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Lambda Labs vs Apache Spark MLlib

Lambda Labs logo

Lambda Labs

AI

GPU cloud for deep learning

From
$1.1/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: Lambda Labs on demand capacity is first come access rather than guaranteed, so an instance type can be unavailable when needed; 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: Lambda Labs covers NVIDIA 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 Lambda Labs and Apache Spark MLlib actually diverge.

Attributes where Lambda Labs and Apache Spark MLlib differ
AttributeLambda LabsApache Spark MLlib
Starting price$1.1/per-hourFree
Pricing modelusage-basedopen-source
Free tierNoYes
PlatformsCloudLinux, macOS, Windows
CategoryAIMachine Learning
Founded20121999

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 Lambda Labs

  • NVIDIA GPUs
  • Pre-installed frameworks
  • Persistent storage
  • SSH access
  • JupyterLab
  • VSCode
  • SSH
  • 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.

Lambda Labs

  • Renting GPU instances for model training and inferencenot Apache Spark MLlib
  • Short term access to high memory accelerators 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 Lambda Labs
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Lambda Labs
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Lambda Labs
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Lambda Labs

Where each one falls short

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

Lambda Labs

  • On demand capacity is first come access rather than guaranteed, so an instance type can be unavailable when needed
  • H100 pricing varies within a band, at $3.99 to $4.29 an hour per GPU, so the rate is not fixed
  • Reserved capacity is arranged by contacting the team rather than self serve
  • Prices are quoted before applicable tax

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

Lambda Labs

$1.1/per-hour
  • On-Demand$1.1/per-hour
    • A10 GPU
    • Instant availability
  • ReservedFree
    • Volume discounts
    • Guaranteed capacity

Apache Spark MLlib

Free

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

Which should you pick?

Choose Lambda Labs if

  • You need nvidia gpus.
  • You work on Cloud.
  • You also want pre-installed frameworks.
Lambda Labs 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 Lambda Labs or Apache Spark MLlib better?
Neither clearly leads. Lambda Labs starts at $1.1/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, Lambda Labs or Apache Spark MLlib?
Apache Spark MLlib has a free tier; the other does not. Paid plans start at $1.1/per-hour for Lambda Labs and Free for Apache Spark MLlib.
Does Lambda Labs or Apache Spark MLlib run on more platforms?
Lambda Labs 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. Lambda Labs starts at $1.1/per-hour.
What is Lambda Labs best used for?
Lambda Labs is most often used for renting gpu instances for model training and inference, short term access to high memory accelerators without buying hardware. Of those, renting gpu instances for model training and inference and short term access to high memory accelerators without buying hardware are not what Apache Spark MLlib is typically brought in for.
What can Lambda Labs do that Apache Spark MLlib cannot?
Lambda Labs covers NVIDIA GPUs, Pre-installed frameworks, Persistent storage, SSH access. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Lambda Labs: What does Lambda Labs GPU pricing depend on?

Lambda Labs pricing depends on the GPU model (H100, B200, A100, V100, etc.), cluster size, and contract length. For example, a 16-GPU H100 cluster costs $6.16/GPU/hour for 2 weeks to 1 year, while A100 GPUs are $1.99-$2.79/GPU/hour.

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.

Lambda Labs: Are there volume discounts for larger GPU clusters?

Yes. Pricing decreases with larger cluster orders. For example, NVIDIA H100 clusters cost $6.16/GPU/hour for 16 GPUs, $5.85/GPU/hour for 64 GPUs, and $5.54/GPU/hour for 256 GPUs (all for 2 weeks to 1 year terms).

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.

Lambda Labs: Can I get custom pricing for a long-term GPU contract?

Yes. For cluster orders of 16+ GPUs with 1-year or longer contracts, Lambda Labs offers custom pricing. Contact their sales team to request a quote.

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.

Lambda Labs: What additional costs should I expect beyond the hourly GPU rate?

All listed prices are plus applicable sales tax, VAT, or GST depending on your location.

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.

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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