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Apache Spark MLlib vs TensorFlow

Apache Spark MLlib logo

Apache Spark MLlib

Software

Scalable machine learning on Apache Spark

From
Free
Rated
-
Full reviewWebsite
TensorFlow logo

TensorFlow

Software

Open-source machine learning framework by Google

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; TensorFlow pyTorch dominates NLP research ecosystem with Hugging Face Transformers starting as PyTorch-only
  • They diverge on capability: Apache Spark MLlib covers Classification, TensorFlow covers Deep learning framework.

Where they differ

Only the attributes on which Apache Spark MLlib and TensorFlow actually diverge.

Attributes where Apache Spark MLlib and TensorFlow differ
AttributeApache Spark MLlibTensorFlow
Pricing modelopen-sourceUnknown
PlatformsLinux, macOS, WindowsPython, JavaScript, C++, Java, Go, Rust
Founded19991998

Identical on both: starting price (Free), free tier (Yes), 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 Apache Spark MLlib

  • Classification
  • Regression
  • Clustering
  • Collaborative filtering
  • Feature engineering
  • Apache Spark
  • Hadoop
  • Kafka

Only in TensorFlow

  • Deep learning framework
  • Neural network training
  • Model deployment
  • TensorBoard visualization
  • Distributed training
  • Keras
  • TensorFlow Lite
  • TensorFlow.js

Both cover

  • Linux support
  • Mac support
  • Windows support

What people use each for

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

Apache Spark MLlib

  • Large-scale distributed machine learning on Spark clustersnot TensorFlow
  • Classification and regression with decision trees, random forests, gradient-boosted treesnot TensorFlow
  • Clustering with K-means and Gaussian Mixture Modelsnot TensorFlow

TensorFlow

  • Machine learningnot Apache Spark MLlib
  • Data analysisnot Apache Spark MLlib
  • Model trainingnot Apache Spark MLlib
  • Predictive analyticsnot Apache Spark MLlib

Where each one falls short

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

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.

TensorFlow

  • PyTorch dominates NLP research ecosystem with Hugging Face Transformers starting as PyTorch-only
  • Broader ecosystem is more complex to navigate for new users compared to PyTorch's more Pythonic API
  • Performance advantage over PyTorch exists mainly at very large scale with TPUs, not for most workloads

Pricing, plan by plan

Apache Spark MLlib

Free

No published plan breakdown. See the Apache Spark MLlib review.

TensorFlow

Free

No published plan breakdown. See the TensorFlow review.

Which should you pick?

Choose Apache Spark MLlib if

  • You need classification.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want regression.
Apache Spark MLlib review

Choose TensorFlow if

  • You need deep learning framework.
  • You want to start without paying.
  • You work on Python, JavaScript, C++, Java, Go, Rust.
  • You also want neural network training.
TensorFlow review

Questions people ask

Is Apache Spark MLlib or TensorFlow better?
Neither clearly leads. Apache Spark MLlib starts at Free and TensorFlow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark MLlib or TensorFlow?
Apache Spark MLlib starts at Free and TensorFlow at Free.
Does Apache Spark MLlib or TensorFlow run on more platforms?
Apache Spark MLlib runs on Linux, macOS, Windows. TensorFlow runs on Python, JavaScript, C++, Java, Go, Rust.
Can I use Apache Spark MLlib for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Spark MLlib best used for?
Apache Spark MLlib is most often used for large-scale distributed machine learning on spark clusters, classification and regression with decision trees, random forests, gradient-boosted trees, clustering with k-means and gaussian mixture models. Of those, large-scale distributed machine learning on spark clusters and classification and regression with decision trees, random forests, gradient-boosted trees are not what TensorFlow is typically brought in for.
What can Apache Spark MLlib do that TensorFlow cannot?
Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering. TensorFlow covers Deep learning framework, Neural network training, Model deployment, TensorBoard visualization. Both handle Linux support, Mac support, Windows support.

Answered from the vendors’ own pages

TensorFlow: Can I run TensorFlow in a web browser?

Yes. TensorFlow.js allows you to develop and deploy machine learning models directly in the browser using JavaScript. It supports both WebGL GPU backend and WebAssembly backends for acceleration.

Source
TensorFlow: Does TensorFlow support deployment on mobile devices?

Yes. TensorFlow Lite enables on-device machine learning on Android, iOS, Raspberry Pi, and embedded systems. LiteRT provides high-performance AI inference for resource-constrained IoT devices.

Source
TensorFlow: What hardware accelerators does TensorFlow support?

TensorFlow supports GPU acceleration and Google's proprietary Tensor Processing Units (TPUs) for specialized matrix operations. Cloud TPUs offer native high-performance support for large-scale machine learning.

Source
TensorFlow: Is TensorFlow free and open-source?

Yes. TensorFlow is completely free and open-source under the Apache 2.0 license. Google released TensorFlow as open-source on November 9, 2015 for anyone to use without licensing costs.

Source
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