Compare OpenLLMetry and Arize Phoenix on pricing, features, platforms, and ratings — both AI Infrastructure & MLOps tools, Open Source vs Free (open source) +…
At a Glance
OpenLLMetry
Arize Phoenix
Primary category
AI Infrastructure & MLOps
AI Infrastructure & MLOps
Rating
Not documented
Not documented
Pricing model
Open Source
Free (open source) + paid managed cloud tier
Starting price
Free (SDK); optional Traceloop hosted platform has a free tier with paid plans custom-priced
Free
Free plan
Not documented
Yes
Free trial
Not documented
Not documented
Platforms
Not documented
Web
Team collaboration
Not documented
Not documented
AI features
Yes
Yes
Public API
Yes
Yes
Pricing Compared
Starting price reflects the lowest paid tier, not the full cost for every team size or usage level.
Fully open source and free under the Apache 2.0 license
Extremely fast to integrate with one-line instrumentation
Vendor-neutral OpenTelemetry foundation avoids lock-in
Large and fast-growing adoption with 500,000-plus monthly installs
Backed by ServiceNow's resources following the 2026 acquisition
Cons
Full monitoring and evaluation platform requires a paid Traceloop plan beyond free-tier limits
Free tier data retention is limited to 24 hours
Enterprise pricing for the hosted platform is not publicly listed
Relatively young product line launched in 2022
Post-acquisition roadmap integration with ServiceNow introduces some uncertainty for standalone users
Arize Phoenix
Pros
Fully open source and self-hostable
Framework- and vendor-agnostic via OpenTelemetry
Active open-source community with 9,000+ GitHub stars
Free managed cloud tier available to start (Arize AX)
Cons
Pricing for production-scale Arize AX deployments isn't publicly listed
Self-hosting and scaling requires infrastructure setup and familiarity with OpenTelemetry
Use Cases
OpenLLMetry
Debugging LLM application behavior — AI engineering teams use OpenLLMetry to trace and debug unexpected LLM outputs, latency spikes, and failures in production.
Enterprise AI governance — Enterprises adopting AI at scale use OpenLLMetry traces to feed governance and compliance workflows.
Rapid observability for AI startups — Startups building LLM-powered products add OpenLLMetry to quickly gain tracing and cost visibility without building custom instrumentation.
Arize Phoenix
LLM application debugging — Trace and inspect exactly what happened during an LLM app's execution.
Agent evaluation and regression testing — Score agent outputs and catch quality regressions before deployment.
Production LLM monitoring — Monitor live LLM applications for failures and quality issues.