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Machine Learning · head to head

KNIME vs Apache Spark MLlib

KNIME logo

KNIME

Machine Learning

Open source data analytics and integration platform

From
Free
Rated
-
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
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: KNIME the free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub; 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: KNIME covers Visual workflows, 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 KNIME and Apache Spark MLlib actually diverge.

Attributes where KNIME and Apache Spark MLlib differ
AttributeKNIMEApache Spark MLlib
Pricing modelfreemiumopen-source
PlatformsLinux, Mac, WindowsLinux, macOS, Windows
Founded20041999

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 KNIME

  • Visual workflows
  • Data preprocessing
  • Machine learning
  • Visualization
  • Reporting
  • Python
  • R
  • Spark

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.

KNIME

  • Data science and machine learning workflowsnot Apache Spark MLlib
  • ETL and data pipeline automationnot Apache Spark MLlib
  • Predictive analytics and modelingnot 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 KNIME
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot KNIME
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot KNIME
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot KNIME

Where each one falls short

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

KNIME

  • The free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub
  • The free AI assistant is limited to 20 interactions a month
  • Paid workflow runtime is metered in credits, with 120 included on Pro and overage at $0.025 per vCore minute
  • The Team plan at $99 a month includes 3 members, with additional seats at $49 a month each
  • Business Hub pricing is on request, and its tiers are capped at 4, 8 and 16 vCores with 5, 5 and 20 users

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

KNIME

Free
  • Analytics PlatformFree
    • 300+ data sources
    • Unlimited local processing
    • K-AI assistant (20 interactions/month)
  • Pro$19/month
    • 120 workflow runtime credits
    • Data app deployment
    • K-AI (500 interactions/month)
  • Team$99/month
    • All Pro features
    • Collaboration spaces for up to 3 team members
    • Additional members: $49/month each
  • Business Hub$null/month
    • Enterprise automation and governance
    • LDAP/OAuth authentication
    • Staged deployment

Apache Spark MLlib

Free

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

Which should you pick?

Choose KNIME if

  • You need visual workflows.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want data preprocessing.
KNIME 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 KNIME or Apache Spark MLlib better?
Neither clearly leads. KNIME 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, KNIME or Apache Spark MLlib?
KNIME starts at Free and Apache Spark MLlib at Free.
Does KNIME or Apache Spark MLlib run on more platforms?
KNIME runs on Linux, Mac, Windows. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use KNIME for free?
Both have a free tier, so you can try either at no cost before committing.
What is KNIME best used for?
KNIME is most often used for data science and machine learning workflows, etl and data pipeline automation, predictive analytics and modeling. Of those, data science and machine learning workflows and etl and data pipeline automation are not what Apache Spark MLlib is typically brought in for.
What can KNIME do that Apache Spark MLlib cannot?
KNIME covers Visual workflows, Data preprocessing, Machine learning, Visualization. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

KNIME: Is KNIME free?

Yes, KNIME Analytics Platform is free with 300+ data sources, unlimited local processing, and 20 K-AI assistant interactions per month.

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.

KNIME: What do KNIME paid plans cost?

Pro plan starts at $19/month with 120 runtime credits. Team plan starts at $99/month for up to 3 members, with additional members at $49/month each.

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.

KNIME: What is KNIME's runtime credit system?

Pro and Team plans include runtime credits for workflow execution. Additional runtime beyond included credits costs $0.025 per vCore minute.

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.

KNIME: Does KNIME offer enterprise pricing?

Yes, Business Hub is available for enterprises needing automation, governance, LDAP/OAuth auth, and dedicated resources. Pricing available on request.

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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