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Spreadsheets · head to head

Baserow vs Apache Spark MLlib

Baserow logo

Baserow

Spreadsheets

Open-source no-code database 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: Baserow the free tier is capped at 3,000 rows and 2GB of storage per workspace; 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: Baserow covers Database tables, 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 Baserow and Apache Spark MLlib actually diverge.

Attributes where Baserow and Apache Spark MLlib differ
AttributeBaserowApache Spark MLlib
Pricing modelfreemiumopen-source
PlatformsWeb, Api, Self-hostedLinux, macOS, Windows
CategorySpreadsheetsMachine Learning
Founded20191999

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 Baserow

  • Database tables
  • Multiple views
  • Forms
  • API access
  • Real-time collaboration
  • Templates
  • Plugins
  • Self-hosting

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.

Baserow

  • Self-hosting an open source alternative to a spreadsheet databasenot Apache Spark MLlib
  • Structured team data with Kanban, calendar and grid viewsnot Apache Spark MLlib
  • Building internal tools on top of a database with an APInot Apache Spark MLlib
  • Sharing data with external app users without giving them full seatsnot 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 Baserow
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Baserow
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Baserow
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Baserow

Where each one falls short

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

Baserow

  • The free tier is capped at 3,000 rows and 2GB of storage per workspace
  • Kanban, calendar and survey views need Premium at $10 per user per month billed yearly
  • Role-based permissions, audit logs and SSO require Premium or higher
  • Row limits are per workspace rather than per table, so splitting data across bases does not raise the ceiling
  • Automation runs are metered as credits, 2,000 a month on free

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

Baserow

Free
  • FreeFree
    • 3000 rows per workspace
    • 2 GB storage per workspace
    • 2000 automation credits monthly
  • Premium$10/month
    • 50000 rows per workspace
    • 20 GB storage per workspace
    • 100000 automation credits monthly
  • Advanced$18/month
    • 250000 rows per workspace
    • 100 GB storage per workspace
    • 500000 automation credits monthly
  • Enterprise$null/month
    • 1000000 rows per workspace
    • 1000 GB storage per workspace
    • 2000000 automation credits monthly

Apache Spark MLlib

Free

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

Which should you pick?

Choose Baserow if

  • You need database tables.
  • You want to start without paying.
  • You work on Web, Api, Self-hosted.
  • You also want multiple views.
Baserow 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 Baserow or Apache Spark MLlib better?
Neither clearly leads. Baserow 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, Baserow or Apache Spark MLlib?
Baserow starts at Free and Apache Spark MLlib at Free.
Does Baserow or Apache Spark MLlib run on more platforms?
Baserow runs on Web, Api, Self-hosted. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Baserow for free?
Both have a free tier, so you can try either at no cost before committing.
What is Baserow best used for?
Baserow is most often used for self-hosting an open source alternative to a spreadsheet database, structured team data with kanban, calendar and grid views, building internal tools on top of a database with an api, sharing data with external app users without giving them full seats. Of those, self-hosting an open source alternative to a spreadsheet database and structured team data with kanban, calendar and grid views are not what Apache Spark MLlib is typically brought in for.
What can Baserow do that Apache Spark MLlib cannot?
Baserow covers Database tables, Multiple views, Forms, API access. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Baserow: Does Baserow have a free plan?

Baserow's Free plan includes 3000 rows per workspace, 2 GB storage, 2000 automation credits monthly, and access to Grid, Form, and Gallery views. It supports up to 500 external app users and includes 65+ templates with no credit card required.

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.

Baserow: What row and storage limits does Baserow provide by plan?

Free plan: 3000 rows and 2 GB storage; Premium plan: 50000 rows and 20 GB; Advanced plan: 250000 rows and 100 GB; Enterprise plan: 1000000 rows and 1 TB. All limits are per workspace.

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.

Baserow: How much does Baserow Premium cost?

Baserow Premium costs $10 per user per month when billed annually, or $12 per month when billing monthly. Yearly billing provides better rates than monthly. Advanced plan costs $18/user/month annually or $22/month monthly.

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.

Baserow: What are automation credit limits in Baserow?

Free plan provides 2000 monthly automation credits, Premium provides 100000 monthly credits, Advanced provides 500000 monthly credits, and Enterprise provides 2000000 monthly credits for workflow automation.

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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