert a bug-causing trace into a dataset record for regression testing.
- Run automated evals in CI to block prompt changes that degrade quality.
- Use Loop agent to autonomously generate better prompts and scorers.
- Collaborate with domain experts to review and annotate AI outputs.
- Compare two model versions side-by-side with hundreds of test cases.
Models Under the Hood
as of 2026-07-14
Limitations
- The free tier offers limited processed data (1 GB) and scores (10K), with only 14-day retention.
- High-volume usage can become costly at $4/GB and $2.50/1K scores.
- Human review scores are limited to 1 per project on the Starter plan.
- Custom topics, charts, and environments require the Pro plan.
as of 2026-06-23
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Braintrust tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Starter
$0/mo
Ideal for
Solo devs or small teams just starting with AI evaluation who want free core tracing and evals.
What this tier adds
Free entry point with 1 GB data, 10K scores, 14-day retention, and unlimited users.
Pro
$249/mo
Ideal for
AI-native teams that need longer retention (30 days), custom charts, environments, and priority support.
What this tier adds
Adds 5 GB data, 50K scores, custom topics/charts/environments, SAML SSO, and basic RBAC.
Enterprise
Custom
Ideal for
Large organizations or those with compliance needs (HIPAA, custom retention, S3 export, SLA).
What this tier adds
Custom data retention, S3 export, HIPAA BAA, RBAC, uptime SLA, and dedicated support.
Where the pricing makes sense
The company stage and team size where Braintrust's pricing actually pencils out — and where peers do it cheaper.
Braintrust's freemium model is accessible for small teams: Starter is free with 1 GB data and 10K scores. Pro at $249/mo suits growing AI-native teams. Enterprise is custom-priced for high volume and compliance needs. Compared to competitors like LangSmith (usage-based) or Arize (higher entry cost), Braintrust's Starter plan offers a low-risk entry, but high-volume users may find overage costs add up.
Setup time & first value
How long it actually takes to get something useful out of Braintrust — broken out by persona, not the marketing-page minute.
For individual developers: under 5 minutes to sign up, install SDK, and log your first trace. For teams: a few hours to integrate SDKs, set up projects, and configure evals. Enterprise onboarding with custom deployment can take a week.
Switching to or from Braintrust
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: Use Braintrust's MCP server or SDKs to re-instrument traces; manually import datasets via CSV or API.
- →From Weights & Biases: Export experiments as datasets and re-run evals in Braintrust playgrounds.
- →From custom logging: Use Braintrust's Python/TS SDKs to send traces; set up new evals for quality gates.
- →From Excel/CSV: Use Braintrust's dataset import to bring in test cases and start evals.
- ↗To LangSmith: Export datasets and experiment results via API; re-instrument with LangSmith SDK.
- ↗To Weights & Biases: Use W&B's API to log data from Braintrust exports.
- ↗To custom database: Use S3 export (Enterprise) to export traces for migration.
Integrations
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