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CoreWeave vs Apache Spark MLlib

CoreWeave logo

CoreWeave

AI

Specialized cloud for GPU compute

From
$0.35/per-hour
Free plan
No
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

  • Only Apache Spark MLlib has a free tier, so it costs nothing to try first.
  • Each has a real cost: CoreWeave gPU nodes are sold as full 8 GPU instances rather than single cards, so the entry cost for an H100 node is $49.24 an hour on demand; 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: CoreWeave covers NVIDIA H100/A100, 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 CoreWeave and Apache Spark MLlib actually diverge.

Attributes where CoreWeave and Apache Spark MLlib differ
AttributeCoreWeaveApache Spark MLlib
Starting price$0.35/per-hourFree
Pricing modelusage-basedopen-source
Free tierNoYes
PlatformsCloudLinux, macOS, Windows
CategoryAIMachine Learning
Founded20171999

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 CoreWeave

  • NVIDIA H100/A100
  • Kubernetes native
  • High bandwidth
  • Object storage
  • Kubernetes
  • Terraform
  • Cloud APIs
  • 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.

CoreWeave

  • Renting GPU compute for model training and inferencenot Apache Spark MLlib
  • Running large scale AI workloads without buying hardwarenot 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 CoreWeave
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot CoreWeave
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot CoreWeave
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot CoreWeave

Where each one falls short

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

CoreWeave

  • GPU nodes are sold as full 8 GPU instances rather than single cards, so the entry cost for an H100 node is $49.24 an hour on demand
  • Spot pricing is roughly 40% of on demand, at $19.71 an hour for the same H100 node, so predictable capacity carries a large premium
  • The newest hardware carries no published price and requires contacting sales
  • Discounts of up to 60% require committed usage agreements negotiated with sales
  • Only the GH200 is offered as a single GPU instance

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

CoreWeave

$0.35/per-hour
  • Standard$0.35/per-hour
    • Various GPU types
    • Kubernetes
  • EnterpriseFree
    • Dedicated clusters
    • Custom solutions

Apache Spark MLlib

Free

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

Which should you pick?

Choose CoreWeave if

  • You need nvidia h100/a100.
  • You work on Cloud.
  • You also want kubernetes native.
CoreWeave 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 CoreWeave or Apache Spark MLlib better?
Neither clearly leads. CoreWeave starts at $0.35/per-hour and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, CoreWeave or Apache Spark MLlib?
Apache Spark MLlib has a free tier; the other does not. Paid plans start at $0.35/per-hour for CoreWeave and Free for Apache Spark MLlib.
Does CoreWeave or Apache Spark MLlib run on more platforms?
CoreWeave runs on Cloud. 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. CoreWeave starts at $0.35/per-hour.
What is CoreWeave best used for?
CoreWeave is most often used for renting gpu compute for model training and inference, running large scale ai workloads without buying hardware. Of those, renting gpu compute for model training and inference and running large scale ai workloads without buying hardware are not what Apache Spark MLlib is typically brought in for.
What can CoreWeave do that Apache Spark MLlib cannot?
CoreWeave covers NVIDIA H100/A100, Kubernetes native, High bandwidth, Object storage. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

CoreWeave: How much does CoreWeave cost?

CoreWeave does not publish pricing on its website. The company uses a quote-based pricing model and directs customers to contact their sales team directly to discuss pricing options and customized solutions.

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.

CoreWeave: How can I get a quote from CoreWeave?

To obtain CoreWeave pricing, you must contact their sales team directly through the Contact Us option on their website. They will provide a customized quote based on your specific compute and infrastructure requirements.

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.

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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