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Apache Spark vs Ona (formerly Gitpod)

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
Ona (formerly Gitpod) logo

Ona (formerly Gitpod)

Developer Tools

Cloud development environments repositioned as a control plane for software engineering agents

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.; Ona (formerly Gitpod) acquisition risk has already materialised: the company closed a sale to OpenAI on 10 August 2026 and now sits inside the Codex team, so nothing public guarantees pricing, continuity or neutrality between model vendors beyond honouring existing commitments.
  • They diverge on capability: Apache Spark covers Unified engine, Ona (formerly Gitpod) covers Ephemeral environments.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Apache Spark and Ona (formerly Gitpod) actually diverge.

Attributes where Apache Spark and Ona (formerly Gitpod) differ
AttributeApache SparkOna (formerly Gitpod)
Pricing modelopen-sourcePer month by compute unit
PlatformsWebWeb, Linux, macOS, Windows, AWS, Google Cloud Platform
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 Ona (formerly Gitpod)

  • Ephemeral environments
  • Agent sessions
  • Bring your own cloud
  • Devcontainer standard
  • Compute unit budgeting
  • Enterprise identity

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

Ona (formerly Gitpod)

  • Giving a large engineering team identical, disposable environments so onboarding is a link rather than a two day setupnot Apache Spark
  • Running several coding agents in parallel against real databases and services instead of on a laptopnot Apache Spark
  • Keeping source code off contractor and offshore laptops by moving the whole environment into a controlled accountnot Apache Spark
  • Reproducing a hard-to-configure toolchain for a short-lived project without polluting anyone machinenot 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.

Ona (formerly Gitpod)

  • Acquisition risk has already materialised: the company closed a sale to OpenAI on 10 August 2026 and now sits inside the Codex team, so nothing public guarantees pricing, continuity or neutrality between model vendors beyond honouring existing commitments.
  • Two disruptive changes of direction inside a year, from development environments to agent orchestration and then from independence to acquisition, mean the product a browser IDE buyer originally chose no longer exists in that form.
  • The Gitpod Classic sunset on 15 October 2025 required manual conversion of configuration, hand-moved secrets, self-rescued workspace data and a brand new account, which is the clearest available precedent for how the vendor handles deprecation.
  • Community self-hosting is finished: the AGPL-3.0 repository is deprecated by its own README, and self-hosted now means a vendor-operated install in your AWS or GCP account, with no Azure, no on-premises and no air-gapped path.
  • Pricing is difficult to model because it runs on a compute unit currency where subscription credits expire at the end of the month, the mapping from units to actual compute is not published on the pricing page, and Enterprise is quotation only.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Ona (formerly Gitpod)

Free
  • FreeFree
    • Up to 3 parallel environments
    • Machines up to 4 vCPU, 16 GB memory and 80 GB disk
    • Ona Cloud multi-tenant only
  • Core$20/month
    • From 20 US dollars a month, up to 100 team members
    • Includes 80 to 2,200 recurring compute units a month depending on tier
    • Additional units from 10 US dollars per 40 units
  • Enterprise$undefined/year
    • Single-tenant deployment in your own AWS or GCP account
    • SAML and OIDC single sign-on
    • Dedicated account management and forward deployed engineers

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 Ona (formerly Gitpod) if

  • You need ephemeral environments.
  • You want to start without paying.
  • You work on Web, Linux, macOS, Windows, AWS, Google Cloud Platform.
  • You also want agent sessions.
Ona (formerly Gitpod) review

Questions people ask

Is Apache Spark or Ona (formerly Gitpod) better?
Neither clearly leads. Apache Spark starts at Free and Ona (formerly Gitpod) at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Ona (formerly Gitpod)?
Apache Spark starts at Free and Ona (formerly Gitpod) at Free.
Does Apache Spark or Ona (formerly Gitpod) run on more platforms?
Apache Spark runs on Web. Ona (formerly Gitpod) runs on Web, Linux, macOS, Windows, AWS, Google Cloud Platform.
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 Ona (formerly Gitpod) is typically brought in for.
What can Apache Spark do that Ona (formerly Gitpod) cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Ona (formerly Gitpod) covers Ephemeral environments, Agent sessions, Bring your own cloud, Devcontainer standard.

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.

Ona (formerly Gitpod): Is Gitpod still called Gitpod?

No. It rebranded to Ona in September 2025, and gitpod.io now redirects to ona.com. The company was acquired by OpenAI in a deal that closed on 10 August 2026.

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.

Ona (formerly Gitpod): Can I still self-host it for free?

No. The AGPL-3.0 self-hosted project is deprecated by its own README. What is offered now is a vendor-operated single-tenant install in your AWS or GCP account on the Enterprise plan.

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.

Ona (formerly Gitpod): What happened to Gitpod Classic?

Pay-as-you-go Classic was switched off on 15 October 2025. Migration required converting gitpod.yml to devcontainer.json, moving secrets by hand, downloading workspace data before the cutoff and creating a new account.

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.

Ona (formerly Gitpod): What does it cost?

Core starts at 20 US dollars a month and includes a monthly allowance of Ona Compute Units, with more from 10 dollars per 40 units. Subscription credits expire monthly. Enterprise is quoted.

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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