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Machine Learning · head to head

Google Vertex AI vs Apache Spark MLlib

Google Vertex AI logo

Google Vertex AI

Machine Learning

Unified ML platform to build, deploy, and scale AI models

From
On request
Rated
-
Full reviewWebsite
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

The short version

  • Only Apache Spark MLlib has a free tier, so it costs nothing to try first.
  • Each has a real cost: Google Vertex AI vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult; 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.
  • They diverge on capability: Google Vertex AI covers AutoML, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Google Vertex AI and Apache Spark MLlib differ
AttributeGoogle Vertex AIApache Spark MLlib
Starting priceOn requestFree
Pricing modelUnknownopen-source
Free tierNoYes
PlatformsCloud, WebLinux, macOS, Windows
Founded20081999

Identical on both: user rating (Not yet rated), category (Machine Learning).

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 Google Vertex AI

  • AutoML
  • Custom training
  • Feature Store
  • Model monitoring
  • Prediction serving
  • BigQuery
  • Cloud Storage
  • TensorFlow

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

What people use each for

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

Google Vertex AI

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

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 Google Vertex 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 Google Vertex AI
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Google Vertex AI
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Google Vertex AI

Where each one falls short

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

Google Vertex AI

  • Vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
  • Requires familiarity with Google Cloud Platform infrastructure and concepts
  • Cost can escalate quickly with large training and inference workloads

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.

Pricing, plan by plan

Google Vertex AI

On request

No published plan breakdown. See the Google Vertex AI review.

Apache Spark MLlib

Free

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

Which should you pick?

Choose Google Vertex AI if

  • You need automl.
  • You work on Cloud, Web.
  • You also want custom training.
Google Vertex AI review

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

Questions people ask

Is Google Vertex AI or Apache Spark MLlib better?
Neither clearly leads. Google Vertex AI starts at On request and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Google Vertex AI or Apache Spark MLlib?
Apache Spark MLlib has a free tier; the other does not. Paid plans start at On request for Google Vertex AI and Free for Apache Spark MLlib.
Does Google Vertex AI or Apache Spark MLlib run on more platforms?
Google Vertex AI runs on Cloud, Web. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Apache Spark MLlib for free?
Yes. Apache Spark MLlib has a free tier, so you can try it without paying. Google Vertex AI starts at On request.
What is Google Vertex AI best used for?
Google Vertex AI is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Apache Spark MLlib is typically brought in for.
What can Google Vertex AI do that Apache Spark MLlib cannot?
Google Vertex AI covers AutoML, Custom training, Feature Store, Model monitoring. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Google Vertex AI: What is the pricing model for Google Vertex AI?

Vertex AI uses a pay-as-you-go model with no upfront costs or lock-in fees. Costs vary by service: training is billed by compute resources and time (30-second increments), online predictions by machine type per hour, and batch predictions by compute time or per-record for specific AutoML types.

Source
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.

Google Vertex AI: What types of data can Vertex AI handle?

Vertex AI supports image, video, text, and tabular data types with tools for uploading, storing, and managing large datasets.

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.

Google Vertex AI: Does Vertex AI support custom model training?

Yes. Vertex AI supports both AutoML for automated machine learning and custom training code in Python, R, and other languages.

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.

Google Vertex AI: What deployment options are available in Vertex AI?

Vertex AI supports online predictions for real-time use cases and batch predictions for large-scale processing.

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.

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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