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Software Development · head to head

DeepSource vs Apache Spark MLlib

DeepSource logo

DeepSource

Software Development

Automated code review and AI-powered code fixes for engineering teams.

From
Free
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

  • Each has a real cost: DeepSource open Source plan caps at 1,000 reviewed pull requests and 1,000 formatting runs per month.; 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.
  • They diverge on capability: DeepSource covers Automated pull request review, Apache Spark MLlib covers Classification.

Where they differ

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

Attributes where DeepSource and Apache Spark MLlib differ
AttributeDeepSourceApache Spark MLlib
Pricing modelfreemiumopen-source
Platformsweb, apiLinux, macOS, Windows
CategorySoftware DevelopmentMachine Learning
FoundedUnknown1999

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 DeepSource

  • Automated pull request review
  • AI-powered autofix
  • Automated code formatting
  • Monorepo support
  • API and webhooks
  • Bring-your-own-key AI

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.

DeepSource

  • Automating pull request code review for engineering teamsnot Apache Spark MLlib
  • Auto-fixing detected code issues with AInot Apache Spark MLlib
  • Enforcing code formatting standards automaticallynot Apache Spark MLlib
  • Scanning large monorepos for quality issuesnot Apache Spark MLlib
  • Running self-hosted AI review in regulated environmentsnot Apache Spark MLlib

Apache Spark MLlib

  • Machine learningnot DeepSource
  • Data sciencenot DeepSource
  • Distributed computingnot DeepSource

Where each one falls short

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

DeepSource

  • Open Source plan caps at 1,000 reviewed pull requests and 1,000 formatting runs per month.
  • AI Review beyond the included credit is billed per 10K lines of code, which can add unpredictable cost.
  • Self-hosted deployment and BYOK AI are Enterprise-only features.
  • Enterprise pricing is not published and requires contacting sales.

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

DeepSource

Free
  • Open SourceFree
    • Free for public repositories
    • 1,000 pull requests reviewed/month
    • 1,000 automated formatting runs/month
  • Team$24/month
    • Unlimited repositories and pull request reviews
    • $100 annual AI Review credit per user
    • Monorepo support
  • Enterprise$undefined/month
    • Self-hosted deployment
    • Bring-your-own-key AI Review
    • SSO

Apache Spark MLlib

Free

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

Which should you pick?

Choose DeepSource if

  • You need automated pull request review.
  • You want to start without paying.
  • You work on web, api.
  • You also want ai-powered autofix.
DeepSource 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 DeepSource or Apache Spark MLlib better?
Neither clearly leads. DeepSource 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, DeepSource or Apache Spark MLlib?
DeepSource starts at Free and Apache Spark MLlib at Free.
Does DeepSource or Apache Spark MLlib run on more platforms?
DeepSource runs on web, api. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use DeepSource for free?
Both have a free tier, so you can try either at no cost before committing.
What is DeepSource best used for?
DeepSource is most often used for automating pull request code review for engineering teams, auto-fixing detected code issues with ai, enforcing code formatting standards automatically, scanning large monorepos for quality issues. Of those, automating pull request code review for engineering teams and auto-fixing detected code issues with ai are not what Apache Spark MLlib is typically brought in for.
What can DeepSource do that Apache Spark MLlib cannot?
DeepSource covers Automated pull request review, AI-powered autofix, Automated code formatting, Monorepo support. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering.

Answered from the vendors’ own pages

DeepSource: What does DeepSource cost?

The Open Source plan is free for public repos; Team is $24 per user/month billed yearly with a $100 annual AI Review credit; Enterprise is custom-priced with self-hosted options.

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
DeepSource: Is there a free plan, and what are its limits?

Yes, the free Open Source plan covers public repositories with 1,000 pull requests reviewed per month and 1,000 automated formatting runs per month.

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.

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DeepSource: How is AI Review usage metered?

Team plans include a $100 annual AI Review credit per user, with additional usage billed at Standard ($8/10K LOC) or Advanced ($15/10K LOC) tiers.

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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
DeepSource: Can I change or cancel my plan?

Yes, subscriptions can be downgraded or canceled at any time.

Source
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