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Machine Learning · head to head

Apache Spark MLlib vs Together AI

Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

The machine learning library inside Apache Spark, for data that will not fit on one machine

From
Free
Rated
-
Full reviewWebsite
Together AI logo

Together AI

AI

Open-source AI at scale

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Spark MLlib the algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.; Together AI free tier limits not clearly specified in pricing documentation
  • They diverge on capability: Apache Spark MLlib covers DataFrame-based pipelines, Together AI covers Open-source models.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark MLlib and Together AI differ
AttributeApache Spark MLlibTogether AI
Pricing modelopen-sourceusage-based
PlatformsLinux, macOS, WindowsApi, Cloud
CategoryMachine LearningAI
Founded19992022

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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

  • DataFrame-based pipelines
  • Distributed algorithms
  • Alternating least squares
  • Feature transformers
  • Model selection
  • Pipeline persistence
  • Language bindings
  • Runs in existing Spark deployments

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

  • Training on a data set too large to hold on one machine, where sampling down would lose the rare events you care aboutnot Together AI
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Together AI
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Together AI
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Together AI

Together AI

  • LLM inference for production AI applicationsnot Apache Spark MLlib
  • Content generation at scalenot Apache Spark MLlib
  • Code execution and embeddingsnot Apache Spark MLlib
  • Model fine-tuning and trainingnot Apache Spark MLlib
  • Startup and enterprise AI deploymentnot 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

  • The algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • There is no deep learning in MLlib; neural network work on Spark requires a separate integration, and the DataFrame-centred interface is an awkward fit for it.
  • Fitted models serialise into Spark's own format, so low-latency serving needs either a Spark session in the request path, which is far too slow, or a conversion through ONNX or MLeap, and this is where most Spark ML projects stall.
  • Debugging is JVM cluster debugging: executor out-of-memory, shuffle spill, skewed partitions and serialisation failures, so an engineer without Spark operations experience spends more time tuning the cluster than improving the model.
  • The cluster is the real cost and Spark holds executors for the duration of a job, so a badly partitioned training run pays for idle cores across the whole fleet while one straggler task finishes.

Together AI

  • Free tier limits not clearly specified in pricing documentation
  • Pricing varies significantly by model and use case
  • Requires account setup for production access
  • Batch API discounts apply only to non-urgent workloads

Pricing, plan by plan

Apache Spark MLlib

Free

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

Together AI

Free
  • Serverless Inference$0.03/1M input tokens
    • Chat and Vision models
    • Image generation
    • Video generation
  • Provisioned Throughput$21600/month
    • Up to 83% savings vs commercial alternatives
    • Reserved capacity
    • Guaranteed throughput
  • Dedicated Inference$5.49/hour
    • H100 GPU instance
    • Single-tenant deployment
    • No resource sharing
  • GPU Clusters$3.99/GPU-hour
    • On-demand capacity
    • Volume discounts available
    • Reserved options with up to 35% savings

Which should you pick?

Choose Apache Spark MLlib if

  • You need dataframe-based pipelines.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want distributed algorithms.
Apache Spark MLlib review

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.
Together AI review

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 training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about, feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data, batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does not, organisations that already run and pay for spark, where adding a modelling step is cheaper than introducing a second platform. Of those, training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about and feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data are not what Together AI is typically brought in for.
What can Apache Spark MLlib do that Together AI cannot?
Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers. Together AI covers Open-source models, Fine-tuning, Fast inference, Embeddings.

Answered from the vendors’ own pages

Apache Spark MLlib: What is the difference between spark.ml and spark.mllib?

spark.ml is the DataFrame-based interface and the one to use. spark.mllib is the older RDD-based package, kept for compatibility, in maintenance and receiving no new features.

Together AI: Does Together AI offer a free tier?

Yes, Together AI advertises 'Start for free, scale on demand,' but specific free tier usage limits are not detailed on the pricing page.

Source
Apache Spark MLlib: Do I need a cluster?

Spark runs in local mode on one machine, which is useful for development, but if you are running on one machine you would generally be better served by scikit-learn or XGBoost, which are faster and more capable at that scale.

Together AI: What are Together AI's highest model prices?

Serverless inference pricing ranges from free for base models up to $4.40 per 1M input tokens for premium models. Video generation costs $0.14 to $3.20 per video depending on resolution.

Source
Apache Spark MLlib: Can I use scikit-learn on Spark instead?

Yes, and it is often the better answer. You can distribute independent model fits across the cluster, or use pandas user-defined functions to run per-group models, keeping Spark for the data and a mature library for the modelling.

Together AI: How much can I save with Provisioned Throughput?

Together AI offers up to 83% savings compared to commercial alternatives when using their Provisioned Throughput option with reserved capacity.

Source
Apache Spark MLlib: How do I serve an MLlib model in real time?

Not directly. Either convert the pipeline to a portable format such as ONNX or MLeap, or reimplement the scoring path. Starting a Spark session per request adds seconds of overhead and is not a serving strategy.

Together AI: What is Together AI's fine-tuning pricing?

Standard fine-tuning costs $0.48 to $2.90 per 1M tokens depending on model size, with a minimum charge of $4.00 per job.

Source
Apache Spark MLlib: Is it free?

The library is Apache 2.0 and costs nothing. The cluster it runs on is billed by your cloud provider or by Databricks, and that is the actual expense.

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