Software · head to head
Apache Spark MLlib vs Together AI
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.; Together AI fine tuning carries a minimum charge of $4.00 per job regardless of dataset size
- They diverge on capability: Apache Spark MLlib covers Classification, Together AI covers Open-source models.
Where they differ
Only the attributes on which Apache Spark MLlib and Together AI actually diverge.
| Attribute | Apache Spark MLlib | Together AI |
|---|---|---|
| Pricing model | open-source | usage-based |
| Platforms | Linux, macOS, Windows | Api, Cloud |
| Founded | 1999 | 2022 |
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 Together AI
- Open-source models
- Fine-tuning
- Fast inference
- Embeddings
- REST API
- Python SDK
- OpenAI compatible
- Api 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 Together AI
- Classification and regression with decision trees, random forests, gradient-boosted treesnot Together AI
- Clustering with K-means and Gaussian Mixture Modelsnot Together AI
Together AI
- Serverless inference against open source chat, vision, embedding, image and video modelsnot Apache Spark MLlib
- Renting dedicated single tenant H100, H200 or B200 GPU clusters by the hournot Apache Spark MLlib
- Fine tuning open weight models on a per token basisnot 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.
Together AI
- Fine tuning carries a minimum charge of $4.00 per job regardless of dataset size
- Reserved GPU commitments beyond 180 days are priced by contacting sales with no published rate
- Volume and enterprise discounts are quote only with no published threshold
- Reserved dedicated inference pricing is contact sales while only on demand rates of $5.49 to $8.99 per GPU hour are published
Pricing, plan by plan
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
Together AI
Free- FreeFree
- $5 credits
- API access
- Pay-per-use$0.2/per-million-tokens
- All models
- Fine-tuning
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.
Choose Together AI if
- You need open-source models.
- You want to start without paying.
- You work on Api, Cloud.
- You also want fine-tuning.
Questions people ask
- Is Apache Spark MLlib or Together AI better?
- Neither clearly leads. Apache Spark MLlib starts at Free and Together AI at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Spark MLlib or Together AI?
- Apache Spark MLlib starts at Free and Together AI at Free.
- Does Apache Spark MLlib or Together AI run on more platforms?
- Apache Spark MLlib runs on Linux, macOS, Windows. Together AI runs on Api, Cloud.
- 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 Together AI is typically brought in for.
- What can Apache Spark MLlib do that Together AI cannot?
- Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering. Together AI covers Open-source models, Fine-tuning, Fast inference, Embeddings.
Related pages
More on Apache Spark MLlib
More on Together AI
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