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NocoDB vs Apache Spark MLlib

NocoDB logo

NocoDB

Spreadsheets

Open-source no-code database platform with REST and GraphQL APIs

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: NocoDB the free cloud tier is capped at 3 editor seats, 1,000 records and 1 GB of storage; 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: NocoDB covers REST API, 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 NocoDB and Apache Spark MLlib actually diverge.

Attributes where NocoDB and Apache Spark MLlib differ
AttributeNocoDBApache Spark MLlib
Pricing modelfreemiumopen-source
PlatformsCloud, Self-hosted, DockerLinux, macOS, Windows
CategorySpreadsheetsMachine Learning
Founded20201999

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 NocoDB

  • REST API
  • GraphQL API
  • No-code database
  • Multiple SQL databases
  • Webhooks
  • Automation
  • Cloud support
  • Self-hosted support

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.

NocoDB

  • Self-hosting an open source alternative to a spreadsheet databasenot Apache Spark MLlib
  • Putting a spreadsheet interface over an existing Postgres or MySQL databasenot Apache Spark MLlib
  • Building internal tools on structured data with an APInot Apache Spark MLlib
  • Team bases with per-field and per-table permissions on the paid tiersnot 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 NocoDB
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot NocoDB
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot NocoDB
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot NocoDB

Where each one falls short

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

NocoDB

  • The free cloud tier is capped at 3 editor seats, 1,000 records and 1 GB of storage
  • Records are the metering unit on cloud, so Plus covers 50K and Business 300K rather than scaling by seat alone
  • Row-level security, audit log retention and team hierarchy require the Scale tier
  • SCIM provisioning and air-gapped deployment are Enterprise only
  • The self-hosted Community edition is unlimited on records and seats but does without workflows, scripts and dashboards, which start at the paid self-hosted tiers

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

NocoDB

Free
  • Free (Cloud)Free
    • 3 editor seats
    • 10 commenter seats
    • 1 GB storage
  • Plus$12/month
    • Unlimited seats
    • 50,000 records
    • 20 GB storage
  • Business$24/month
    • Unlimited seats
    • 300,000 records
    • 100 GB storage
  • Scale$45/month
    • Minimum 3 seats required
    • 1,000,000 records
    • 150 GB storage

Apache Spark MLlib

Free

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

Which should you pick?

Choose NocoDB if

  • You need rest api.
  • You want to start without paying.
  • You work on Cloud, Self-hosted, Docker.
  • You also want graphql api.
NocoDB 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 NocoDB or Apache Spark MLlib better?
Neither clearly leads. NocoDB 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, NocoDB or Apache Spark MLlib?
NocoDB starts at Free and Apache Spark MLlib at Free.
Does NocoDB or Apache Spark MLlib run on more platforms?
NocoDB runs on Cloud, Self-hosted, Docker. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use NocoDB for free?
Both have a free tier, so you can try either at no cost before committing.
What is NocoDB best used for?
NocoDB is most often used for self-hosting an open source alternative to a spreadsheet database, putting a spreadsheet interface over an existing postgres or mysql database, building internal tools on structured data with an api, team bases with per-field and per-table permissions on the paid tiers. Of those, self-hosting an open source alternative to a spreadsheet database and putting a spreadsheet interface over an existing postgres or mysql database are not what Apache Spark MLlib is typically brought in for.
What can NocoDB do that Apache Spark MLlib cannot?
NocoDB covers REST API, GraphQL API, No-code database, Multiple SQL databases. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

NocoDB: Is NocoDB free?

Yes, NocoDB offers a free cloud plan with 3 editor seats, 1,000 records, 1 GB storage, and 1,000 API calls per month. A free self-hosted Community edition with unlimited records and seats is also available.

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.

NocoDB: How much does NocoDB Plus cost?

NocoDB Plus costs $12 per seat per month (billed annually) or $15 per seat per month (monthly). For unlimited seats, it costs $108/month annually or $180/month monthly. Plus includes 50,000 records, 20 GB storage, and 100,000 API calls per month.

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.

NocoDB: What is the difference between NocoDB Plus and Business plans?

Plus ($12/seat annual) includes 50,000 records and 100,000 API calls per month. Business ($24/seat annual) includes 300,000 records, 1,000,000 API calls per month, external database connections, SAML SSO, and table/field permissions.

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.

NocoDB: Can I self-host NocoDB for free?

Yes, NocoDB's Community edition is free for self-hosting. It includes unlimited records, storage, and seats, with all core features including webhooks and 6 view types.

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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