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Machine Learning · head to head

Apache Spark MLlib vs Weights & Biases

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
Weights & Biases logo

Weights & Biases

Machine Learning

Developer tools for machine learning

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Weights & Biases pricing can be prohibitive for large teams without enterprise discounts
  • They diverge on capability: Apache Spark MLlib covers DataFrame-based pipelines, Weights & Biases covers Experiment tracking.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Spark MLlib and Weights & Biases actually diverge.

Attributes where Apache Spark MLlib and Weights & Biases differ
AttributeApache Spark MLlibWeights & Biases
Pricing modelopen-sourceUnknown
PlatformsLinux, macOS, WindowsWeb, Python SDK, REST API
Founded19992017

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).

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 Apache Spark MLlib

  • DataFrame-based pipelines
  • Distributed algorithms
  • Alternating least squares
  • Feature transformers
  • Model selection
  • Pipeline persistence
  • Language bindings
  • Runs in existing Spark deployments

Only in Weights & Biases

  • Experiment tracking
  • Dataset versioning
  • Model registry
  • Hyperparameter sweeps
  • Collaborative dashboards
  • PyTorch
  • TensorFlow
  • Keras

What people use each for

The jobs each tool is most often brought in to do.

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 Weights & Biases
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Weights & Biases
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Weights & Biases
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Weights & Biases

Weights & Biases

  • Machine learningnot Apache Spark MLlib
  • Data analysisnot Apache Spark MLlib
  • Model trainingnot Apache Spark MLlib
  • Predictive analyticsnot Apache Spark MLlib

Where each one falls short

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

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.

Weights & Biases

  • Pricing can be prohibitive for large teams without enterprise discounts
  • Limited integrations compared to some competitors
  • Dashboard customization options limited on lower plans
  • Requires some setup and configuration knowledge

Pricing, plan by plan

Apache Spark MLlib

Free

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

Weights & Biases

Free
  • FreeFree
    • 5 model seats
    • 5 GB storage
    • 1 GB/month Weave ingestion
  • Pro$60/month
    • 10 seats
    • 100 GB storage
    • Private projects
  • Teams$179/month
    • Team collaboration
    • Advanced analytics
    • Dedicated support

Which should you pick?

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

Choose Weights & Biases if

  • You need experiment tracking.
  • You want to start without paying.
  • You work on Web, Python SDK, REST API.
  • You also want dataset versioning.
Weights & Biases review

Questions people ask

Is Apache Spark MLlib or Weights & Biases better?
Neither clearly leads. Apache Spark MLlib starts at Free and Weights & Biases at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark MLlib or Weights & Biases?
Apache Spark MLlib starts at Free and Weights & Biases at Free.
Does Apache Spark MLlib or Weights & Biases run on more platforms?
Apache Spark MLlib runs on Linux, macOS, Windows. Weights & Biases runs on Web, Python SDK, REST API.
Can I use Apache Spark MLlib for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Spark MLlib best used for?
Apache Spark MLlib is most often used for training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about, feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data, batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does not, organisations that already run and pay for spark, where adding a modelling step is cheaper than introducing a second platform. Of those, training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about and feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data are not what Weights & Biases is typically brought in for.
What can Apache Spark MLlib do that Weights & Biases cannot?
Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers. Weights & Biases covers Experiment tracking, Dataset versioning, Model registry, Hyperparameter sweeps.

Answered from the vendors’ own pages

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.

Weights & Biases: Does Weights & Biases have a free plan?

Yes. The Free tier includes 5 model seats, 5 GB storage, and 1 GB/month Weave ingestion. Academic users get unlimited tracked hours, 200 GB storage, and 100 seats at no cost.

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.

Weights & Biases: What are the paid plans for Weights & Biases?

Pro starts at $60/month with 10 seats and 100 GB storage. Team plans start at $179/month. Enterprise pricing is custom.

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.

Weights & Biases: What machine learning features does W&B provide?

Weights & Biases captures hyperparameters, metrics, and model outputs automatically. Features include experiment tracking, interactive Reports for sharing findings, Artifacts for managing datasets and models, advanced hyperparameter sweeps, and model deployment tools.

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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