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Apache Hadoop vs Ona (formerly Gitpod)

Apache Hadoop logo

Apache Hadoop

Technology

The original open source framework for distributed storage and batch processing on commodity servers, now largely a legacy platform.

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 Hadoop the free vendor distributions no longer exist: Cloudera's CDH and Hortonworks' HDP have reached end of support and the successor CDP is subscription-only, so running Hadoop without paying now means assembling, testing and security-patching Apache releases yourself.; 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 Hadoop covers HDFS, 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 Hadoop and Ona (formerly Gitpod) actually diverge.

Attributes where Apache Hadoop and Ona (formerly Gitpod) differ
AttributeApache HadoopOna (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 Hadoop

  • HDFS
  • YARN
  • MapReduce
  • HDFS federation and high availability
  • Kerberos security
  • Rack awareness
  • S3A and object store connectors
  • Ecosystem compatibility

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 Hadoop

  • Operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storagenot Ona (formerly Gitpod)
  • Running Spark or Flink under YARN on hardware you already own, using HDFS as the storage layernot Ona (formerly Gitpod)
  • Keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdictionnot Ona (formerly Gitpod)
  • Maintaining legacy Hive and MapReduce workloads during a staged migration to a lakehouse or cloud platformnot 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 Hadoop
  • Running several coding agents in parallel against real databases and services instead of on a laptopnot Apache Hadoop
  • Keeping source code off contractor and offshore laptops by moving the whole environment into a controlled accountnot Apache Hadoop
  • Reproducing a hard-to-configure toolchain for a short-lived project without polluting anyone machinenot Apache Hadoop

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Hadoop

  • The free vendor distributions no longer exist: Cloudera's CDH and Hortonworks' HDP have reached end of support and the successor CDP is subscription-only, so running Hadoop without paying now means assembling, testing and security-patching Apache releases yourself.
  • HDFS couples storage to compute, so adding capacity means buying whole nodes with CPU and memory you may not need, and the entire industry moved to object storage precisely because it lets the two be bought separately.
  • The NameNode holds all filesystem metadata in memory, so a cluster with tens of millions of small files exhausts heap long before it exhausts disk, and the remedy is a file compaction job that somebody has to write, schedule and own indefinitely.
  • Operating it is a distinct specialism covering Kerberos, YARN queue tuning, JVM garbage collection and the compatibility matrix between Hive, HBase, Ranger, Oozie and the core, and an upgrade touches all of them at once rather than one at a time.
  • MapReduce is maintained for compatibility rather than actively developed, and new work goes to Spark or Flink, so a job written against MapReduce today is written against an API that will not gain anything further.
  • Hiring is against you: the talent pool has moved to cloud data platforms over the past decade, so a Hadoop estate increasingly depends on a small number of individuals, which makes it a succession risk before it is a technical one.

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 Hadoop

Free

No published plan breakdown. See the Apache Hadoop 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 Hadoop if

  • You need hdfs.
  • You want to start without paying.
  • You also want yarn.
Apache Hadoop 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 Hadoop or Ona (formerly Gitpod) better?
Neither clearly leads. Apache Hadoop 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 Hadoop or Ona (formerly Gitpod)?
Apache Hadoop starts at Free and Ona (formerly Gitpod) at Free.
Does Apache Hadoop or Ona (formerly Gitpod) run on more platforms?
Apache Hadoop runs on Web. Ona (formerly Gitpod) runs on Web, Linux, macOS, Windows, AWS, Google Cloud Platform.
Can I use Apache Hadoop for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Hadoop best used for?
Apache Hadoop is most often used for operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storage, running spark or flink under yarn on hardware you already own, using hdfs as the storage layer, keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdiction, maintaining legacy hive and mapreduce workloads during a staged migration to a lakehouse or cloud platform. Of those, operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storage and running spark or flink under yarn on hardware you already own, using hdfs as the storage layer are not what Ona (formerly Gitpod) is typically brought in for.
What can Apache Hadoop do that Ona (formerly Gitpod) cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Ona (formerly Gitpod) covers Ephemeral environments, Agent sessions, Bring your own cloud, Devcontainer standard.

Answered from the vendors’ own pages

Apache Hadoop: Is Hadoop dead?

No, but it is legacy. Large on-premises HDFS estates still run and are still supported, and Spark and Flink still run on YARN. What has ended is Hadoop as a default choice for new platforms, which now start on object storage.

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 Hadoop: Can I still get a free packaged distribution?

Not a maintained one. CDH and HDP reached end of support and Cloudera's CDP is a paid subscription. The remaining free route is building and patching Apache releases yourself, which is a real engineering commitment.

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 Hadoop: Do I need Hadoop to run Spark?

No. Spark runs standalone, on Kubernetes and on managed cloud services, and reads object storage directly. Many Spark deployments include Hadoop client libraries for the filesystem connectors without running a Hadoop cluster at all.

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 Hadoop: What replaced HDFS?

Object storage, typically S3 or a compatible system, combined with an open table format such as Apache Iceberg or Delta Lake. Apache Ozone exists as an object store within the Hadoop ecosystem for organisations staying on-premises.

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 Hadoop: Is it cheaper than the cloud?

It can be at multi-petabyte scale with steady, predictable utilisation, particularly where egress charges would be large. Include the staffing cost honestly, because the specialist operators a Hadoop cluster requires are scarce and therefore expensive.

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