Software · head to head
Apache Spark MLlib vs TensorBoard
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.; TensorBoard built and documented as a TensorFlow companion tool; the project's own site presents it as inspecting TensorFlow runs and graphs specifically, per tensorflow.org/tensorboard.
Where they differ
Only the attributes on which Apache Spark MLlib and TensorBoard actually diverge.
| Attribute | Apache Spark MLlib | TensorBoard |
|---|---|---|
| Platforms | Linux, macOS, Windows | Web |
| Founded | 1999 | Unknown |
Identical on both: starting price (Free), pricing model (open-source), 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 TensorBoard
Nothing recorded that Apache Spark MLlib does not also cover.
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 TensorBoard
- Classification and regression with decision trees, random forests, gradient-boosted treesnot TensorBoard
- Clustering with K-means and Gaussian Mixture Modelsnot TensorBoard
TensorBoard
No use cases recorded yet. See the TensorBoard review.
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.
TensorBoard
- Built and documented as a TensorFlow companion tool; the project's own site presents it as inspecting TensorFlow runs and graphs specifically, per tensorflow.org/tensorboard.
- Source is Apache-2.0 licensed on GitHub (github.com/tensorflow/tensorboard), so there is no vendor-hosted paid tier or support contract distinct from the open source project.
Pricing, plan by plan
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
TensorBoard
FreeNo published plan breakdown. See the TensorBoard 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.
Questions people ask
- Is Apache Spark MLlib or TensorBoard better?
- Neither clearly leads. Apache Spark MLlib starts at Free and TensorBoard at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Spark MLlib or TensorBoard?
- Apache Spark MLlib starts at Free and TensorBoard at Free.
- Does Apache Spark MLlib or TensorBoard run on more platforms?
- Apache Spark MLlib runs on Linux, macOS, Windows. TensorBoard runs on Web.
- 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 TensorBoard is typically brought in for.
- What can Apache Spark MLlib do that TensorBoard cannot?
- Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering.
Related pages
More on Apache Spark MLlib
More on TensorBoard
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