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Apache Spark vs Figma

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
Figma logo

Figma

Technology

The collaborative interface design tool

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.; Figma no offline editing capability, can only view and create new files when disconnected
  • They diverge on capability: Apache Spark covers Unified engine, Figma covers Real-time collaboration.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark and Figma differ
AttributeApache SparkFigma
Pricing modelopen-sourceUnknown
PlatformsWebWeb, macOS, Windows, iOS, Android
FoundedUnknown2012

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 Figma

  • Real-time collaboration
  • Vector networks
  • Auto-layout
  • Components & variants
  • Prototyping
  • Design systems
  • Developer handoff
  • Version control

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 Figma
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Figma
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot Figma
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Figma

Figma

  • UI/UX designnot Apache Spark
  • Design systemsnot Apache Spark
  • Prototypingnot Apache Spark
  • Design collaborationnot Apache Spark
  • Developer handoffnot 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.

Figma

  • No offline editing capability, can only view and create new files when disconnected
  • Mobile apps are read-only, cannot edit designs on iOS or Android
  • No self-hosted option, requires cloud connectivity and internet access
  • Performance degradation with large files containing thousands of layers
  • Library components and unloaded pages not accessible offline

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Figma

Free
  • StarterFree
    • Unlimited drafts
    • 3 Figma files
    • 3 FigJam boards
  • Professional$12/month
    • Unlimited files and folders
    • Team libraries
    • Advanced prototyping
  • Organization$55/month
    • Unlimited teams
    • Shared libraries
    • Design system theming
  • Enterprise$90/month
    • Custom workspaces
    • API access
    • SCIM seat management

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 Figma if

  • You need real-time collaboration.
  • You want to start without paying.
  • You work on Web, macOS, Windows, iOS, Android.
  • You also want vector networks.
Figma review

Questions people ask

Is Apache Spark or Figma better?
Neither clearly leads. Apache Spark starts at Free and Figma at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Figma?
Apache Spark starts at Free and Figma at Free.
Does Apache Spark or Figma run on more platforms?
Apache Spark runs on Web. Figma runs on Web, macOS, Windows, 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 Figma is typically brought in for.
What can Apache Spark do that Figma cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Figma covers Real-time collaboration, Vector networks, Auto-layout, Components & variants.

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.

Figma: What's included in Figma's free tier?

Figma's Starter plan is free forever and includes unlimited drafts, up to 3 Figma files, 3 FigJam boards, core design features (vector editing, auto layout, components, prototyping), basic developer handoff (inspect), and 150 AI credits per day up to 500 per month. Maximum 2 editors per file.

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.

Figma: Does Figma work offline?

Figma has very limited offline support. You can create one new file and edit currently loaded pages, but cannot open previously created files, access new pages, search for library components, or see real-time collaboration. Changes are cached locally for up to 30 days and sync when you reconnect.

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.

Figma: Can you self-host Figma?

No. Figma is a closed, cloud-only platform with no option to self-host or run on your own servers.

Source
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.

Figma: What export formats does Figma support?

Figma exports to PNG, JPEG, SVG (at 1x scale only), and PDF (at 1x scale only). Export settings allow customization of resolution, quality, and scale.

Source
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.

Figma: What platforms does Figma support?

Figma is available as a web app (all platforms), desktop apps for macOS and Windows, and mobile apps for iOS and Android. Linux users can access Figma through the web browser only.

Source
Figma: What are Figma's integrations?

Figma integrates with Slack, Microsoft Teams, Jira, Asana, Linear, Notion, VS Code, Zapier, and hundreds of other tools. Integration categories include collaboration, project management, developer handoff, prototyping, and AI/code generation.

Source
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