Machine Learning · head to head
Ray vs Weights & Biases
Weights & Biases
Machine Learning
Developer tools for machine learning
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Ray windows support is beta and multi node Ray clusters are untested on Windows; Weights & Biases pricing can be prohibitive for large teams without enterprise discounts
- They diverge on capability: Ray covers Distributed computing, Weights & Biases covers Experiment tracking.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Ray and Weights & Biases actually diverge.
| Attribute | Ray | Weights & Biases |
|---|---|---|
| Pricing model | freemium | Unknown |
| Platforms | Linux, Mac, Windows | Web, Python SDK, REST API |
| Founded | 2019 | 2017 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Ray
- Distributed computing
- Ray Train
- Ray Tune
- RLlib
- Ray Serve
- scikit-learn
- Kubernetes
Only in Weights & Biases
- Experiment tracking
- Dataset versioning
- Model registry
- Hyperparameter sweeps
- Collaborative dashboards
- Keras
- Lightning
- Web support
Both cover
- PyTorch
- TensorFlow
- Hugging Face
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
Ray
- Distributed AI model training and servingnot Weights & Biases
- Large-scale data processingnot Weights & Biases
- Reinforcement learning workloadsnot Weights & Biases
- ML inference servingnot Weights & Biases
Weights & Biases
- Machine learningnot Ray
- Data analysisnot Ray
- Model trainingnot Ray
- Predictive analyticsnot Ray
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Ray
- Windows support is beta and multi node Ray clusters are untested on Windows
- Windows lacks copy on write forking, which raises memory requirements, and Ray code assumes UNIX filenames
- Multi node clusters are untested on Apple Silicon Macs
- The Java API is experimental and community supported only, and requires matching Java and Python versions
- Python 3.13 support is beta
Weights & Biases
- Pricing can be prohibitive for large teams without enterprise discounts
- Limited integrations compared to some competitors
- Dashboard customization options limited on lower plans
- Requires some setup and configuration knowledge
Pricing, plan by plan
Ray
Free- Open SourceFree
- Full Ray framework
- All libraries
- Community support
- Anyscale PlatformFree
- Managed infrastructure
- Enterprise support
- SLAs
Weights & Biases
Free- FreeFree
- 5 model seats
- 5 GB storage
- 1 GB/month Weave ingestion
- Pro$60/month
- 10 seats
- 100 GB storage
- Private projects
- Teams$179/month
- Team collaboration
- Advanced analytics
- Dedicated support
Which should you pick?
Choose Ray if
- You need distributed computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want ray train.
Choose Weights & Biases if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Python SDK, REST API.
- You also want dataset versioning.
Questions people ask
- Is Ray or Weights & Biases better?
- Neither clearly leads. Ray starts at Free and Weights & Biases at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Ray or Weights & Biases?
- Ray starts at Free and Weights & Biases at Free.
- Does Ray or Weights & Biases run on more platforms?
- Ray runs on Linux, Mac, Windows. Weights & Biases runs on Web, Python SDK, REST API.
- Can I use Ray for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Ray best used for?
- Ray is most often used for distributed ai model training and serving, large-scale data processing, reinforcement learning workloads, ml inference serving. Of those, distributed ai model training and serving and large-scale data processing are not what Weights & Biases is typically brought in for.
- What can Ray do that Weights & Biases cannot?
- Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Weights & Biases covers Experiment tracking, Dataset versioning, Model registry, Hyperparameter sweeps. Both handle PyTorch, TensorFlow, Hugging Face, Linux support.
Answered from the vendors’ own pages
Ray: Is Ray free?
Yes. Ray is free and open source software with over 34,800 GitHub stars and 1,000+ contributors. Users can download and use the Ray framework at no cost.
SourceWeights & Biases: Does Weights & Biases have a free plan?
Yes. The Free tier includes 5 model seats, 5 GB storage, and 1 GB/month Weave ingestion. Academic users get unlimited tracked hours, 200 GB storage, and 100 seats at no cost.
SourceRay: Is there a paid option for Ray?
Yes. Anyscale, the managed platform built by Ray's creators, offers paid tiers with enterprise features like governance and advanced tooling. Specific Anyscale pricing details are not listed on the Ray website.
SourceWeights & Biases: What are the paid plans for Weights & Biases?
Pro starts at $60/month with 10 seats and 100 GB storage. Team plans start at $179/month. Enterprise pricing is custom.
SourceRay: Can I try Ray with credits?
Yes. New users can try Ray with $100 credit on Anyscale's managed platform to explore the service.
SourceWeights & Biases: What machine learning features does W&B provide?
Weights & Biases captures hyperparameters, metrics, and model outputs automatically. Features include experiment tracking, interactive Reports for sharing findings, Artifacts for managing datasets and models, advanced hyperparameter sweeps, and model deployment tools.
SourceRelated pages
More on Weights & Biases
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