Softwr

Spreadsheets · head to head

Budibase vs Apache Spark MLlib

Budibase logo

Budibase

Spreadsheets

Build business apps in minutes

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: Budibase cloud plans are metered on automation actions, from 5,000 a month on Pro to 250,000 on Business; 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: Budibase covers Drag-and-drop builder, 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 Budibase and Apache Spark MLlib actually diverge.

Attributes where Budibase and Apache Spark MLlib differ
AttributeBudibaseApache Spark MLlib
PlatformsWeb, Self-hostedLinux, macOS, Windows
CategorySpreadsheetsMachine Learning
Founded20191999

Identical on both: starting price (Free), pricing model (open-source), 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 Budibase

  • Drag-and-drop builder
  • Database design
  • REST API
  • User management
  • Custom code
  • Workflows
  • Responsive design
  • 100+ integrations

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.

Budibase

  • Building internal tools over existing databasesnot Apache Spark MLlib
  • Self-hosting an open source low-code platformnot Apache Spark MLlib
  • Admin panels and CRUD apps for operations teamsnot Apache Spark MLlib
  • Automations triggered by data changesnot 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 Budibase
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Budibase
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Budibase
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Budibase

Where each one falls short

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

Budibase

  • Cloud plans are metered on automation actions, from 5,000 a month on Pro to 250,000 on Business
  • Creator seats are limited and cost $50 each beyond the included count
  • End users are $5 per user per month on top of the plan
  • Custom branding needs Premium, SSO enforcement and environment variables need Business, and audit logs are Enterprise only
  • Log retention is tiered from 1 day on Pro to 365 days on Enterprise
  • Monthly billing costs 20 percent more than annual

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

Budibase

Free
  • Open SourceFree
    • Self-hosted
    • Community support
  • FreeFree
    • Cloud hosted
    • Limited features
  • Premium$50/month
    • Advanced features
    • Email support

Apache Spark MLlib

Free

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

Which should you pick?

Choose Budibase if

  • You need drag-and-drop builder.
  • You want to start without paying.
  • You work on Web, Self-hosted.
  • You also want database design.
Budibase 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 Budibase or Apache Spark MLlib better?
Neither clearly leads. Budibase 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, Budibase or Apache Spark MLlib?
Budibase starts at Free and Apache Spark MLlib at Free.
Does Budibase or Apache Spark MLlib run on more platforms?
Budibase runs on Web, Self-hosted. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Budibase for free?
Both have a free tier, so you can try either at no cost before committing.
What is Budibase best used for?
Budibase is most often used for building internal tools over existing databases, self-hosting an open source low-code platform, admin panels and crud apps for operations teams, automations triggered by data changes. Of those, building internal tools over existing databases and self-hosting an open source low-code platform are not what Apache Spark MLlib is typically brought in for.
What can Budibase do that Apache Spark MLlib cannot?
Budibase covers Drag-and-drop builder, Database design, REST API, User management. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Budibase: What happens if I exceed the monthly action limit on my plan?

Actions are hard monthly limits. Once you reach the limit on your plan (5K for Pro, 20K for Premium, 250K for Business), you must upgrade to a higher tier to continue using the service.

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.

Budibase: Is there a charge for additional users beyond what is included in my plan?

Yes. Each plan includes a limited number of creators (1 for Pro, expandable for Premium, 3 for Business), but end users are charged at $5 per user per month as an add-on.

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.

Budibase: How much extra do I pay if I choose monthly billing instead of yearly?

Monthly billing costs 20% more than the yearly equivalent. For example, paying monthly for Pro at $19/month equals $228 per year, versus $190 per year on a yearly subscription.

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.

Budibase: Can I try Budibase before committing to a paid plan?

Yes. All paid cloud plans include a 14-day free trial. The open source version is also available free with unlimited actions and unlimited users, though with community support only.

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.

Budibase: Does the free open source plan include the same features as paid plans?

The open source plan includes unlimited actions, unlimited apps, automations, agents, and users, but support is community-only rather than through Budibase directly. Paid plans add features like AI credits, custom branding, backups, and email or priority support.

Source
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.

Share

Related pages

More on Budibase

More on Apache Spark MLlib

Keep looking

Other head to heads