Phoenix vs Sentry
Updated March 10, 2026
Overview
Phoenix is an open-source LLM observability and evaluation platform from Arize AI. It supports OpenTelemetry-based tracing across LLM and agent applications, with built-in evaluators, dataset management, and prompt playgrounds. Phoenix can be self-hosted with Docker or run via the Arize-hosted cloud version.
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.
- OpenTelemetry-based LLM and agent tracing
- Built-in evaluators for hallucination and relevance
- Dataset and experiment management
- Prompt playgrounds and versioning
- Self-hosted Docker deployment or Arize cloud
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Open-source with active development by Arize
- OpenTelemetry-native (no proprietary trace format lock-in)
- Strong evaluator library out of the box
- Both self-hosted and managed cloud options available
- Upgrade path to full Arize enterprise platform
Cons
- Smaller community than Langfuse for open-source-first teams
- Cloud version's enterprise pricing is contact-sales only
- UI and feature set tilt toward ML engineers more than application developers
Pros
- Developer-friendly platform
- Production-ready
- Good performance
- Active development
Cons
- Enterprise pricing varies
- Setup complexity
- Learning curve
Phoenix or Sentry — which should you choose?
Choose Phoenix if you wantChoose if you want
- OpenTelemetry-native LLM observability
- Hallucination detection for RAG
- Experiment tracking and golden-set evaluation
- Prompt iteration and comparison
- Self-hosted tracing for compliance-sensitive teams
Choose Sentry if you wantChoose if you want
Compare Phoenix 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 500+ models through one gateway.