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Apache Spark vs Supabase

Apache Spark logo

Apache Spark

Technology

A distributed engine for batch, SQL, streaming and machine learning workloads over data that does not fit on one machine.

From
Free
Rated
-
Full reviewWebsite
Supabase logo

Supabase

Technology

The open source Firebase alternative

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Spark running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.; Supabase free tier projects auto-pause after 1 week of inactivity
  • They diverge on capability: Apache Spark covers Unified engine, Supabase covers PostgreSQL database.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Spark and Supabase actually diverge.

Attributes where Apache Spark and Supabase differ
AttributeApache SparkSupabase
Pricing modelopen-sourceUnknown
PlatformsWebWeb, iOS, Android
FoundedUnknown2020

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Technology).

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

  • Unified engine
  • Catalyst optimiser
  • DataFrame and SQL APIs
  • Structured Streaming
  • Spark Connect
  • Kubernetes and YARN support
  • Table format integration
  • MLlib

Only in Supabase

  • PostgreSQL database
  • Auto-generated APIs
  • Authentication & authorization
  • Real-time subscriptions
  • Storage
  • Edge functions
  • Vector embeddings
  • Database migrations

What people use each for

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

Apache Spark

  • Nightly ETL over terabytes in object storage, where a single machine would take longer than the batch window allowsnot Supabase
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Supabase
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot Supabase
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Supabase

Supabase

  • Web application backends using Postgres plus auto-generated REST/GraphQL APIsnot Apache Spark
  • Mobile app backends via Flutter/Swift/Kotlin client librariesnot Apache Spark
  • AI applications using vector/embeddings storagenot Apache Spark
  • Authentication with 20+ social providers plus email/passwordnot Apache Spark
  • File and media storage (S3-compatible with CDN) and realtime sync over WebSocketsnot Apache Spark

Where each one falls short

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

Apache Spark

  • Running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.
  • The fastest Spark is not open source. Databricks' Photon engine and comparable vendor accelerations are proprietary, so benchmark numbers quoted for Spark frequently describe a fork you can only rent, and moving off that vendor loses the performance you sized your pipelines around.
  • It is a distributed system with distributed overheads, and modern single-node tools such as DuckDB and Polars finish faster on datasets up to hundreds of gigabytes with no cluster to start, so a Spark job below that threshold is paying coordination cost for nothing.
  • Structured Streaming is micro-batch, which puts an end-to-end latency floor in the range of hundreds of milliseconds to seconds; workloads that need genuine per-event latency go to Flink instead, and discovering this after building on Spark means a rewrite.
  • Major upgrades deliberately break jobs: Spark 4.0 turns ANSI SQL mode on by default, so silent overflow and invalid casts that previously produced nulls now raise runtime errors, and a pipeline that worked for years can start failing purely on upgrade.
  • PySpark hides a process boundary, and Python UDFs serialise every row between the JVM and a Python worker; a direct translation of pandas code into PySpark UDFs can run an order of magnitude slower than the equivalent built-in expressions.

Supabase

  • Free tier projects auto-pause after 1 week of inactivity
  • Free tier hard caps: 500 MB database, 1 GB file storage, 5 GB egress/month, 50,000 monthly active users, max 2 active free projects per organization
  • Pro plan charges usage overages beyond included quotas, e.g. $0.00325 per MAU above 100,000 and $0.125/GB disk above 8 GB
  • SOC2/ISO 27001 certification and HIPAA (add-on) compliance require the $599/month Team plan; not available on the $25/month Pro plan

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Supabase

Free
  • FreeFree
    • 50,000 MAU
    • 500MB database
    • 5GB egress
  • Pro$25/month
    • 100,000 MAU
    • 8GB disk
    • 250GB egress
  • Team$599/month
    • SOC2 and ISO 27001
    • HIPAA add-on
    • 28-day log / 14-day backup retention
  • Enterprise$undefined/month
    • Custom limits
    • Dedicated support manager
    • 24/7/365 support

Which should you pick?

Choose Apache Spark if

  • You need unified engine.
  • You want to start without paying.
  • You also want catalyst optimiser.
Apache Spark review

Choose Supabase if

  • You need postgresql database.
  • You want to start without paying.
  • You work on Web, iOS, Android.
  • You also want auto-generated apis.
Supabase review

Questions people ask

Is Apache Spark or Supabase better?
Neither clearly leads. Apache Spark starts at Free and Supabase at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Supabase?
Apache Spark starts at Free and Supabase at Free.
Does Apache Spark or Supabase run on more platforms?
Apache Spark runs on Web. Supabase runs on Web, iOS, Android.
Can I use Apache Spark for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Spark best used for?
Apache Spark is most often used for nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows, building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction, feature engineering and model training across datasets too large to fit in pandas on one node, migrating legacy mapreduce or hive workloads onto an engine that is still actively developed and widely supported by cloud vendors. Of those, nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows and building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction are not what Supabase is typically brought in for.
What can Apache Spark do that Supabase cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Supabase covers PostgreSQL database, Auto-generated APIs, Authentication & authorization, Real-time subscriptions.

Answered from the vendors’ own pages

Apache Spark: When is Spark the wrong choice?

When your data fits comfortably on one machine. DuckDB or Polars will process hundreds of gigabytes on a single large node faster than a Spark cluster, without a scheduler, a driver or a shuffle. Spark earns its overhead when the data genuinely does not fit.

Supabase: How much does Supabase cost?

Supabase offers a free tier and three paid plans. Pro starts at $25 per month, Team at $599 per month, and Enterprise requires a custom quote. All pricing is in USD charged monthly.

Source
Apache Spark: Is Spark the same on Databricks as the open source version?

No. Databricks runs its own runtime including the proprietary Photon engine and its own optimisations, so performance figures and some behaviours do not carry over to open source Spark on EMR, Dataproc or your own Kubernetes cluster.

Supabase: What are Supabase's usage overage fees?

Pro plan users pay $0.00325 per monthly active user over the 100,000 included, and $0.125 per GB for database storage exceeding 8 GB per project. Compute instances cost $10 to $3,730+ per month.

Source
Apache Spark: Can I use Spark for real-time processing?

For near-real-time, yes, with Structured Streaming's micro-batch model, which lands in the sub-second to seconds range. For true per-event latency in the low milliseconds, Flink is the usual choice.

Apache Spark: Does upgrading between major versions break things?

Yes, by design in some cases. Spark 4.0 makes ANSI SQL mode the default, which converts previously silent overflow and cast failures into runtime errors. Upgrades need a testing pass over production pipelines rather than a version bump.

Apache Spark: Do I need to know Scala?

No. Python covers the vast majority of work and PySpark is the most common interface. Scala still helps when reading the source, writing custom data sources or diagnosing errors that surface as JVM stack traces.

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