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Modal vs Apache Spark MLlib

Modal logo

Modal

AI

Cloud functions for AI and ML

From
Free
Free plan
Yes
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
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: Modal the Team plan carries a $250 monthly base fee and returns only $100 of that as free credits, so $150 is a flat charge before any compute; 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: Modal covers Serverless GPUs, 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 Modal and Apache Spark MLlib actually diverge.

Attributes where Modal and Apache Spark MLlib differ
AttributeModalApache Spark MLlib
Pricing modelusage-basedopen-source
PlatformsCloud, ApiLinux, macOS, Windows
CategoryAIMachine Learning
Founded20211999

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 Modal

  • Serverless GPUs
  • Python functions
  • Auto-scaling
  • Fast cold starts
  • Python SDK
  • GitHub Actions
  • Cloud storage
  • Cloud 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.

Modal

  • Running serverless GPU workloads for model inference and trainingnot Apache Spark MLlib
  • Executing Python functions on cloud compute without managing serversnot 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 Modal
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Modal
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Modal
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Modal

Where each one falls short

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

Modal

  • The Team plan carries a $250 monthly base fee and returns only $100 of that as free credits, so $150 is a flat charge before any compute
  • Compute is billed per second across separate GPU and CPU meters, so total cost depends on execution time rather than any fixed rate
  • The Starter plan's $30 monthly free credit is the only allowance below the paid base fee
  • Enterprise volume discounts are custom and unpublished

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

Modal

Free
  • StarterFree
    • 3 seats
    • 100 containers
    • 10 GPU concurrency
  • Team$250/month
    • Unlimited seats
    • 5,000 containers
    • 50 GPU concurrency
  • Enterprise$null/custom
    • Custom seats, containers, and GPU concurrency

Apache Spark MLlib

Free

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

Which should you pick?

Choose Modal if

  • You need serverless gpus.
  • You want to start without paying.
  • You work on Cloud, Api.
  • You also want python functions.
Modal 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 Modal or Apache Spark MLlib better?
Neither clearly leads. Modal 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, Modal or Apache Spark MLlib?
Modal starts at Free and Apache Spark MLlib at Free.
Does Modal or Apache Spark MLlib run on more platforms?
Modal runs on Cloud, Api. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Modal for free?
Both have a free tier, so you can try either at no cost before committing.
What is Modal best used for?
Modal is most often used for running serverless gpu workloads for model inference and training, executing python functions on cloud compute without managing servers. Of those, running serverless gpu workloads for model inference and training and executing python functions on cloud compute without managing servers are not what Apache Spark MLlib is typically brought in for.
What can Modal do that Apache Spark MLlib cannot?
Modal covers Serverless GPUs, Python functions, Auto-scaling, Fast cold starts. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Modal: How much does Modal cost?

Modal uses pay-as-you-go pricing with Team plan at 250 USD/month base. Starter includes 30 USD/month free credits; Team includes 100 USD/month free credits. Compute charges per second for CPU cores, memory, and GPU instances.

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.

Modal: Is there a free tier?

Yes, Starter plan is free plus 30 USD/month in compute credits included monthly for new users.

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.

Modal: What are the seat limits?

Starter plan includes 3 seats; Team plan provides unlimited seats; Enterprise tier has custom seat allocations.

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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