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Machine Learning · head to head

Ollama vs Ray

Ollama logo

Ollama

Machine Learning

Open-source tool for running LLMs locally on desktop and servers

From
Free
Rated
-
Full reviewWebsite
Ray logo

Ray

Machine Learning

Scale AI and Python applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Ollama requires user to provide computational hardware; no free cloud compute; models may not fit in available RAM on typical machines; Ray windows support is beta and multi node Ray clusters are untested on Windows
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Ollama and Ray actually diverge.

Attributes where Ollama and Ray differ
AttributeOllamaRay
PlatformsmacOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted)Linux, Mac, Windows
FoundedUnknown2019

Identical on both: starting price (Free), pricing model (freemium), 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 Ollama

Nothing recorded that Ray does not also cover.

Only in Ray

  • Distributed computing
  • Ray Train
  • Ray Tune
  • RLlib
  • Ray Serve
  • PyTorch
  • TensorFlow
  • Hugging Face

What people use each for

The jobs each tool is most often brought in to do.

Ollama

  • Local development and testing without API costs or rate limitsnot Ray
  • Privacy-sensitive applications requiring data to remain on-devicenot Ray
  • Cost-sensitive deployments where computational resources are already availablenot Ray
  • Fully offline environments or air-gapped networksnot Ray

Ray

  • Distributed AI model training and servingnot Ollama
  • Large-scale data processingnot Ollama
  • Reinforcement learning workloadsnot Ollama
  • ML inference servingnot Ollama

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Ollama

  • Requires user to provide computational hardware; no free cloud compute; models may not fit in available RAM on typical machines
  • No hosted service option for inference; all computational burden falls to user
  • Limited to open-weight models; cannot run proprietary models like GPT-4 or Claude locally
  • Performance depends entirely on user's hardware; no SLAs or guarantees on speed

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

Pricing, plan by plan

Ollama

Free
  • FreeFree
    • CLI, API, desktop apps
    • Unlimited public models
    • 40,000+ community integrations
  • Pro$20/month
    • Access to larger, more powerful cloud models
    • Run 3 concurrent cloud models
    • 50x more usage than Free
  • Max$100/month
    • Run 10 concurrent cloud models
    • 5x more usage than Pro
  • Team$25/month
    • Per seat pricing (5-seat minimum = $125/month)
    • Shared billing
    • Zero data retention

Ray

Free
  • Open SourceFree
    • Full Ray framework
    • All libraries
    • Community support
  • Anyscale PlatformFree
    • Managed infrastructure
    • Enterprise support
    • SLAs

Which should you pick?

Choose Ollama if

  • You want to start without paying.
  • You work on macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted).
Ollama review

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

Questions people ask

Is Ollama or Ray better?
Neither clearly leads. Ollama starts at Free and Ray at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Ollama or Ray?
Ollama starts at Free and Ray at Free.
Does Ollama or Ray run on more platforms?
Ollama runs on macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted). Ray runs on Linux, Mac, Windows.
Can I use Ollama for free?
Both have a free tier, so you can try either at no cost before committing.
What is Ollama best used for?
Ollama is most often used for local development and testing without api costs or rate limits, privacy-sensitive applications requiring data to remain on-device, cost-sensitive deployments where computational resources are already available, fully offline environments or air-gapped networks. Of those, local development and testing without api costs or rate limits and privacy-sensitive applications requiring data to remain on-device are not what Ray is typically brought in for.
What can Ollama do that Ray cannot?
Ray covers Distributed computing, Ray Train, Ray Tune, RLlib.

Answered from the vendors’ own pages

Ollama: How much does Ollama cost?

Ollama is free to use with unlimited public models. Pro paid plans start at $20/month for 3 concurrent cloud models, or $100/month for Max with 10 concurrent models. Team plans cost $25/seat/month with a 5-seat minimum.

Source
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.

Source
Ollama: What does the Ollama free tier include?

The free tier includes CLI and API access, unlimited public models, 40,000+ community integrations, and private data retention, though limited to 1 concurrent cloud model.

Source
Ray: 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.

Source
Ollama: How much usage is included with each Ollama plan?

Pro includes 50x more usage than Free, and Max includes 5x more usage than Pro. Session limits reset every 5 hours and weekly limits reset every 7 days across all tiers.

Source
Ray: Can I try Ray with credits?

Yes. New users can try Ray with $100 credit on Anyscale's managed platform to explore the service.

Source
Ollama: Does Ollama log or train on user data?

No, Ollama explicitly states that prompt or response data is never logged or trained on, protecting user privacy across all plans.

Source
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