Compare CVAT and Ray on pricing, features, platforms, and ratings — both AI Infrastructure & MLOps tools, Free / Open Source with Freemium SaaS tiers vs Free…
At a Glance
CVAT
Ray
Primary category
AI Infrastructure & MLOps
AI Infrastructure & MLOps
Rating
Not documented
Not documented
Pricing model
Free / Open Source with Freemium SaaS tiers
Free and open source (Apache 2.0 license); Anyscale sells a separate paid managed platform built on Ray
Starting price
Free
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
Web
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.
CVAT
Free — $0 N/A
Solo — $33/month ($23/month annual) Monthly or annual
Team — $33/user/month ($23/user/month annual) Monthly or annual, minimum 2 seats
Enterprise (Self-Hosted) — From $12,000/year Annual
Ray
Ray (Open Source) — Free n/a
Anyscale Platform — Usage-based, not publicly listed custom/contact sales
Pros & Cons
CVAT
Pros
Free and open source (MIT license) with full self-hosting option
Strong video annotation and object-tracking/interpolation tools
Wide dataset format support for ML pipelines
Active open-source community and OpenCV endorsement
Learning curve for teams new to annotation workflows
Paid SaaS tiers add up per seat at larger team sizes
Enterprise self-hosted tier pricing starts fairly high
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
CVAT
Autonomous vehicle and robotics training data — Labeling large volumes of image and video data for perception models.
Medical imaging annotation — Self-hosting CVAT to maintain full data sovereignty over sensitive imaging data.
Retail and agriculture computer vision datasets — Annotating shelf, inventory, crop or livestock imagery for applied ML models.
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.