Compare LiteLLM and Ray on pricing, features, platforms, and ratings — both AI Infrastructure & MLOps tools, Open Source / Freemium (Enterprise custom…
At a Glance
LiteLLM
Ray
Primary category
AI Infrastructure & MLOps
AI Infrastructure & MLOps
Rating
Not documented
Not documented
Pricing model
Open Source / Freemium (Enterprise custom pricing)
Free and open source (Apache 2.0 license); Anyscale sells a separate paid managed platform built on Ray
Starting price
Free (open source); Enterprise pricing on request
Free (open source); Anyscale's managed platform pricing is usage-based and not publicly listed
Free plan
Yes
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.
LiteLLM
Open Source — Free N/A
Enterprise — Custom pricing Annual (typical for enterprise contracts)
Ray
Ray (Open Source) — Free n/a
Anyscale Platform — Usage-based, not publicly listed custom/contact sales
Pros & Cons
LiteLLM
Pros
Core gateway and SDK are completely free and open source with no feature paywall
Extremely wide provider coverage (100-plus LLMs) through one consistent API
Proven at large scale with over one billion production requests and major enterprise users like Netflix and Adobe
Automatic fallback and load balancing improve reliability across providers
Strong observability integrations fit into existing monitoring stacks rather than forcing a new one
Cons
Self-hosting and configuring the proxy for production requires real infrastructure and DevOps effort
Enterprise features (SSO, audit logs, SLAs) require a custom-priced paid plan
Rapidly evolving open-source project can introduce breaking changes between versions
Governance features add complexity that may be unnecessary for small teams using a single provider
Community support model means response times can vary compared to a dedicated commercial vendor
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
LiteLLM
Multi-provider LLM infrastructure — Platform teams centralize access to multiple LLM providers behind one gateway, enabling automatic fallback and cost control across an organization.
AI cost governance and chargeback — Organizations track and attribute LLM spend per team, project, or user to manage budgets across many internal applications.
Provider migration and redundancy — Teams avoid vendor lock-in by routing through LiteLLM, allowing them to switch or add LLM providers without rewriting application code.
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.