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Technology · head to head

Apache Hadoop vs Confluent Cloud

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
Rated
-
Full reviewWebsite
Confluent Cloud logo

Confluent Cloud

Technology

The data streaming platform built on Apache Kafka, delivered as a fully managed cloud service

From
Free
Rated
-
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.; Confluent Cloud requires understanding of eCKU (Elastic Confluent Unit) billing
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Hadoop and Confluent Cloud actually diverge.

Attributes where Apache Hadoop and Confluent Cloud differ
AttributeApache HadoopConfluent Cloud
Pricing modelopen-sourceusage-based

Identical on both: starting price (Free), free tier (Yes), platforms (Web), user rating (Not yet rated), category (Technology).

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 Confluent Cloud

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

Confluent Cloud

  • Apache Kafka event streaming and processingnot Apache Hadoop
  • Real-time data pipeline architecturenot Apache Hadoop
  • Multi-cloud event broker deploymentnot Apache Hadoop
  • Stream processing and analyticsnot Apache Hadoop
  • Microservices data synchronizationnot 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.

Confluent Cloud

  • Requires understanding of eCKU (Elastic Confluent Unit) billing
  • Data in/egress costs add $0.014-$0.050 per GB
  • Storage costs of $0.03-$0.08 per GB-month
  • Minimum commitment recommended for cost optimization

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Confluent Cloud

Free
  • BasicFree
    • Free first eCKU
    • Then $0.14 per eCKU-hour
    • 99.5% uptime
  • Standard$385/month
    • $0.75 per eCKU-hour
    • 99.99% uptime with 2+ eCKUs
    • Infinite storage
  • Enterprise$895/month
    • $1.75-$2.25 per eCKU-hour
    • Private networking
    • GBps+ scaling
  • Freight$2300/month
    • $2.25 per eCKU-hour
    • Specialty logging tier
    • Highest throughput requirements

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 Confluent Cloud if

  • You want to start without paying.
Confluent Cloud review

Questions people ask

Is Apache Hadoop or Confluent Cloud better?
Neither clearly leads. Apache Hadoop starts at Free and Confluent Cloud at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Confluent Cloud?
Apache Hadoop starts at Free and Confluent Cloud at Free.
Does Apache Hadoop or Confluent Cloud 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 Confluent Cloud is typically brought in for.
What can Apache Hadoop do that Confluent Cloud 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.

Confluent Cloud: How much does Confluent Cloud cost?

Confluent Cloud pricing starts free on Basic tier with $0.14 per eCKU-hour, Standard at $0.75 per eCKU-hour (~$385/month base), Enterprise at $1.75-$2.25 per eCKU-hour (~$895/month base), and Freight specialty tier at $2.25 per eCKU-hour (~$2300/month).

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.

Confluent Cloud: Is there a free tier for Confluent Cloud?

Yes, the Basic tier is free with one eCKU credit included. Additional usage is billed at $0.14 per eCKU-hour, plus data transfer and storage fees apply.

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.

Confluent Cloud: What additional fees does Confluent Cloud charge?

Beyond eCKU hours, Confluent charges data in/egress at $0.014-$0.050 per GB and storage at $0.03-$0.08 per GB-month. Costs vary by tier and region.

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.

Confluent Cloud: Does Confluent offer discounts for higher usage?

Yes, volume discounts are negotiated upfront based on annual consumption estimates. Annual commitments allow flexible cross-product allocation with guaranteed cost savings.

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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