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Apache Spark vs Depot

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
Depot logo

Depot

Developer Tools

Cloud-based builds and CI runs 55x faster with less cost

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.; Depot developer plan minute limits may be insufficient for active development
  • They diverge on capability: Apache Spark covers Unified engine, Depot covers Depot CI.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark and Depot differ
AttributeApache SparkDepot
Pricing modelopen-sourceUnknown
PlatformsWebCloud, GitHub, CLI
CategoryTechnologyDeveloper Tools
FoundedUnknown2022

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 Depot

  • Depot CI
  • Container builds
  • GitHub Actions runners
  • Distributed caching
  • Build API
  • Depot Registry
  • Cross-platform builds
  • SSH debugging

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

Depot

  • Accelerating Docker builds for container-based developmentnot Apache Spark
  • Replacing GitHub Actions runners with faster infrastructurenot Apache Spark
  • Distributing cache across Monorepo buildsnot Apache Spark
  • Debugging failed CI builds interactively with SSHnot 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.

Depot

  • Developer plan minute limits may be insufficient for active development
  • Startup plan at $200/mo is expensive for smaller teams
  • Business plan requires custom pricing consultation
  • macOS M2 runners not available on Developer plan

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Depot

Free
  • Developer$20/month
    • 1 user
    • 500 Docker build minutes per month
    • 2,000 Depot CI minutes per month
  • Startup$200/month
    • Unlimited users
    • 5,000 Docker build minutes per month
    • 20,000 Depot CI minutes per month
  • Business$undefined/custom
    • Custom pricing
    • Unlimited users
    • Custom monthly compute

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 Depot if

  • You need depot ci.
  • You want to start without paying.
  • You work on Cloud, GitHub, CLI.
  • You also want container builds.
Depot review

Questions people ask

Is Apache Spark or Depot better?
Neither clearly leads. Apache Spark starts at Free and Depot at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Depot?
Apache Spark starts at Free and Depot at Free.
Does Apache Spark or Depot run on more platforms?
Apache Spark runs on Web. Depot runs on Cloud, GitHub, CLI.
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 Depot is typically brought in for.
What can Apache Spark do that Depot cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Depot covers Depot CI, Container builds, GitHub Actions runners, Distributed caching.

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.

Depot: How much faster are Depot runners compared to GitHub Actions?

Depot runners offer 30% faster CPUs and 10x faster networking than standard GitHub-hosted runners, enabling builds to complete significantly faster.

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.

Depot: What is included in the Developer plan?

Developer plan ($20/month) includes 500 Docker build minutes, 2,000 Depot CI minutes, 2,000 GitHub Actions minutes, 25 GB storage, unlimited concurrency, and community support.

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.

Depot: Can I use Depot with my own AWS account?

Yes, Depot Managed allows deployment within customer AWS accounts. This is available to interested customers; contact sales for details.

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.

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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