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Software · head to head

Google Vertex AI vs Ray

Google Vertex AI logo

Google Vertex AI

Software

Unified ML platform to build, deploy, and scale AI models

From
On request
Rated
-
Full reviewWebsite
Ray logo

Ray

Software

Scale AI and Python applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only Ray has a free tier, so it costs nothing to try first.
  • Each has a real cost: Google Vertex AI vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult; Ray windows support is beta and multi node Ray clusters are untested on Windows
  • They diverge on capability: Google Vertex AI covers AutoML, Ray covers Distributed computing.

Where they differ

Only the attributes on which Google Vertex AI and Ray actually diverge.

Attributes where Google Vertex AI and Ray differ
AttributeGoogle Vertex AIRay
Starting priceOn requestFree
Pricing modelUnknownfreemium
Free tierNoYes
PlatformsCloud, WebLinux, Mac, Windows
Founded20082019

Identical on both: user rating (Not yet rated), category (Unknown).

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 Google Vertex AI

  • AutoML
  • Custom training
  • Feature Store
  • Model monitoring
  • Prediction serving
  • BigQuery
  • Cloud Storage
  • Dataflow

Only in Ray

  • Distributed computing
  • Ray Train
  • Ray Tune
  • RLlib
  • Ray Serve
  • Hugging Face
  • scikit-learn
  • Kubernetes

Both cover

  • TensorFlow
  • PyTorch

What people use each for

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

Google Vertex AI

  • Machine learningnot Ray
  • Data analysisnot Ray
  • Model trainingnot Ray
  • Predictive analyticsnot Ray

Ray

  • Distributing Python workloads across a clusternot Google Vertex AI
  • Scaling model training and hyperparameter tuningnot Google Vertex AI
  • Serving models and running distributed reinforcement learningnot Google Vertex AI

Where each one falls short

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

Google Vertex AI

  • Vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
  • Requires familiarity with Google Cloud Platform infrastructure and concepts
  • Cost can escalate quickly with large training and inference workloads

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

Google Vertex AI

On request

No published plan breakdown. See the Google Vertex AI review.

Ray

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

Which should you pick?

Choose Google Vertex AI if

  • You need automl.
  • You work on Cloud, Web.
  • You also want custom training.
Google Vertex AI 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 Google Vertex AI or Ray better?
Neither clearly leads. Google Vertex AI starts at On request and Ray at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Google Vertex AI or Ray?
Ray has a free tier; the other does not. Paid plans start at On request for Google Vertex AI and Free for Ray.
Does Google Vertex AI or Ray run on more platforms?
Google Vertex AI runs on Cloud, Web. Ray runs on Linux, Mac, Windows.
Can I use Ray for free?
Yes. Ray has a free tier, so you can try it without paying. Google Vertex AI starts at On request.
What is Google Vertex AI best used for?
Google Vertex AI is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Ray is typically brought in for.
What can Google Vertex AI do that Ray cannot?
Google Vertex AI covers AutoML, Custom training, Feature Store, Model monitoring. Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Both handle TensorFlow, PyTorch.

Answered from the vendors’ own pages

Google Vertex AI: What is the pricing model for Google Vertex AI?

Vertex AI uses a pay-as-you-go model with no upfront costs or lock-in fees. Costs vary by service: training is billed by compute resources and time (30-second increments), online predictions by machine type per hour, and batch predictions by compute time or per-record for specific AutoML types.

Source
Google Vertex AI: What types of data can Vertex AI handle?

Vertex AI supports image, video, text, and tabular data types with tools for uploading, storing, and managing large datasets.

Source
Google Vertex AI: Does Vertex AI support custom model training?

Yes. Vertex AI supports both AutoML for automated machine learning and custom training code in Python, R, and other languages.

Source
Google Vertex AI: What deployment options are available in Vertex AI?

Vertex AI supports online predictions for real-time use cases and batch predictions for large-scale processing.

Source
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