Compare OpenLLMetry and Ray on pricing, features, platforms, and ratings — both AI Infrastructure & MLOps tools, Open Source vs Free and open source (Apache…
At a Glance
OpenLLMetry
Ray
Primary category
AI Infrastructure & MLOps
AI Infrastructure & MLOps
Rating
Not documented
Not documented
Pricing model
Open Source
Free and open source (Apache 2.0 license); Anyscale sells a separate paid managed platform built on Ray
Starting price
Free (SDK); optional Traceloop hosted platform has a free tier with paid plans custom-priced
Free (open source); Anyscale's managed platform pricing is usage-based and not publicly listed
Free plan
Not documented
Yes
Free trial
Not documented
Not documented
Platforms
Not documented
Not documented
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.
OpenLLMetry
OpenLLMetry SDK — Free N/A
Traceloop Free Forever — $0/month Monthly
Traceloop Enterprise — Custom Custom
Ray
Ray (Open Source) — Free n/a
Anyscale Platform — Usage-based, not publicly listed custom/contact sales
Pros & Cons
OpenLLMetry
Pros
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
Ray
Pros
Free and open source under the permissive Apache 2.0 license
Scales seamlessly from a single laptop to clusters with thousands of nodes
Used in production by major AI companies, including reported use in ChatGPT-related infrastructure
Comprehensive library ecosystem covering data, training, tuning, and serving in one framework
Large, active open-source community with 1,000+ contributors and 34,800+ GitHub stars
Cons
Distributed systems concepts add a learning curve compared to single-machine Python tools
Self-managing Ray clusters without Anyscale requires nontrivial DevOps expertise
Debugging failures across a distributed cluster can be more difficult than single-node debugging
Enterprise-grade support and managed infrastructure are only available through the separately priced Anyscale platform
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.
Ray
Distributed model training — ML engineers use Ray Train to scale model training across multiple GPUs and nodes without rewriting training code from scratch.
Large-scale batch inference and data processing — Data teams use Ray Data and Ray Core to run distributed ETL and batch inference jobs across large datasets.
LLM and model serving at scale — Teams building AI products use Ray Serve to deploy and scale model and large language model inference in production.