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Apache Spark vs Garden

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
Free plan
Yes
Full reviewWebsite
Garden logo

Garden

Developer Tools

Kubernetes development and testing automation with shared caching, now owned by Incredibuild

From
Free
Free plan
Yes
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.; Garden incredibuild acquired Garden in November 2024 and the pricing page now redirects to documentation, so there is no public commercial offer and no published roadmap for a team betting a platform on it.
  • They diverge on capability: Apache Spark covers Unified engine, Garden covers Action graph.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Spark and Garden differ
AttributeApache SparkGarden
Pricing modelopen-sourceOpen source, no licence fee
PlatformsWebLinux, macOS, Windows, Kubernetes
CategoryTechnologyDeveloper Tools

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 Apache Spark

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

Only in Garden

  • Action graph
  • Shared result caching
  • Remote Kubernetes environments
  • In-cluster builds
  • Same config everywhere
  • Hot reload sync
  • Helm and Kustomize support
  • Templating

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

Garden

  • A platform team whose integration test suite takes longer than the coffee break and needs caching to cut the loopnot Apache Spark
  • An organisation with thirty or more services on Kubernetes where docker-compose no longer approximates productionnot Apache Spark
  • Giving every developer an ephemeral namespace instead of queuing for one shared staging environmentnot Apache Spark
  • Running the identical build and test definition locally and in CI so failures reproduce on a laptopnot 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.

Garden

  • Incredibuild acquired Garden in November 2024 and the pricing page now redirects to documentation, so there is no public commercial offer and no published roadmap for a team betting a platform on it.
  • It is tightly bound to Kubernetes, so teams running on ECS, Nomad, plain VMs or serverless get little from it and would need a different tool entirely.
  • The YAML action graph is a real configuration surface: a large monorepo needs someone to own and maintain Garden config as a first-class artefact, which is a standing platform-engineering cost.
  • Shared remote environments mean every developer needs cluster access and a cost allocation model, so cloud spend rises and namespace sprawl becomes a thing you have to police.
  • Caching correctness depends on declaring dependencies accurately; an under-declared action will serve a stale cached pass and hide a genuine test failure, which is a hard class of bug to notice.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Garden

Free
  • Garden open sourceFree
    • Apache 2.0 licensed CLI
    • Full action graph and caching
    • Self-managed clusters
  • Commercial tier$undefined/year
    • Hosted caching and team features
    • Support and onboarding
    • Pricing no longer published since the Incredibuild acquisition

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 Garden if

  • You need action graph.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Kubernetes.
  • You also want shared result caching.
Garden review

Questions people ask

Is Apache Spark or Garden better?
Neither clearly leads. Apache Spark starts at Free and Garden at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Garden?
Apache Spark starts at Free and Garden at Free.
Does Apache Spark or Garden run on more platforms?
Apache Spark runs on Web. Garden runs on Linux, macOS, Windows, Kubernetes.
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 Garden is typically brought in for.
What can Apache Spark do that Garden cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Garden covers Action graph, Shared result caching, Remote Kubernetes environments, In-cluster builds.

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.

Garden: Is Garden still maintained?

The open source project remains on GitHub under Incredibuild ownership, but the commercial pricing page redirects to the docs and there is no public paid offer.

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.

Garden: Do I need Kubernetes?

Effectively yes. Garden is built around Kubernetes environments and offers little to teams on other runtimes.

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.

Garden: Does it replace my CI system?

No. It runs inside GitHub Actions, GitLab CI or similar and speeds up what those pipelines execute.

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.

Garden: What is the actual saving?

It comes from skipping builds and tests whose inputs have not changed, so the benefit scales with how many services you have and how often only a few change.

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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