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Apache Hadoop vs Octopus Deploy

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
Octopus Deploy logo

Octopus Deploy

Developer Tools

Continuous delivery and runbook automation for releases

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.; Octopus Deploy free tier caps at 10 projects, 10 tenants, 10 machines, 10 users, 5 concurrent tasks and 1 space
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Hadoop and Octopus Deploy actually diverge.

Attributes where Apache Hadoop and Octopus Deploy differ
AttributeApache HadoopOctopus Deploy
Pricing modelopen-sourcesubscription
CategoryTechnologyDeveloper Tools

Identical on both: starting price (Free), free tier (Yes), platforms (Web), 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 Octopus Deploy

Nothing recorded that Apache Hadoop does not also cover.

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 Octopus Deploy
  • Running Spark or Flink under YARN on hardware you already own, using HDFS as the storage layernot Octopus Deploy
  • Keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdictionnot Octopus Deploy
  • Maintaining legacy Hive and MapReduce workloads during a staged migration to a lakehouse or cloud platformnot Octopus Deploy

Octopus Deploy

  • Automating software deployments across development, testing, productionnot Apache Hadoop
  • Automating emergency tasks like website failover and incident responsenot Apache Hadoop
  • Managing multi-tenant deployments without duplicating configurationnot Apache Hadoop
  • Tracking DORA metrics and enforcing deployment approval gatesnot 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.

Octopus Deploy

  • Free tier caps at 10 projects, 10 tenants, 10 machines, 10 users, 5 concurrent tasks and 1 space
  • Annual billing is the only option for both Octopus Cloud and Octopus Server; there is no monthly plan
  • Professional cloud tier is $4,330 per year and Enterprise cloud is $24,600 per year, with tenants and machines sold as add-ons beyond the free allotment

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Octopus Deploy

Free
  • FreeFree
    • 10 projects
    • 10 tenants
    • 10 machines
  • Professional (Cloud)$4330/year
    • Up to 100 projects
    • Unlimited spaces and users
    • Tenants and machines available as add-ons
  • Professional (Server)$2080/year
    • Up to 100 projects
    • Unlimited spaces and users
    • Tenants and machines available as add-ons
  • Enterprise (Cloud)$24600/year
    • 100+ projects supported
    • Unlimited spaces and users
    • 20 concurrent tasks

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 Octopus Deploy if

  • You want to start without paying.
Octopus Deploy review

Questions people ask

Is Apache Hadoop or Octopus Deploy better?
Neither clearly leads. Apache Hadoop starts at Free and Octopus Deploy at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Octopus Deploy?
Apache Hadoop starts at Free and Octopus Deploy at Free.
Does Apache Hadoop or Octopus Deploy run on more platforms?
Both run on Web, so platform support will not decide this one for you.
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 Octopus Deploy is typically brought in for.
What can Apache Hadoop do that Octopus Deploy cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability.

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.

Octopus Deploy: How much does Octopus Deploy cost?

Octopus Deploy offers a free tier at $0/year with limits on projects (10), users (10), and tenants (10). Professional plans start at $2,080/year for self-hosted or $4,330/year for cloud hosting.

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.

Octopus Deploy: What does the free Octopus Deploy tier include?

The free tier includes 10 projects, 10 tenants, 10 machines, 10 users, 5 concurrent tasks, 1TB file storage, 100GB database storage, and community support only.

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.

Octopus Deploy: Can I try Octopus Deploy for free before purchasing?

Yes, a free tier is available with unlimited duration but capped usage, plus a 30-day free trial with full Enterprise features is offered to evaluate the platform.

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.

Octopus Deploy: What are the Enterprise pricing differences between Cloud and Server hosting?

Enterprise Cloud hosting costs $24,600/year and includes 20 concurrent tasks and 24/7 support. Enterprise Server self-hosted costs $15,600/year with flexible concurrent task configuration and 24/7 support.

Source
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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