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Spreadsheets · head to head

APITable vs Apache Spark MLlib

APITable logo

APITable

Spreadsheets

Open source spreadsheet database with an API first design and an embeddable widget system

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: APITable records are held in the product own schema rather than as native database tables, so the practical row ceiling is a product limit and large tables should be load tested before commitment.; 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: APITable covers API first design, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which APITable and Apache Spark MLlib actually diverge.

Attributes where APITable and Apache Spark MLlib differ
AttributeAPITableApache Spark MLlib
Pricing modelOpen source, no licence feeopen-source
PlatformsWeb, LinuxLinux, macOS, Windows
CategorySpreadsheetsMachine 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 APITable

  • API first design
  • Widget SDK
  • Embeddable views
  • Multiple view types
  • Linked records and formulas
  • Self hosting
  • Real time collaboration

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.

APITable

  • Embedding an editable table view inside a product you are building, rather than sending users to another toolnot Apache Spark MLlib
  • An internal platform where tables are read and written mostly by code through the API and only occasionally by peoplenot Apache Spark MLlib
  • A self hosted replacement for a per seat spreadsheet database where seat count is the cost drivernot Apache Spark MLlib
  • Building a custom dashboard widget that sits beside the data instead of in a separate reporting toolnot 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 APITable
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot APITable
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot APITable
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot APITable

Where each one falls short

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

APITable

  • Records are held in the product own schema rather than as native database tables, so the practical row ceiling is a product limit and large tables should be load tested before commitment.
  • The hosted free tier caps records per space, so evaluating on the cloud version gives a misleading picture of what the software can do when self hosted.
  • Automation is thin compared with the commercial alternatives, and most real workflows end up in an external automation tool.
  • The self hosted deployment is heavier than a single container, so running it properly means managing several services rather than one.
  • Documentation and community activity are uneven in English, and finding an answer to an operational problem can take longer than the problem itself deserves.

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

APITable

Free
  • Self hosted open sourceFree
    • No licence fee
    • No product record cap on self hosted deployments
    • Docker and Kubernetes deployment

Apache Spark MLlib

Free

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

Which should you pick?

Choose APITable if

  • You need api first design.
  • You want to start without paying.
  • You work on Web, Linux.
  • You also want widget sdk.
APITable 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 APITable or Apache Spark MLlib better?
Neither clearly leads. APITable 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, APITable or Apache Spark MLlib?
APITable starts at Free and Apache Spark MLlib at Free.
Does APITable or Apache Spark MLlib run on more platforms?
APITable runs on Web, Linux. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use APITable for free?
Both have a free tier, so you can try either at no cost before committing.
What is APITable best used for?
APITable is most often used for embedding an editable table view inside a product you are building, rather than sending users to another tool, an internal platform where tables are read and written mostly by code through the api and only occasionally by people, a self hosted replacement for a per seat spreadsheet database where seat count is the cost driver, building a custom dashboard widget that sits beside the data instead of in a separate reporting tool. Of those, embedding an editable table view inside a product you are building, rather than sending users to another tool and an internal platform where tables are read and written mostly by code through the api and only occasionally by people are not what Apache Spark MLlib is typically brought in for.
What can APITable do that Apache Spark MLlib cannot?
APITable covers API first design, Widget SDK, Embeddable views, Multiple view types. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

APITable: What is the record limit?

The self hosted edition has no product cap, but performance is bounded by the internal schema rather than by your database. Test at your expected size.

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.

APITable: Can I embed it in my own product?

Yes, and that is the main reason to choose it. Views embed and the widget SDK extends them.

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.

APITable: Is the hosted version representative?

No. Its free tier caps records per space, which is a commercial limit rather than a technical one.

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.

APITable: How hard is self hosting?

Harder than a single container. Plan for several services and someone to keep them running.

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.

APITable: Does it do automations?

Lightly. Expect to pair it with an external automation tool for anything multi step.

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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