Braintrust vs Sentry (2026)
Updated March 10, 2026
Overview
Braintrust is an LLM evaluation and observability platform. Compare its evals, tracing, pricing, pros and cons, and alternatives.
Sentry provides runtime error monitoring and performance observability for AI applications. Its LLM monitoring capabilities track model calls, token usage, and latency alongside traditional error tracking. Sentry helps teams catch and debug issues in production AI pipelines with detailed stack traces and context.
Key features
Core capabilities each platform advertises.
- Experiments comparing prompts and models
- Code-based and LLM-as-judge scorers
- Datasets built from production traces
- Production logging and tracing
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Unlimited users on every plan, including free
- Production traces, datasets, and experiments connect in one eval workflow
- Framework-agnostic SDKs across multiple languages
- Loop agent drafts scorers and datasets from your own data
Cons
- Processed data and scores bill separately, and both grow with eval volume
- 14-day retention on Starter and 30 days on Pro
- Gateway is in public preview with no SLA
- Self-hosting and custom data export are Enterprise only
Pros
- Developer-friendly platform
- Production-ready
- Good performance
- Active development
Cons
- Enterprise pricing varies
- Setup complexity
- Learning curve
Braintrust or Sentry — which should you choose?
Choose Braintrust if you wantChoose if you want
- Iterating on prompts with production data
- Running evaluations across model versions
- Building golden datasets from production traffic
- Human review of LLM outputs
Choose Sentry if you wantChoose if you want
Compare Braintrust and Sentry on your own traffic
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 1,000+ models through one gateway.
Try Respan free