Braintrust
PlatformFreeAI evaluation and observability — eval framework, tracing, prompt playground, CI/CD integration.
Capabilities13 decomposed
scalable trace ingestion and storage with proprietary brainstore database
Medium confidenceIngests production execution traces (prompts, responses, tool calls, latency, cost metadata) from AI applications via native SDKs (Python, TypeScript, Go, Ruby, C#) and stores them in Braintrust's proprietary Brainstore database optimized for nested AI data structures. The system handles millions of traces with full-text search and supports querying large, deeply-nested trace hierarchies without flattening. Traces are retained for 14 days (Starter), 30 days (Pro), or custom periods (Enterprise), with per-GB pricing ($4/GB overage on Starter, $3/GB on Pro).
Proprietary Brainstore database designed specifically for AI observability with claimed 0.0x faster full-text search and 0.00x faster write latency vs. competitors; handles nested trace structures natively without flattening, enabling structurally-aware queries across multi-turn conversations and chained tool calls
Faster trace querying and storage than generic observability platforms (Datadog, New Relic) because Brainstore is purpose-built for AI trace schemas rather than generic logs
llm-as-judge and code-based evaluation scoring with automated quality gates
Medium confidenceEvaluates AI application outputs using three scoring approaches: (1) LLM-as-judge evaluators that use Claude or GPT-4 to score responses against custom rubrics, (2) code-based scorers written in Python/TypeScript that implement custom logic (regex, semantic similarity, domain-specific checks), and (3) human evaluators who manually score outputs via annotation UI. Scores are tracked per evaluation run with versioning, and automated quality gates can block deployments if scores fall below thresholds. Pricing is per-1k scores ($2.50/1k on Starter, $1.50/1k on Pro, with 10k/50k monthly included respectively).
Unified evaluation framework supporting three scoring modalities (LLM-as-judge, code-based, human) with automatic regression detection in CI/CD pipelines; integrates directly with version control to block deployments based on score thresholds, enabling quality gates without custom orchestration
More integrated than point solutions (Weights & Biases, Arize) because evaluation, tracing, and deployment gates are unified in one platform rather than requiring separate tools
role-based access control (rbac) and saml sso for enterprise compliance
Medium confidenceEnterprise-grade access control with role-based permissions (viewer, editor, admin) and SAML/OAuth SSO integration for identity management. Supports fine-grained permissions on projects, datasets, and evaluations. SAML SSO enables centralized authentication via corporate identity providers (Okta, Azure AD, etc.). Available on Pro/Enterprise tiers; Starter tier has basic roles only. Enterprise tier supports custom RBAC policies and BAA (HIPAA) agreements.
SAML SSO and fine-grained RBAC with HIPAA BAA support; unlike consumer-grade platforms, Enterprise tier enables centralized identity management and compliance-grade access control for regulated industries
More compliant than basic role systems because SAML SSO integrates with corporate identity providers and HIPAA BAA enables handling of protected health information
evaluation result comparison and regression analysis across versions
Medium confidenceCompares evaluation scores across prompt versions, model changes, or time periods to detect regressions and improvements. Generates comparison reports showing score deltas, statistical significance (if applicable), and affected test cases. Supports baseline selection (previous version, main branch, or custom baseline). Regression alerts can be configured to notify teams when scores drop below thresholds. Comparison results are visualized in dashboards and can be exported for reporting.
Automated regression detection across evaluation runs with configurable baselines and alerts; unlike manual comparison, regression analysis is integrated into the evaluation workflow and can block deployments if thresholds are violated
More integrated than external analytics tools because regression detection is built into the evaluation platform rather than requiring post-hoc analysis
compliance and security certifications with data governance
Medium confidenceProvides SOC 2 Type II, GDPR, and HIPAA compliance certifications with Business Associate Agreement (BAA) available on Enterprise tier. Implements data governance controls including encryption, access logging, and data residency options. Supports on-premises or hosted deployment for Enterprise customers requiring data sovereignty.
Provides multiple compliance certifications (SOC 2, GDPR, HIPAA) as standard features rather than add-ons, treating compliance as a core platform concern. On-premises deployment option enables data sovereignty for regulated industries.
