Arize Phoenix
Open-source LLM observability platform built on OpenTelemetry and the OpenInference spec that captures traces and spans, runs evaluations, visualizes embeddings, and performs cluster analysis to identify LLM failure modes.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Self-hosted deployment provides full data residency; no auth by default means the UI should be placed behind a reverse proxy with authentication for production use.
⚡ Reliability
Best When
You want a self-hosted, open-source LLM tracing and evaluation environment with embedding visualization and OpenTelemetry compatibility for deep failure analysis.
Avoid When
You need a managed SaaS platform, enterprise support SLAs, or long-term trace retention without managing your own storage infrastructure.
Use Cases
- • Instrument any LLM framework using the OpenInference instrumentation library and visualize full trace waterfalls in Phoenix UI
- • Run embedding visualizations to cluster agent inputs and identify systematic failure patterns at scale
- • Evaluate trace quality with built-in Phoenix evals (hallucination, relevance, toxicity) using LLM-as-judge
- • Self-host Phoenix on-premise to keep all LLM traces within your security perimeter with no external data egress
- • Export OpenTelemetry spans from Phoenix to Arize cloud for longer-term storage and enterprise dashboards
Not For
- • Teams that need a fully managed cloud service with uptime SLAs and zero infrastructure ownership
- • Real-time business alerting and PagerDuty integrations based on LLM quality metrics
- • Non-Python primary stacks where OpenInference instrumentation libraries are unavailable
Interface
Authentication
Self-hosted Phoenix requires no auth by default; Arize cloud uses API key authentication.
Pricing
Phoenix is Apache 2.0 open source; Arize cloud is the commercial managed offering.
Agent Metadata
Known Gotchas
- ⚠ OpenInference instrumentors must be imported before the frameworks they wrap — import order bugs cause silent trace gaps
- ⚠ Self-hosted Phoenix stores data in SQLite by default; high trace volume requires switching to PostgreSQL manually
- ⚠ Embedding visualizations require running a UMAP projection which is CPU-intensive and blocks the UI for large datasets
- ⚠ Eval functions make LLM calls using your configured provider — parallel eval runs can exhaust rate limits unexpectedly
- ⚠ Phoenix notebook mode and server mode cannot run simultaneously; agents that spawn a local Phoenix server conflict with existing notebook sessions
Alternatives
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Scores are editorial opinions as of 2026-03-07.