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Apache Spark vs Restate

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
Restate logo

Restate

Developer Tools

Durable execution engine in Rust with a free tier and low-latency workflows

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.; Restate the runtime is under a Business Source Licence rather than an open source licence, so building a competing managed offering on it is prohibited and some procurement functions will reject it on licence grounds alone.
  • They diverge on capability: Apache Spark covers Unified engine, Restate covers Durable execution.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Spark and Restate differ
AttributeApache SparkRestate
Pricing modelopen-sourcePer month by durable action count
PlatformsWebLinux, macOS, Web
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 Restate

  • Durable execution
  • Virtual objects
  • Single binary runtime
  • Multi-language SDKs
  • Workflows and sagas
  • Restate Cloud

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

Restate

  • Payment or order flows where a crash between two external API calls must not double-charge or lose the ordernot Apache Spark
  • AI agent loops that call tools over minutes or hours and must survive a deployment mid-runnot Apache Spark
  • Replacing hand-rolled retry, idempotency key and outbox code across a set of microservicesnot Apache Spark
  • A small team that wants durable workflows without operating a Temporal cluster and its databasenot 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.

Restate

  • The runtime is under a Business Source Licence rather than an open source licence, so building a competing managed offering on it is prohibited and some procurement functions will reject it on licence grounds alone.
  • Restate Cloud jumps from a free fifty thousand actions straight to 300 US dollars a month for twenty million, so a workload sitting at a few hundred thousand actions pays enterprise-shaped money for hobby-shaped volume.
  • It is a young project against Temporal’s established base, so hiring engineers who already know it is unrealistic and the answer to most questions is in the docs rather than on Stack Overflow.
  • The programming model requires structuring code around durable handlers and virtual objects, which is a genuine rewrite for existing services rather than a library you add.
  • The single-binary simplicity that makes small deployments cheap also means fewer operational knobs at very large scale than a system designed as separable components.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Restate

Free
  • FreeFree
    • Fifty thousand durable actions per month
    • No credit card required
    • Community support
  • Starter$300/month
    • Twenty million actions per month
    • Two hundred actions per second, bursting to one thousand
    • Twenty GB storage
  • Pro$1000/month
    • Fifty million actions per month
    • Five hundred actions per second, bursting to two thousand
    • Fifty GB storage
  • Enterprise$undefined/year
    • Negotiated volume
    • Twenty-four seven P0 support
    • 99.99 per cent SLA

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 Restate if

  • You need durable execution.
  • You want to start without paying.
  • You work on Linux, macOS, Web.
  • You also want virtual objects.
Restate review

Questions people ask

Is Apache Spark or Restate better?
Neither clearly leads. Apache Spark starts at Free and Restate at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Restate?
Apache Spark starts at Free and Restate at Free.
Does Apache Spark or Restate run on more platforms?
Apache Spark runs on Web. Restate runs on Linux, macOS, Web.
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 Restate is typically brought in for.
What can Apache Spark do that Restate cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Restate covers Durable execution, Virtual objects, Single binary runtime, Multi-language SDKs.

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.

Restate: Is Restate open source?

The SDKs are MIT. The runtime is under a Business Source Licence, which permits self-hosting but not offering it as a competing service.

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.

Restate: How does it compare with Temporal?

Same problem, different shape: one Rust binary with embedded storage instead of a server plus database plus workers, and lower per-step latency.

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.

Restate: Is there a free tier?

Yes, fifty thousand durable actions a month on Restate Cloud with no card required.

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.

Restate: Which languages are supported?

TypeScript, Java, Kotlin, Go, Python and Rust.

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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