More compliant than generic observability platforms because it's specifically designed for regulated industries; more flexible than cloud-only solutions because on-premises deployment is available for Enterprise customers.
interactive prompt playground with a/b comparison and environment tagging
Medium confidenceWeb-based IDE for iterating on prompts with real-time execution against live LLM APIs (OpenAI, Anthropic, etc.). Supports side-by-side A/B comparison of prompt versions, variable templating, and environment-specific configuration (dev/staging/prod with different models or parameters). Prompt versions are automatically versioned and tagged with metadata (author, timestamp, environment). Playground annotations enable inline comments on prompt iterations. Available on Pro tier and above; Starter tier has no playground access.
Integrated playground with environment-aware prompt versioning and A/B comparison UI; unlike standalone prompt editors, versions are automatically linked to evaluation results and deployment history, enabling traceability from prompt iteration to production performance
More integrated than PromptHub or Prompt.com because playground results are directly comparable to evaluation scores and production traces in the same platform
versioned dataset management with test case organization and export
Medium confidenceCentralized repository for organizing evaluation test cases (inputs, expected outputs, metadata) with automatic versioning and branching. Datasets can be created from production traces (sampling real user inputs), manually uploaded (CSV/JSON), or generated by the Loop agent. Datasets are tagged with metadata (version, author, creation date) and can be filtered by attributes. Supports exporting datasets for use in external evaluation frameworks. Dataset versions are immutable, enabling reproducible evaluations across time.
Immutable dataset versioning with automatic sampling from production traces; unlike generic test management tools, datasets are directly linked to evaluation runs and prompt versions, enabling traceability of which test set was used for each evaluation decision
More integrated than external test frameworks (pytest, Jest) because datasets are versioned alongside evaluation results and prompt history in a single system
ci/cd integration with automated regression detection and deployment gates
Medium confidenceIntegrates with CI/CD pipelines (GitHub Actions, GitLab CI, etc.) to automatically run evaluations on prompt or model changes and block deployments if quality scores regress below configured thresholds. Compares current evaluation results against baseline (previous version or main branch) and generates pass/fail reports. Supports custom quality gates (e.g., 'accuracy must stay above 90%' or 'latency must not increase by >10%'). Integration is framework-agnostic and triggered via webhook or API calls from CI/CD runners.
Automated regression detection integrated directly into CI/CD pipelines with configurable quality gates; unlike manual evaluation workflows, changes are automatically evaluated against baselines and deployments are blocked if thresholds are violated, enabling quality gates without human intervention
More automated than manual evaluation processes because regressions are detected before deployment rather than after production issues occur
real-time trace monitoring with full-text search and pattern discovery via topics
Medium confidenceLive dashboard for monitoring production traces in real-time with filtering, sorting, and full-text search across prompt/response content and metadata. 'Topics' feature uses LLM-powered pattern discovery to automatically classify traces into categories (e.g., 'user authentication errors', 'slow API calls') based on custom prompts. Supports custom trace views with annotation interfaces for human review. Alerts can be configured to notify teams when specific patterns emerge or metrics exceed thresholds (latency, cost, error rate). Topics feature available on Pro/Enterprise tiers only.
LLM-powered Topics feature automatically discovers patterns in traces without manual labeling; unlike generic log aggregation (Datadog, Splunk), Topics uses custom prompts to classify AI-specific failure modes (hallucinations, safety violations, performance issues) based on semantic understanding rather than regex patterns
More intelligent than keyword-based alerting because Topics understands semantic patterns in LLM outputs rather than requiring predefined error strings
loop agent for autonomous prompt and dataset optimization
Medium confidenceAI agent that autonomously iterates on prompts, scorers, and datasets to improve evaluation scores. Given a high-level optimization goal (e.g., 'improve accuracy on customer support responses'), Loop generates new prompt variations, creates additional test cases, and runs evaluations to find improvements. Operates in a feedback loop: evaluate → analyze results → generate improvements → re-evaluate. Results are tracked with version history and can be reviewed/approved before deployment. Available on Pro/Enterprise tiers only; Starter tier excluded.
