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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

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: LangGraph steeper learning curve compared to high-level abstractions; 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: LangGraph covers Human-in-the-loop controls, 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 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

  • 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.

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

  • Training on a data set too large to hold on one machine, where sampling down would lose the rare events you care aboutnot LangGraph
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot LangGraph
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot LangGraph
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot 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

  • 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

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 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 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 DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

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: 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.

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: 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.

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: 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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