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Apache Hadoop vs OpsLevel

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
OpsLevel logo

OpsLevel

Developer Tools

Internal developer portal that scores service ownership and production readiness

From
On request
Free plan
No
Full reviewWebsite

The short version

  • Only Apache Hadoop has a free tier, so it costs nothing to try first.
  • 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.; OpsLevel opsLevel does not publish prices at all, not even a starting figure, so you cannot size a budget or compare against Backstage's zero licence cost without entering a sales cycle.
  • They diverge on capability: Apache Hadoop covers HDFS, OpsLevel covers Service catalogue.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Hadoop and OpsLevel differ
AttributeApache HadoopOpsLevel
Starting priceFreeOn request
Pricing modelopen-sourcequote
Free tierYesNo
CategoryTechnologyDeveloper Tools

Identical on both: 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 OpsLevel

  • Service catalogue
  • Scorecards and rubric
  • Checks
  • Campaigns
  • Self-service actions
  • Slack and Teams integration
  • Catalogue auto-enrichment
  • Deployment on request

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

OpsLevel

  • A platform team that needs to prove every production service has a named owner and an on-call rota before an auditnot Apache Hadoop
  • Driving a fleet-wide migration such as a runtime upgrade across 400 services with a deadline and visible progressnot Apache Hadoop
  • Giving developers a paved road to create a new service from a template without waiting on the platform teamnot Apache Hadoop
  • An organisation where an incident took an hour to route because nobody could identify the owning teamnot 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.

OpsLevel

  • OpsLevel does not publish prices at all, not even a starting figure, so you cannot size a budget or compare against Backstage's zero licence cost without entering a sales cycle.
  • The Standard plan caps at 50 users, which is below the size at which service ownership becomes a real problem, so most genuine buyers land on Enterprise and its unpublished pricing.
  • Scores are only as honest as the metadata teams maintain; where ownership records go stale the rubric produces confident numbers about a catalogue that no longer reflects reality, and leadership acts on them.
  • It reports on quality rather than producing it, so it can generate friction between platform teams who set the rubric and product teams who see it as a scoreboard imposed on them without extra headcount to fix the findings.
  • As a hosted product it needs read access to source control, cloud accounts and observability tooling, which is a meaningful vendor review in regulated environments; on-premises exists but only on the Enterprise tier.

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

OpsLevel

On request
  • Standard$undefined/year
    • Up to 50 users
    • Unlimited catalogued components
    • Scorecards, campaigns and the global rubric
  • Enterprise$undefined/year
    • Unlimited users
    • Custom integrations
    • Customisable dashboards

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 OpsLevel if

  • You need service catalogue.
  • You also want scorecards and rubric.
OpsLevel review

Questions people ask

Is Apache Hadoop or OpsLevel better?
Neither clearly leads. Apache Hadoop starts at Free and OpsLevel at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or OpsLevel?
Apache Hadoop has a free tier; the other does not. Paid plans start at Free for Apache Hadoop and On request for OpsLevel.
Does Apache Hadoop or OpsLevel 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?
Yes. Apache Hadoop has a free tier, so you can try it without paying. OpsLevel starts at On request.
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 OpsLevel is typically brought in for.
What can Apache Hadoop do that OpsLevel cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. OpsLevel covers Service catalogue, Scorecards and rubric, Checks, Campaigns.

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.

OpsLevel: How much does OpsLevel cost?

It does not publish pricing. Billing is per developer using the portal, with volume discounts, and a quote requires a sales conversation.

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.

OpsLevel: How does it compare with Backstage?

Backstage has no licence fee but you staff a team to build and run it. OpsLevel is hosted with checks, campaigns and scorecards out of the box, and you pay per developer instead.

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.

OpsLevel: Does it require writing catalogue YAML by hand?

No. It auto-discovers and enriches entries from connected systems, though teams still curate ownership and metadata.

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.

OpsLevel: Can it run on premises?

Yes, but only on the Enterprise plan.

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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