Autonomous agent that generates prompt variations and test cases based on evaluation feedback; unlike manual prompt engineering, Loop explores the optimization space systematically and tracks all iterations with version history, enabling reproducible optimization workflows
More autonomous than manual prompt iteration because Loop generates and evaluates variations automatically rather than requiring human-in-the-loop for each change
multi-provider llm integration with framework-agnostic sdk instrumentation
Medium confidenceFramework-agnostic SDKs (Python, TypeScript, Go, Ruby, C#) that instrument AI applications to send traces to Braintrust without requiring framework-specific adapters. Supports any LLM provider (OpenAI, Anthropic, Cohere, local models) and any AI framework (LangChain, LlamaIndex, custom code). Instrumentation is non-invasive: add a few lines of code to initialize the Braintrust client and wrap LLM calls. SDKs automatically capture prompts, completions, latency, cost, and tool calls. No vendor lock-in at the SDK level; traces can be exported to S3 (Pro/Enterprise only).
Framework-agnostic SDKs that work with any LLM provider and framework without requiring adapter code; unlike framework-specific integrations, Braintrust SDKs capture traces uniformly across heterogeneous stacks (OpenAI + Anthropic + local models) in a single system
Less invasive than framework-specific integrations (LangChain callbacks, LlamaIndex handlers) because SDKs work with any code without framework dependencies
mcp (model context protocol) server for ide-integrated observability and optimization
Medium confidenceBraintrust exposes a Model Context Protocol (MCP) server that connects coding agents and IDEs to the Braintrust platform, enabling queries and operations from within development environments. Supports querying logs/traces, running evaluations, and updating prompts directly from IDE or agent context. Enables use cases like 'ask Claude to analyze my production traces' or 'have an agent automatically run evals and suggest prompt improvements'. MCP integration allows AI agents to autonomously interact with Braintrust data and workflows.
MCP server exposes Braintrust observability and optimization capabilities to AI agents and IDEs; unlike REST APIs, MCP enables agents to autonomously query traces, run evals, and suggest improvements within a single agentic context without context-switching
More integrated with agentic workflows than REST APIs because agents can query and modify Braintrust state directly within their reasoning loop
s3 export for long-term trace archival and downstream analysis
Medium confidenceAutomatically exports traces to customer-owned S3 buckets for long-term storage and analysis outside Braintrust. Enables data retention beyond Braintrust's limits (14/30 days default) and allows integration with downstream analytics tools (Snowflake, BigQuery, custom data pipelines). Export is asynchronous and can be scheduled. Exported traces are in JSON format with full metadata. Available on Pro/Enterprise tiers only; Starter tier excluded.
Automated S3 export enables long-term trace archival outside Braintrust's retention limits; unlike manual export, S3 export can be scheduled and integrated with downstream data pipelines, enabling compliance-grade retention without vendor lock-in
More flexible than Braintrust-only retention because traces can be stored indefinitely in customer-owned S3 and analyzed with external tools
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓AI teams running production applications with high trace volume (100k+ traces/month)
- ✓Companies needing compliance-grade trace retention and audit trails
- ✓Teams using multiple AI frameworks and providers simultaneously
- ✓Teams deploying LLM applications with strict quality requirements (customer-facing, compliance-sensitive)
- ✓Prompt engineers iterating rapidly and needing automated feedback loops
- ✓Organizations requiring human-in-the-loop evaluation for regulatory or safety reasons
- ✓Enterprise organizations with compliance requirements (HIPAA, SOC 2, GDPR)
- ✓Teams with multiple roles and need for fine-grained access control
Known Limitations
- ⚠Data retention capped at 14 days on Starter tier; Pro/Enterprise required for 30+ days
- ⚠Proprietary Brainstore database creates vendor lock-in; S3 export available only on Pro/Enterprise tiers
- ⚠Trace ingestion latency and throughput limits unknown from documentation
- ⚠No on-premises deployment available for Starter/Pro tiers
- ⚠LLM-as-judge scoring depends on external model availability and cost (Claude/GPT-4 API calls not included in Braintrust pricing)
- ⚠Starter tier limited to 1 human review score per project; Pro/Enterprise required for unlimited human scoring
Requirements
Input / Output
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About
AI product evaluation and observability platform. Features eval framework, logging/tracing, prompt playground, and dataset management. Supports CI/CD integration for automated quality checks. Used by major AI companies.
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