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Machine Learning & Data Science · head to head

BentoML vs Ray

BentoML logo

BentoML

Machine Learning & Data Science

Build production-ready ML applications

From
Free
Rated
-
Full reviewWebsite
Ray logo

Ray

Machine Learning & Data Science

Scale AI and Python applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: BentoML core BentoML framework is Apache 2.0 and free, but the managed BentoCloud enterprise tier has no published pricing: the README instructs buyers to sign up for personal access or contact sales for enterprise use, with no rate card shown.; Ray windows support is beta and multi node Ray clusters are untested on Windows
  • They diverge on capability: BentoML covers Model packaging, Ray covers Distributed computing.

Where they differ

Only the attributes on which BentoML and Ray actually diverge.

Attributes where BentoML and Ray differ
AttributeBentoMLRay

Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), platforms (Linux, Mac, Windows), user rating (Not yet rated), category (Machine Learning & Data Science), founded (2019).

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 BentoML

  • Model packaging
  • REST API generation
  • Adaptive batching
  • Multi-framework support
  • Container deployment
  • XGBoost
  • Docker

Only in Ray

  • Distributed computing
  • Ray Train
  • Ray Tune
  • RLlib
  • Ray Serve
  • Hugging Face
  • Kubernetes

Both cover

  • PyTorch
  • TensorFlow
  • scikit-learn
  • Linux support
  • Mac support
  • Windows support

What people use each for

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

BentoML

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

Ray

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

Where each one falls short

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

BentoML

  • Core BentoML framework is Apache 2.0 and free, but the managed BentoCloud enterprise tier has no published pricing: the README instructs buyers to sign up for personal access or contact sales for enterprise use, with no rate card shown.

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

BentoML

Free
  • Open SourceFree
    • Model packaging
    • API creation
    • Local serving
  • BentoCloudFree
    • Managed deployment
    • Auto-scaling
    • Monitoring

Ray

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

Which should you pick?

Choose BentoML if

  • You need model packaging.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want rest api generation.
BentoML 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 BentoML or Ray better?
Neither clearly leads. BentoML 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, BentoML or Ray?
BentoML starts at Free and Ray at Free.
Does BentoML or Ray run on more platforms?
Both run on Linux, Mac, Windows, so platform support will not decide this one for you.
Can I use BentoML for free?
Both have a free tier, so you can try either at no cost before committing.
What is BentoML best used for?
BentoML 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 BentoML do that Ray cannot?
BentoML covers Model packaging, REST API generation, Adaptive batching, Multi-framework support. Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Both handle PyTorch, TensorFlow, scikit-learn, Linux support.
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