Machine Learning & Data Science · head to head
Ray vs Apache Spark MLlib
Apache Spark MLlib
Machine Learning & Data Science
Scalable machine learning on Apache Spark
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Ray windows support is beta and multi node Ray clusters are untested on Windows; Apache Spark MLlib apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
- They diverge on capability: Ray covers Distributed computing, Apache Spark MLlib covers Classification.
Where they differ
Only the attributes on which Ray and Apache Spark MLlib actually diverge.
| Attribute | Ray | Apache Spark MLlib |
|---|---|---|
| Pricing model | freemium | open-source |
| Platforms | Linux, Mac, Windows | Linux, macOS, Windows |
| Founded | 2019 | 1999 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).
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 Apache Spark MLlib
- Classification
- Regression
- Clustering
- Collaborative filtering
- Feature engineering
- Apache Spark
- Hadoop
- Kafka
Both cover
- 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 Apache Spark MLlib
- Scaling model training and hyperparameter tuningnot Apache Spark MLlib
- Serving models and running distributed reinforcement learningnot Apache Spark MLlib
Apache Spark MLlib
- Large-scale distributed machine learning on Spark clustersnot Ray
- Classification and regression with decision trees, random forests, gradient-boosted treesnot Ray
- Clustering with K-means and Gaussian Mixture Modelsnot 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
Apache Spark MLlib
- Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
Pricing, plan by plan
Ray
Free- Open SourceFree
- Full Ray framework
- All libraries
- Community support
- Anyscale PlatformFree
- Managed infrastructure
- Enterprise support
- SLAs
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
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 Apache Spark MLlib if
- You need classification.
- You want to start without paying.
- You work on Linux, macOS, Windows.
- You also want regression.
Questions people ask
- Is Ray or Apache Spark MLlib better?
- Neither clearly leads. Ray starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Ray or Apache Spark MLlib?
- Ray starts at Free and Apache Spark MLlib at Free.
- Does Ray or Apache Spark MLlib run on more platforms?
- Ray runs on Linux, Mac, Windows. Apache Spark MLlib runs on Linux, macOS, Windows.
- 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 Apache Spark MLlib is typically brought in for.
- What can Ray do that Apache Spark MLlib cannot?
- Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering. Both handle Linux support, Mac support, Windows support.
Related pages
More on Apache Spark MLlib
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