Braintrust
Braintrust is an AI observability platform designed to help teams build and ship quality AI products at scale. The platform addresses the unique challenges of AI systems, which fail differently than traditional software—AI drifts, hallucinates, and regresses silently.
Core Capabilities
Braintrust provides a comprehensive solution built around three pillars of AI observability:
- Trace Everything: Inspect prompts, responses, and tool calls in real time with scalable trace ingestion
- Measure Quality with Evals: Score outputs using LLMs, code, or human evaluation
- Catch Issues Early: Block bad releases before they hit production with automations and alerts
Platform Features
The platform enables teams to:
- See what actually happened in production by inspecting every trace
- Drill into tool calls and track latency, cost, and quality in real-time
- Get alerts before users notice something's wrong
- Compare prompts and models side-by-side
- Turn production traces into evaluation datasets
Braintrust serves the entire team from engineering to product, providing a unified platform for AI observability and evaluation. The platform has gained trust from leading AI teams and recently announced an $80M Series B funding round.
Target Audience
AI engineering teams, product teams building AI-powered applications, ML engineers, and organizations deploying LLM-based systems at scale
Key Features
- Real-time trace inspection for prompts, responses, and tool calls
- Scalable trace ingestion
- Quality evaluation with LLMs, code, or human scoring
- Live performance monitoring
- Automations and alerts for early issue detection
- Prompt and model comparison
- Latency, cost, and quality tracking
- Production trace to eval conversion
- Tool call drill-down analysis
- Bad release blocking before production
Use Cases
- Monitoring AI customer support agents in production
- Tracking LLM costs across completion, prompt caching, and other metrics
- Evaluating AI model quality and performance
- Debugging AI application failures and errors
- Comparing different prompts and models for optimization
- Ensuring AI quality across engineering and product teams
- Detecting AI drift, hallucinations, and silent regressions
Pros
- Comprehensive AI observability in one platform
- Real-time monitoring and alerting
- Supports multiple evaluation methods (LLM, code, human)
- Scalable trace ingestion for high-volume production systems
- Trusted by leading AI teams
- Strong funding ($80M Series B) indicating market validation
Cons
- May require technical expertise to set up and configure
- Pricing not publicly listed (contact sales required for enterprise)
Pricing
Free tier available with sign-up; enterprise pricing available through sales contact
