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Apache Spark MLlib vs Teable

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
Teable logo

Teable

Spreadsheets

Spreadsheet interface over real PostgreSQL tables, so the data stays queryable by anything that speaks SQL

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Teable the automation builder is far less capable than the established commercial alternatives, so multi step workflows usually end up in an external tool that must be paid for and maintained separately.
  • They diverge on capability: Apache Spark MLlib covers DataFrame-based pipelines, Teable covers PostgreSQL native storage.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Spark MLlib and Teable differ
AttributeApache Spark MLlibTeable
Pricing modelopen-sourceOpen source, no licence fee
PlatformsLinux, macOS, WindowsWeb, Linux
CategoryMachine LearningSpreadsheets
Founded1999Unknown

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 Apache Spark MLlib

  • DataFrame-based pipelines
  • Distributed algorithms
  • Alternating least squares
  • Feature transformers
  • Model selection
  • Pipeline persistence
  • Language bindings
  • Runs in existing Spark deployments

Only in Teable

  • PostgreSQL native storage
  • Multiple views
  • Linked records and rollups
  • Generated REST API
  • Real time collaboration
  • Self hosting via Docker
  • Field level permissions

What people use each for

The jobs each tool is most often brought in to do.

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 Teable
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Teable
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Teable
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Teable

Teable

  • A team that has hit the record ceiling of a hosted spreadsheet database and does not want to move to raw SQLnot Apache Spark MLlib
  • Operational data that a business intelligence tool must also read directly, without an export or a sync jobnot Apache Spark MLlib
  • A regulated or data resident organisation that needs the underlying database inside its own infrastructurenot Apache Spark MLlib
  • An internal tool where a grid interface and a REST API over the same table are both requirednot Apache Spark MLlib

Where each one falls short

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

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.

Teable

  • The automation builder is far less capable than the established commercial alternatives, so multi step workflows usually end up in an external tool that must be paid for and maintained separately.
  • Self hosting moves PostgreSQL backups, version upgrades, connection pooling and capacity planning onto your team, and the licence saving disappears if that time is costed honestly.
  • The integration catalogue is small, so connecting to a common business system often means writing against the REST API rather than installing a connector.
  • The project is young relative to the products it replaces, and interface and API changes still arrive at a pace that requires reading release notes before upgrading.
  • Storing every table as a real PostgreSQL table means schema changes are real migrations, so a careless field type change on a large table can lock it far longer than a spreadsheet user would expect.

Pricing, plan by plan

Apache Spark MLlib

Free

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

Teable

Free
  • Self hosted open sourceFree
    • No licence fee
    • Unlimited rows subject to your PostgreSQL capacity
    • Docker deployment

Which should you pick?

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

Choose Teable if

  • You need postgresql native storage.
  • You want to start without paying.
  • You work on Web, Linux.
  • You also want multiple views.
Teable review

Questions people ask

Is Apache Spark MLlib or Teable better?
Neither clearly leads. Apache Spark MLlib starts at Free and Teable at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark MLlib or Teable?
Apache Spark MLlib starts at Free and Teable at Free.
Does Apache Spark MLlib or Teable run on more platforms?
Apache Spark MLlib runs on Linux, macOS, Windows. Teable runs on Web, Linux.
Can I use Apache Spark MLlib for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Spark MLlib best used for?
Apache Spark MLlib is most often used for training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about, feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data, batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does not, organisations that already run and pay for spark, where adding a modelling step is cheaper than introducing a second platform. Of those, training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about and feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data are not what Teable is typically brought in for.
What can Apache Spark MLlib do that Teable cannot?
Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers. Teable covers PostgreSQL native storage, Multiple views, Linked records and rollups, Generated REST API.

Answered from the vendors’ own pages

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.

Teable: How many rows can it actually hold?

As many as your PostgreSQL instance can serve. There is no product imposed record limit on the self hosted edition, which is the main reason to choose it.

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.

Teable: Can I query the data with SQL directly?

Yes. Tables are real PostgreSQL tables, so any SQL client or reporting tool can read them.

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.

Teable: Is self hosting genuinely free?

The licence is. The database server, backups and the engineer maintaining them are not.

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.

Teable: Does it replace Airtable feature for feature?

No. Views and field types are close; automations, apps and the integration catalogue are not.

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.

Teable: What happens to my data if the project stops?

It remains in a standard PostgreSQL database that you control, which is a materially better exit than a proprietary export format.

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