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Apache Spark vs Spacelift

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
Spacelift logo

Spacelift

Developer Tools

Infrastructure-as-code orchestration for Terraform, OpenTofu, Pulumi and Kubernetes

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.; Spacelift users are unlimited and private workers are metered, so deployment concurrency rather than headcount drives the bill, which inverts how most engineering budgets are built and is easy to under-forecast.
  • They diverge on capability: Apache Spark covers Unified engine, Spacelift covers Multi-tool orchestration.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Spark and Spacelift differ
AttributeApache SparkSpacelift
Pricing modelopen-sourceBy private worker and tier, not by user
PlatformsWebWeb, Self-hosted, Cloud
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 Spacelift

  • Multi-tool orchestration
  • Policy as code
  • Private workers
  • Spaces
  • Blueprints
  • Drift detection

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

Spacelift

  • Platform teams running infrastructure changes through approval gates rather than through application CInot Apache Spark
  • Organisations standardising Terraform and OpenTofu across many teams with policy enforcementnot Apache Spark
  • Estates needing private runners so cloud credentials never leave the networknot Apache Spark
  • Teams wanting drift detection and reconciliation rather than plan-time checks alonenot 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.

Spacelift

  • Users are unlimited and private workers are metered, so deployment concurrency rather than headcount drives the bill, which inverts how most engineering budgets are built and is easy to under-forecast.
  • There is no affordable paid entry point. The free tier stops at two users and the next tier is a five-figure commitment with nothing in between.
  • Three of the five tiers are quote-only, so a shortlist cannot be costed without entering a sales process with each vendor on it.
  • The self-hosted build lags the hosted one, has no public worker pool and lacks the Azure and Google Cloud integrations available in the SaaS product, with OIDC federation offered as the workaround.
  • Stacks, Rego policies, Blueprints, Spaces and Spacelift-managed state are proprietary constructs, so leaving means rebuilding the orchestration layer rather than pointing it at a different runner.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Spacelift

Free
  • FreeFree
    • Two users
    • Public workers only
    • No private workers
  • Starter Plus$undefined/year
    • Unlimited users
    • Two public workers
    • One to two private workers
  • Business$undefined/year
    • Unlimited users
    • Three private workers
    • Quoted
  • Enterprise$undefined/year
    • Unlimited users
    • Five to thirty private workers
    • Self-hosted option

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 Spacelift if

  • You need multi-tool orchestration.
  • You want to start without paying.
  • You work on Web, Self-hosted, Cloud.
  • You also want policy as code.
Spacelift review

Questions people ask

Is Apache Spark or Spacelift better?
Neither clearly leads. Apache Spark starts at Free and Spacelift at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Spacelift?
Apache Spark starts at Free and Spacelift at Free.
Does Apache Spark or Spacelift run on more platforms?
Apache Spark runs on Web. Spacelift runs on Web, Self-hosted, Cloud.
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 Spacelift is typically brought in for.
What can Apache Spark do that Spacelift cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Spacelift covers Multi-tool orchestration, Policy as code, Private workers, Spaces.

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.

Spacelift: How is Spacelift priced?

By tier and by private worker count rather than per user. Users are unlimited on every paid plan. The published figure on the pricing page carries no period label, and the FAQ describes the tier as an annual subscription, so confirm the period in writing.

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.

Spacelift: Does it support OpenTofu?

Yes, and Spacelift sponsors the OpenTofu project, which is a substantive hedge for anyone avoiding Terraform under the Business Source Licence.

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.

Spacelift: Is there a self-hosted version?

Yes, described as carrying Enterprise functionality, but it is priced on request and lags the hosted product on cloud integrations.

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.

Spacelift: Why not just use the CI system we already have?

That is the real comparison. General purpose CI handles state, drift, approval gates and concurrency control for infrastructure poorly, and whether that is worth a five-figure floor depends on how much infrastructure change your organisation actually ships.

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.

Spacelift: What happens to our pipelines if we leave?

They do not port. Rego policies, Blueprints, Spaces and managed state are Spacelift constructs and the orchestration has to be rebuilt elsewhere.

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