Software · head to head
Ray vs BentoML
The short version
- Each has a real cost: Ray windows support is beta and multi node Ray clusters are untested on Windows; 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.
- They diverge on capability: Ray covers Distributed computing, BentoML covers Model packaging.
Where they differ
Only the attributes on which Ray and BentoML actually diverge.
Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), platforms (Linux, Mac, Windows), user rating (Not yet rated), category (Unknown), 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 Ray
- Distributed computing
- Ray Train
- Ray Tune
- RLlib
- Ray Serve
- Hugging Face
- Kubernetes
Only in BentoML
- Model packaging
- REST API generation
- Adaptive batching
- Multi-framework support
- Container deployment
- XGBoost
- Docker
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.
Ray
- Distributing Python workloads across a clusternot BentoML
- Scaling model training and hyperparameter tuningnot BentoML
- Serving models and running distributed reinforcement learningnot BentoML
BentoML
- 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
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.
Pricing, plan by plan
Ray
Free- Open SourceFree
- Full Ray framework
- All libraries
- Community support
- Anyscale PlatformFree
- Managed infrastructure
- Enterprise support
- SLAs
BentoML
Free- Open SourceFree
- Model packaging
- API creation
- Local serving
- BentoCloudFree
- Managed deployment
- Auto-scaling
- Monitoring
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.
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.
Questions people ask
- Is Ray or BentoML better?
- Neither clearly leads. Ray starts at Free and BentoML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Ray or BentoML?
- Ray starts at Free and BentoML at Free.
- Does Ray or BentoML run on more platforms?
- Both run on Linux, Mac, Windows, so platform support will not decide this one for you.
- 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 BentoML is typically brought in for.
- What can Ray do that BentoML cannot?
- Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. BentoML covers Model packaging, REST API generation, Adaptive batching, Multi-framework support. Both handle PyTorch, TensorFlow, scikit-learn, Linux support.