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Machine Learning · head to head

Domino Data Lab vs Apache Spark MLlib

Domino Data Lab logo

Domino Data Lab

Machine Learning

Enterprise MLOps platform

From
Free
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

  • Each has a real cost: Domino Data Lab pricing is by quote only: the pricing page publishes no rate and no minimum, and the tier breakdown is behind a downloadable datasheet form; 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: Domino Data Lab covers Reproducible environments, 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 Domino Data Lab and Apache Spark MLlib actually diverge.

Attributes where Domino Data Lab and Apache Spark MLlib differ
AttributeDomino Data LabApache Spark MLlib
Pricing modelsubscriptionopen-source
PlatformsWebLinux, macOS, Windows
Founded20131999

Identical on both: starting price (Free), free tier (Yes), 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 Domino Data Lab

  • Reproducible environments
  • Model registry
  • Model monitoring
  • Collaboration
  • Governance
  • AWS
  • Azure
  • GCP

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.

Domino Data Lab

  • Running reproducible data science workspaces and experiments on shared computenot Apache Spark MLlib
  • Deploying and monitoring models with governance controlsnot Apache Spark MLlib
  • Giving regulated enterprises a self managed MLOps platformnot 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 Domino Data Lab
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Domino Data Lab
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Domino Data Lab
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Domino Data Lab

Where each one falls short

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

Domino Data Lab

  • Pricing is by quote only: the pricing page publishes no rate and no minimum, and the tier breakdown is behind a downloadable datasheet form
  • Licensing is split by user type, with separate data science professional, data analyst, service account and admin licences
  • FinOps, Nexus and Governance are paid add on modules rather than part of the platform
  • Support level is a separate priced choice
  • Self managed VPC or on premises deployment requires the Premium tier or higher
  • No free trial is offered on the pricing page

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

Domino Data Lab

Free
  • TrialFree
    • 14-day trial
    • Full features
  • EnterpriseFree
    • Full platform
    • Enterprise support
    • SLA

Apache Spark MLlib

Free

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

Which should you pick?

Choose Domino Data Lab if

  • You need reproducible environments.
  • You want to start without paying.
  • You also want model registry.
Domino Data Lab 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 Domino Data Lab or Apache Spark MLlib better?
Neither clearly leads. Domino Data Lab starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Domino Data Lab or Apache Spark MLlib?
Domino Data Lab starts at Free and Apache Spark MLlib at Free.
Does Domino Data Lab or Apache Spark MLlib run on more platforms?
Domino Data Lab runs on Web. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Domino Data Lab for free?
Both have a free tier, so you can try either at no cost before committing.
What is Domino Data Lab best used for?
Domino Data Lab is most often used for running reproducible data science workspaces and experiments on shared compute, deploying and monitoring models with governance controls, giving regulated enterprises a self managed mlops platform. Of those, running reproducible data science workspaces and experiments on shared compute and deploying and monitoring models with governance controls are not what Apache Spark MLlib is typically brought in for.
What can Domino Data Lab do that Apache Spark MLlib cannot?
Domino Data Lab covers Reproducible environments, Model registry, Model monitoring, Collaboration. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Domino Data Lab: What user license types are available and what can they do?

Data Science Professionals get full development, model training, and GPU access. Data Analysts get Python/R environments and dashboard creation with limited computing. License counts vary by tier (5-10 admin licenses and 5-10 service accounts).

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.

Domino Data Lab: What support response times are included?

Premium tier includes 2-business-day SLA for support. Enterprise includes 1-business-day SLA plus 24/7 support for critical issues. Both tiers include monitoring and support services.

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.

Domino Data Lab: Are there additional modules available beyond the base subscription?

Yes, advanced add-on modules are available including FinOps (cost optimization), Nexus (hybrid/multicloud support), and Governance. These require separate purchase on top of your subscription tier.

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.

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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