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Software Development · head to head

Factory vs Apache Spark MLlib

Factory logo

Factory

Software Development

The autonomy stack for enterprise teams

From
$20/month
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

Scalable machine learning on Apache Spark

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: Factory business and Enterprise plans require direct sales contact for pricing; 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.

Where they differ

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

Attributes where Factory and Apache Spark MLlib differ
AttributeFactoryApache Spark MLlib
Starting price$20/monthFree
Pricing modelsubscriptionopen-source
Free tierNoYes
PlatformsWebLinux, macOS, Windows
CategorySoftware DevelopmentMachine Learning
FoundedUnknown1999

Identical on both: 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 Factory

Nothing recorded that Apache Spark MLlib does not also cover.

Only in Apache Spark MLlib

  • Classification
  • Regression
  • Clustering
  • Collaborative filtering
  • Feature engineering
  • Apache Spark
  • Hadoop
  • Kafka

What people use each for

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

Factory

  • AI agent development and deploymentnot Apache Spark MLlib
  • Multi-platform automationnot Apache Spark MLlib
  • Background task executionnot Apache Spark MLlib
  • Agent orchestrationnot Apache Spark MLlib

Apache Spark MLlib

  • Machine learningnot Factory
  • Data sciencenot Factory
  • Distributed computingnot Factory

Where each one falls short

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

Factory

  • Business and Enterprise plans require direct sales contact for pricing
  • Individual plans (Pro, Plus, Max) have limited feature details

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.

Pricing, plan by plan

Factory

$20/month
  • Pro$20/month
    • Agent-native multi-platform experience
    • Desktop, CLI, and SDK access
    • Cloud and local background agents
  • Plus$100/month
    • Everything in Pro
    • Approximately 5x usage limits
    • Droid Computers for remote agents
  • Max$200/month
    • Everything in Plus
    • Approximately 10x usage limits
    • Early access to new features
  • Business$null/mo

Apache Spark MLlib

Free

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

Which should you pick?

Choose Factory if

Nothing in the data separates Factory from Apache Spark MLlib on the points above - pick on price and on how each one feels to use.

Factory review

Choose Apache Spark MLlib if

  • You need classification.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want regression.
Apache Spark MLlib review

Questions people ask

Is Factory or Apache Spark MLlib better?
Neither clearly leads. Factory starts at $20/month and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Factory or Apache Spark MLlib?
Apache Spark MLlib has a free tier; the other does not. Paid plans start at $20/month for Factory and Free for Apache Spark MLlib.
Does Factory or Apache Spark MLlib run on more platforms?
Factory runs on 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. Factory starts at $20/month.
What is Factory best used for?
Factory is most often used for ai agent development and deployment, multi-platform automation, background task execution, agent orchestration. Of those, ai agent development and deployment and multi-platform automation are not what Apache Spark MLlib is typically brought in for.
What can Factory do that Apache Spark MLlib cannot?
Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering.

Answered from the vendors’ own pages

Factory: What is Factory.ai's pricing?

Factory.ai offers individual plans starting at USD$20 per month for Pro, USD$100 per month for Plus, and USD$200 per month for Max. Team plans including Business and Enterprise tiers are available at custom pricing based on the number of seats required.

Source
Apache Spark MLlib: How much does Apache Spark MLlib cost?

MLlib is completely free and open source, licensed under the Apache License Version 2.0. There are no subscription, licensing, or usage fees.

Source
Factory: What are the usage limits for each plan?

The Pro plan is the baseline tier. Plus provides approximately 5 times the usage limits of Pro, while Max provides approximately 10 times the usage limits of Pro. Exact usage limits are not specified on the pricing page.

Source
Apache Spark MLlib: What licensing does MLlib use?

MLlib is licensed under Apache License Version 2.0, making it freely available for all users regardless of organization size or use case.

Source
Factory: What AI models are included with Factory.ai?

All Factory.ai plans include access to GPT-5, Claude Opus and Sonnet, Google Gemini, and open-weight models. Business and Enterprise plans also include dedicated compute with partitioned inference and options for on-premise deployment.

Source
Apache Spark MLlib: How do I use MLlib?

MLlib is built into Apache Spark. Download Spark, which includes MLlib as a module, and deploy on your choice of infrastructure including Hadoop, Mesos, Kubernetes, standalone, or cloud.

Source
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