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Machine Learning · head to head

Ray vs Together AI

Ray logo

Ray

Machine Learning

Scale AI and Python applications

From
Free
Rated
-
Full reviewWebsite
Together AI logo

Together AI

AI

Open-source AI at scale

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; Together AI free tier limits not clearly specified in pricing documentation
  • They diverge on capability: Ray covers Distributed computing, Together AI covers Open-source models.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Ray and Together AI actually diverge.

Attributes where Ray and Together AI differ
AttributeRayTogether AI
Pricing modelfreemiumusage-based
PlatformsLinux, Mac, WindowsApi, Cloud
CategoryMachine LearningAI
Founded20192022

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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
  • PyTorch
  • TensorFlow
  • Hugging Face

Only in Together AI

  • Open-source models
  • Fine-tuning
  • Fast inference
  • Embeddings
  • REST API
  • Python SDK
  • OpenAI compatible
  • Api support

What people use each for

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

Ray

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

Together AI

  • LLM inference for production AI applicationsnot Ray
  • Content generation at scalenot Ray
  • Code execution and embeddingsnot Ray
  • Model fine-tuning and trainingnot Ray
  • Startup and enterprise AI deploymentnot 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

Together AI

  • Free tier limits not clearly specified in pricing documentation
  • Pricing varies significantly by model and use case
  • Requires account setup for production access
  • Batch API discounts apply only to non-urgent workloads

Pricing, plan by plan

Ray

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

Together AI

Free
  • Serverless Inference$0.03/1M input tokens
    • Chat and Vision models
    • Image generation
    • Video generation
  • Provisioned Throughput$21600/month
    • Up to 83% savings vs commercial alternatives
    • Reserved capacity
    • Guaranteed throughput
  • Dedicated Inference$5.49/hour
    • H100 GPU instance
    • Single-tenant deployment
    • No resource sharing
  • GPU Clusters$3.99/GPU-hour
    • On-demand capacity
    • Volume discounts available
    • Reserved options with up to 35% savings

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 Together AI if

  • You need open-source models.
  • You want to start without paying.
  • You work on Api, Cloud.
  • You also want fine-tuning.
Together AI review

Questions people ask

Is Ray or Together AI better?
Neither clearly leads. Ray starts at Free and Together AI at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Ray or Together AI?
Ray starts at Free and Together AI at Free.
Does Ray or Together AI run on more platforms?
Ray runs on Linux, Mac, Windows. Together AI runs on Api, Cloud.
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 Together AI is typically brought in for.
What can Ray do that Together AI cannot?
Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Together AI covers Open-source models, Fine-tuning, Fast inference, Embeddings.

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.

Source
Together AI: Does Together AI offer a free tier?

Yes, Together AI advertises 'Start for free, scale on demand,' but specific free tier usage limits are not detailed on the pricing page.

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
Together AI: What are Together AI's highest model prices?

Serverless inference pricing ranges from free for base models up to $4.40 per 1M input tokens for premium models. Video generation costs $0.14 to $3.20 per video depending on resolution.

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
Together AI: How much can I save with Provisioned Throughput?

Together AI offers up to 83% savings compared to commercial alternatives when using their Provisioned Throughput option with reserved capacity.

Source
Together AI: What is Together AI's fine-tuning pricing?

Standard fine-tuning costs $0.48 to $2.90 per 1M tokens depending on model size, with a minimum charge of $4.00 per job.

Source
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