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PlanetScale vs Apache Spark MLlib

PlanetScale logo

PlanetScale

Databases

The MySQL-compatible serverless database

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

Scalable machine learning on Apache Spark

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: PlanetScale pricing varies significantly across 17+ AWS and GCP regions; Apache Spark MLlib apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
  • They diverge on capability: PlanetScale covers Database Branching, Apache Spark MLlib covers Classification.

Where they differ

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

Attributes where PlanetScale and Apache Spark MLlib differ
AttributePlanetScaleApache Spark MLlib
Pricing modelusage-basedopen-source
PlatformsCloud-hosted (AWS, GCP, Azure)Linux, macOS, Windows
CategoryDatabasesMachine Learning
Founded20181999

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 PlanetScale

  • Database Branching
  • Non-blocking Schema Changes
  • Insights
  • Horizontal Scaling
  • Connection Pooling
  • Query Caching
  • Automatic Backups
  • Global Replication

Only in Apache Spark MLlib

  • Classification
  • Regression
  • Clustering
  • Collaborative filtering
  • Feature engineering
  • Apache Spark
  • Hadoop
  • Kafka

What people use each for

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

PlanetScale

  • MySQL-compatible applications requiring horizontal scalingnot Apache Spark MLlib
  • PostgreSQL deployments with custom cluster configurationsnot Apache Spark MLlib
  • Multi-region database deployments on AWS or GCPnot Apache Spark MLlib
  • Applications requiring transparent sharding via Vitessnot Apache Spark MLlib

Apache Spark MLlib

  • Machine learningnot PlanetScale
  • Data sciencenot PlanetScale
  • Distributed computingnot PlanetScale

Where each one falls short

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

PlanetScale

  • Pricing varies significantly across 17+ AWS and GCP regions
  • Additional costs for EBS storage beyond base tier, backup storage, and egress
  • Dedicated PgBouncer and replicas incur separate charges
  • Metal tier pricing increases sharply with larger configurations

Apache Spark MLlib

  • Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.

Pricing, plan by plan

PlanetScale

Free
  • Postgres EBS Single-Node (ARM64 PS-5)$5/month
    • 512 MiB RAM
    • Single-node configuration
    • EBS storage included
  • Postgres EBS HA (ARM64 PS-5)$15/month
    • 512 MiB RAM
    • 3-node high-availability setup
    • 1 primary + 2 replicas
  • Postgres Metal (M-10)$50/month
    • 1/8 vCPU, 1 GiB RAM
    • 3-node HA configuration
    • 10 GiB NVMe storage included
  • Vitess Non-Metal 3-Node$39/month
    • Sharding-capable database
    • x86-64 architecture
    • 3-node configuration

Apache Spark MLlib

Free

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

Which should you pick?

Choose PlanetScale if

  • You need database branching.
  • You want to start without paying.
  • You work on Cloud-hosted (AWS, GCP, Azure).
  • You also want non-blocking schema changes.
PlanetScale review

Choose Apache Spark MLlib if

  • You need classification.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want regression.
Apache Spark MLlib review

Questions people ask

Is PlanetScale or Apache Spark MLlib better?
Neither clearly leads. PlanetScale 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, PlanetScale or Apache Spark MLlib?
PlanetScale starts at Free and Apache Spark MLlib at Free.
Does PlanetScale or Apache Spark MLlib run on more platforms?
PlanetScale runs on Cloud-hosted (AWS, GCP, Azure). Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use PlanetScale for free?
Both have a free tier, so you can try either at no cost before committing.
What is PlanetScale best used for?
PlanetScale is most often used for mysql-compatible applications requiring horizontal scaling, postgresql deployments with custom cluster configurations, multi-region database deployments on aws or gcp, applications requiring transparent sharding via vitess. Of those, mysql-compatible applications requiring horizontal scaling and postgresql deployments with custom cluster configurations are not what Apache Spark MLlib is typically brought in for.
What can PlanetScale do that Apache Spark MLlib cannot?
PlanetScale covers Database Branching, Non-blocking Schema Changes, Insights, Horizontal Scaling. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering.

Answered from the vendors’ own pages

PlanetScale: How much does a PlanetScale Postgres database cost per month?

PlanetScale Postgres pricing starts at $5/month for single-node ARM64 configurations with 512 MiB RAM and $15/month for the same specs in high-availability mode with 1 primary and 2 replicas. Metal tier starts at $50/month for M-10 configuration (1/8 vCPU, 1 GiB RAM). Exact pricing depends on cluster size, node architecture (ARM64 vs x86-64), storage configuration, and selected AWS/GCP region.

Source
Apache Spark MLlib: How much does Apache Spark MLlib cost?

MLlib is completely free and open source, licensed under the Apache License Version 2.0. There are no subscription, licensing, or usage fees.

Source
PlanetScale: Is there a free tier for PlanetScale?

PlanetScale offers a free tier for development and testing workloads. After free tier limits are reached, usage-based pricing applies starting at $5/month for the smallest Postgres single-node configuration, with costs scaling based on cluster size, compute, storage, and additional features like dedicated PgBouncer or replicas.

Source
Apache Spark MLlib: What licensing does MLlib use?

MLlib is licensed under Apache License Version 2.0, making it freely available for all users regardless of organization size or use case.

Source
PlanetScale: What is the difference between PlanetScale ARM64 and x86-64 pricing?

ARM64 instances cost significantly less than x86-64 equivalents. For example, a Postgres EBS HA cluster with 512 MiB RAM costs $15/month on ARM64 but $39/month on x86-64. This pricing difference extends across all cluster sizes, with larger x86-64 configurations reaching up to $5,599/month compared to ARM64 alternatives.

Source
Apache Spark MLlib: How do I use MLlib?

MLlib is built into Apache Spark. Download Spark, which includes MLlib as a module, and deploy on your choice of infrastructure including Hadoop, Mesos, Kubernetes, standalone, or cloud.

Source
PlanetScale: What is included in a PlanetScale cluster price versus additional costs?

The advertised cluster price covers the base compute and configured storage. Additional charges apply for EBS storage beyond the base allocation, backup storage, data egress, optional dedicated PgBouncer connections, and replicas beyond the base high-availability configuration. Regional pricing varies across 17+ AWS and GCP zones.

Source
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