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Business Intelligence · head to head

Deepnote vs Apache Spark MLlib

Deepnote logo

Deepnote

Business Intelligence

Collaborative cloud workspace for data analytics and machine learning

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: Deepnote free plan limited to 3 editors, restricting team usage; 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: Deepnote covers Collaborative notebooks, 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 Deepnote and Apache Spark MLlib actually diverge.

Attributes where Deepnote and Apache Spark MLlib differ
AttributeDeepnoteApache Spark MLlib
Pricing modelSubscription with free tieropen-source
PlatformsWeb, APILinux, macOS, Windows
CategoryBusiness IntelligenceMachine Learning
FoundedUnknown1999

Identical on both: starting price (Free), free tier (Yes), 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 Deepnote

  • Collaborative notebooks
  • Interactive dashboards
  • Data agent building
  • Scheduled pipelines
  • Model management
  • 100+ integrations
  • GPU support
  • API deployment

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.

Deepnote

  • Data exploration and analysis workflowsnot Apache Spark MLlib
  • Building interactive business intelligence dashboardsnot Apache Spark MLlib
  • Collaborative machine learning model developmentnot Apache Spark MLlib
  • Automating ETL and data pipeline orchestrationnot Apache Spark MLlib
  • Creating shareable reports without exportsnot 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 Deepnote
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Deepnote
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Deepnote
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Deepnote

Where each one falls short

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

Deepnote

  • Free plan limited to 3 editors, restricting team usage
  • Limited revision history on free plan compared to competitors
  • Requires Team plan or higher for automated scheduling
  • GPU support incurs additional charges beyond base subscription
  • No mentioned offline capability

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

Deepnote

Free
  • FreeFree
    • Up to 3 editors
    • Up to 5 projects
    • Limited Deepnote AI
  • Team$39/month
    • Unlimited viewers and notebooks
    • Full Deepnote AI access
    • Premium integrations
  • Enterprise$null/custom
    • Everything in Team plan
    • Custom contracts
    • Priority support

Apache Spark MLlib

Free

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

Which should you pick?

Choose Deepnote if

  • You need collaborative notebooks.
  • You want to start without paying.
  • You work on Web, API.
  • You also want interactive dashboards.
Deepnote 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 Deepnote or Apache Spark MLlib better?
Neither clearly leads. Deepnote 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, Deepnote or Apache Spark MLlib?
Deepnote starts at Free and Apache Spark MLlib at Free.
Does Deepnote or Apache Spark MLlib run on more platforms?
Deepnote runs on Web, API. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Deepnote for free?
Both have a free tier, so you can try either at no cost before committing.
What is Deepnote best used for?
Deepnote is most often used for data exploration and analysis workflows, building interactive business intelligence dashboards, collaborative machine learning model development, automating etl and data pipeline orchestration. Of those, data exploration and analysis workflows and building interactive business intelligence dashboards are not what Apache Spark MLlib is typically brought in for.
What can Deepnote do that Apache Spark MLlib cannot?
Deepnote covers Collaborative notebooks, Interactive dashboards, Data agent building, Scheduled pipelines. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Deepnote: What is included in the free Deepnote plan?

The free plan includes up to 3 editors, up to 5 projects, limited Deepnote AI, basic machines with 5 GB RAM, and 7-day revision history.

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.

Deepnote: What data sources can Deepnote integrate with?

Deepnote integrates with 100+ data sources including major data warehouses like Snowflake, BigQuery, and Redshift, as well as BI platforms like Looker, Tableau, and Power BI.

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.

Deepnote: Does Deepnote support collaboration?

Yes, Deepnote provides real-time collaborative notebooks where multiple team members can work simultaneously. The Team plan allows unlimited viewers and notebooks.

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.

Deepnote: What compliance certifications does Deepnote have?

Deepnote is SOC 2, HIPAA, GDPR, and CCPA compliant and offers role-based access control, single sign-on, and directory synchronization.

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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