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Metabase vs Apache Spark MLlib

Metabase logo

Metabase

Spreadsheets

Fast analytics with the friendly UX

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: Metabase aI Service charges $3.75 per 1 million tokens (after 1M free tokens included); 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: Metabase covers No-code Query 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 Metabase and Apache Spark MLlib actually diverge.

Attributes where Metabase and Apache Spark MLlib differ
AttributeMetabaseApache Spark MLlib
Pricing modelfreemiumopen-source
PlatformsWeb, Self-hosted cloudLinux, macOS, Windows
CategorySpreadsheetsMachine Learning
Founded20141999

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 Metabase

  • No-code Query Builder
  • SQL Editor
  • Interactive Dashboards
  • Alerts
  • Embedding
  • PostgreSQL
  • MySQL
  • MongoDB

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.

Metabase

  • Business intelligence and data exploration for non-technical usersnot Apache Spark MLlib
  • Embedded analytics for SaaS applicationsnot Apache Spark MLlib
  • Self-service reporting and dashboard creationnot Apache Spark MLlib
  • Integration with 40+ data sources including cloud warehousesnot 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 Metabase
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Metabase
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Metabase
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Metabase

Where each one falls short

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

Metabase

  • AI Service charges $3.75 per 1 million tokens (after 1M free tokens included)
  • Transforms cost $0.01 per run after 1,000 included runs per month
  • Advanced Transforms cost $0.02 per run after included allocation
  • Storage overages start at $2 per 1M rows (1M rows free)
  • All yearly plans offer 10% discount versus monthly, incentivizing annual 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.
  • 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

Metabase

Free
  • Open SourceFree
    • Unlimited users
    • Unlimited questions and dashboards
    • AI-powered query generation
  • Starter$90/month
    • Email and chat support
    • $100/month standard monthly billing
    • 20+ data connectors
  • Pro$517.5/month
    • $575/month standard monthly
    • Row/column-level permissions
    • SSO
  • Enterprise$20000/year
    • Dedicated success engineer
    • 1-day support response SLA
    • Air-gapped deployment

Apache Spark MLlib

Free

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

Which should you pick?

Choose Metabase if

  • You need no-code query builder.
  • You want to start without paying.
  • You work on Web, Self-hosted cloud.
  • You also want sql editor.
Metabase 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 Metabase or Apache Spark MLlib better?
Neither clearly leads. Metabase 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, Metabase or Apache Spark MLlib?
Metabase starts at Free and Apache Spark MLlib at Free.
Does Metabase or Apache Spark MLlib run on more platforms?
Metabase runs on Web, Self-hosted cloud. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Metabase for free?
Both have a free tier, so you can try either at no cost before committing.
What is Metabase best used for?
Metabase is most often used for business intelligence and data exploration for non-technical users, embedded analytics for saas applications, self-service reporting and dashboard creation, integration with 40+ data sources including cloud warehouses. Of those, business intelligence and data exploration for non-technical users and embedded analytics for saas applications are not what Apache Spark MLlib is typically brought in for.
What can Metabase do that Apache Spark MLlib cannot?
Metabase covers No-code Query Builder, SQL Editor, Interactive Dashboards, Alerts. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Metabase: How much does Metabase cost?

Metabase Open Source is free for self-hosted deployments with unlimited users and dashboards. Cloud-hosted Starter tier costs $90/month annually ($100/month monthly) with Pro at $517.50/month annually ($575/month monthly). Enterprise plans start at $20,000 per year.

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.

Metabase: Does Metabase charge for additional users?

Yes, Metabase charges per-user fees beyond included users. Starter includes 5 users at $6 per additional user per month. Pro includes 10 users at $12 per additional user per month. Enterprise pricing is custom.

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.

Metabase: What usage-based fees does Metabase charge?

Metabase charges $3.75 per 1 million tokens for AI services (after 1M free tokens), $0.01 per transform run after 1,000 included per month, $0.02 for advanced transforms, and storage overages at $2 per 1M rows beyond the included 1M rows.

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.

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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