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Apache Hadoop vs Depot

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

Where they differ

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

Attributes where Apache Hadoop and Depot differ
AttributeApache HadoopDepot
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 Hadoop

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

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 Hadoop

  • Operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storagenot Depot
  • Running Spark or Flink under YARN on hardware you already own, using HDFS as the storage layernot Depot
  • Keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdictionnot Depot
  • Maintaining legacy Hive and MapReduce workloads during a staged migration to a lakehouse or cloud platformnot Depot

Depot

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

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 Hadoop

Free

No published plan breakdown. See the Apache Hadoop 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 Hadoop if

  • You need hdfs.
  • You want to start without paying.
  • You also want yarn.
Apache Hadoop 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 Hadoop or Depot better?
Neither clearly leads. Apache Hadoop 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 Hadoop or Depot?
Apache Hadoop starts at Free and Depot at Free.
Does Apache Hadoop or Depot run on more platforms?
Apache Hadoop runs on Web. Depot runs on Cloud, GitHub, CLI.
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 Depot is typically brought in for.
What can Apache Hadoop do that Depot cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Depot covers Depot CI, Container builds, GitHub Actions runners, Distributed caching.

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.

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 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.

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 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.

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 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.

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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