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Machine Learning & Data Science · head to head

Ray vs Weights & Biases

Ray logo

Ray

Machine Learning & Data Science

Scale AI and Python applications

From
Free
Rated
-
Full reviewWebsite
Weights & Biases logo

Weights & Biases

Machine Learning & Data Science

Developer tools for machine learning

From
Free
Rated
-
Full reviewWebsite

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.

Where they differ

Only the attributes on which Ray and Weights & Biases actually diverge.

Attributes where Ray and Weights & Biases differ
AttributeRayWeights & Biases
Pricing modelfreemiumUnknown
PlatformsLinux, Mac, WindowsWeb, Python SDK, REST API
Founded20192017

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).

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

  • Distributing Python workloads across a clusternot Weights & Biases
  • Scaling model training and hyperparameter tuningnot Weights & Biases
  • Serving models and running distributed reinforcement learningnot 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.
Ray review

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.
Weights & Biases review

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 distributing python workloads across a cluster, scaling model training and hyperparameter tuning, serving models and running distributed reinforcement learning. Of those, distributing python workloads across a cluster and scaling model training and hyperparameter tuning 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

Weights & 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.

Source
Weights & 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.

Source
Weights & 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.

Source
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