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AI · head to head

LangGraph vs Apache Spark MLlib

LangGraph logo

LangGraph

AI

Agent runtime and orchestration framework

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: LangGraph steeper learning curve compared to high-level abstractions; 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: LangGraph covers Human-in-the-loop controls, Apache Spark MLlib covers Classification.

Where they differ

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

Attributes where LangGraph and Apache Spark MLlib differ
AttributeLangGraphApache Spark MLlib
Pricing modelOpen source and free, with optional managed platformopen-source
PlatformsPython, JavaScript, WebLinux, macOS, Windows
CategoryAIMachine 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 LangGraph

  • Human-in-the-loop controls
  • Customizable workflows
  • Memory management
  • Token-by-token streaming
  • Low-level control
  • Multi-agent support

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.

LangGraph

  • Building production AI agents with auditable workflowsnot Apache Spark MLlib
  • Designing multi-agent systems for complex tasksnot Apache Spark MLlib
  • Implementing human oversight in autonomous systemsnot Apache Spark MLlib
  • Creating reliable agentic applications at scalenot Apache Spark MLlib

Apache Spark MLlib

  • Machine learningnot LangGraph
  • Data sciencenot LangGraph
  • Distributed computingnot LangGraph

Where each one falls short

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

LangGraph

  • Steeper learning curve compared to high-level abstractions
  • Requires understanding of graph-based architecture
  • Debugging complex workflows can be challenging
  • Not optimized for simple, one-off use cases

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

LangGraph

Free
  • Open SourceFree
    • MIT-licensed framework
    • Self-hosted deployment
    • Full API access
  • LangGraph Platform$35/month
    • Managed hosting
    • Enterprise deployment
    • Integrated tooling

Apache Spark MLlib

Free

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

Which should you pick?

Choose LangGraph if

  • You need human-in-the-loop controls.
  • You want to start without paying.
  • You work on Python, JavaScript, Web.
  • You also want customizable workflows.
LangGraph 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 LangGraph or Apache Spark MLlib better?
Neither clearly leads. LangGraph 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, LangGraph or Apache Spark MLlib?
LangGraph starts at Free and Apache Spark MLlib at Free.
Does LangGraph or Apache Spark MLlib run on more platforms?
LangGraph runs on Python, JavaScript, Web. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use LangGraph for free?
Both have a free tier, so you can try either at no cost before committing.
What is LangGraph best used for?
LangGraph is most often used for building production ai agents with auditable workflows, designing multi-agent systems for complex tasks, implementing human oversight in autonomous systems, creating reliable agentic applications at scale. Of those, building production ai agents with auditable workflows and designing multi-agent systems for complex tasks are not what Apache Spark MLlib is typically brought in for.
What can LangGraph do that Apache Spark MLlib cannot?
LangGraph covers Human-in-the-loop controls, Customizable workflows, Memory management, Token-by-token streaming. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering.

Answered from the vendors’ own pages

LangGraph: Is LangGraph free to use?

Yes. The core LangGraph framework is MIT-licensed and completely free. You only pay if you use the optional managed LangGraph Platform for hosting.

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
LangGraph: What programming languages does LangGraph support?

LangGraph provides first-class support for Python and JavaScript, enabling cross-platform agent development.

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
LangGraph: Can I deploy LangGraph in production?

Yes. LangGraph can be self-hosted on your own infrastructure or deployed through LangGraph Platform with enterprise support and SLA guarantees.

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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