Softwr

AI · head to head

RunPod vs Apache Spark MLlib

RunPod logo

RunPod

AI

GPU cloud for AI and ML

From
$0.2/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: RunPod idle volume disk storage is billed at $0.20 per GB per month, double the $0.10 per GB per month charged while the pod is running; 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: RunPod covers GPU instances, 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 RunPod and Apache Spark MLlib actually diverge.

Attributes where RunPod and Apache Spark MLlib differ
AttributeRunPodApache Spark MLlib
Starting price$0.2/per-hourFree
Pricing modelusage-basedopen-source
Free tierNoYes
PlatformsCloud, ApiLinux, macOS, Windows
CategoryAIMachine Learning
Founded20221999

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 RunPod

  • GPU instances
  • Serverless
  • Templates
  • Persistent storage
  • Docker
  • REST API
  • SSH access
  • 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.

RunPod

  • Renting GPU compute by the second for model training and inferencenot Apache Spark MLlib
  • Running serverless GPU workers that scale with request volumenot Apache Spark MLlib
  • Attaching persistent network storage shared across GPU podsnot 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 RunPod
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot RunPod
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot RunPod
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot RunPod

Where each one falls short

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

RunPod

  • Idle volume disk storage is billed at $0.20 per GB per month, double the $0.10 per GB per month charged while the pod is running
  • Reserved clusters of all terms from 1 to 12 months are priced by contacting sales with no published rate
  • L40S, H100 SXM and B200 cluster configurations are listed as contact sales rather than at a published hourly rate
  • High performance network storage costs $0.14 per GB per month, twice the standard sub 1TB rate of $0.07

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

RunPod

$0.2/per-hour
  • Community Cloud$0.2/per-hour
    • Affordable GPUs
    • Spot instances
  • Secure Cloud$0.44/per-hour
    • Enterprise security
    • Dedicated hardware

Apache Spark MLlib

Free

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

Which should you pick?

Choose RunPod if

  • You need gpu instances.
  • You work on Cloud, Api.
  • You also want serverless.
RunPod 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 RunPod or Apache Spark MLlib better?
Neither clearly leads. RunPod starts at $0.2/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, RunPod or Apache Spark MLlib?
Apache Spark MLlib has a free tier; the other does not. Paid plans start at $0.2/per-hour for RunPod and Free for Apache Spark MLlib.
Does RunPod or Apache Spark MLlib run on more platforms?
RunPod runs on Cloud, Api. 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. RunPod starts at $0.2/per-hour.
What is RunPod best used for?
RunPod is most often used for renting gpu compute by the second for model training and inference, running serverless gpu workers that scale with request volume, attaching persistent network storage shared across gpu pods. Of those, renting gpu compute by the second for model training and inference and running serverless gpu workers that scale with request volume are not what Apache Spark MLlib is typically brought in for.
What can RunPod do that Apache Spark MLlib cannot?
RunPod covers GPU instances, Serverless, Templates, Persistent storage. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

RunPod: What is the pricing model for Runpod GPU compute?

Runpod uses usage-based pricing billed per second rather than fixed subscriptions. GPU pod pricing ranges from $0.27/hour for budget options like RTX A5000 to $7.89/hour for high-end options like B300. Serverless inference is billed based on worker usage.

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.

RunPod: Are there minimum contracts or commitments required to use Runpod?

No minimum contracts or commitments are required for on-demand services. Per-second billing is available, and you pay only for what you use. Long-term commitments offer additional savings through reserved capacity options.

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.

RunPod: What are the storage costs on Runpod?

Storage pricing is tiered: Container Disk costs $0.10/GB/month, Volume Disk costs $0.10/GB/month when running or $0.20/GB/month when idle, and Network Storage ranges from $0.05-$0.07/GB/month for standard to $0.14/GB/month for high-performance.

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.

RunPod: Does Runpod charge egress fees for data transfer?

No, Runpod does not charge egress fees when using persistent network storage, which helps reduce data transfer costs for workloads that need to move data in and out frequently.

Source
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.

RunPod: How does Runpod Serverless handle cold starts and idle costs?

Runpod Serverless offers zero idle cost and sub-200ms cold starts via FlashBoot technology. There is no warm-up tax, meaning you don't pay for idle capacity or accept cold-start latency penalties.

Source
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.

Share

Related pages

More on RunPod

More on Apache Spark MLlib

Keep looking

Other head to heads