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Apache Hadoop vs Spacelift

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
Spacelift logo

Spacelift

Developer Tools

Infrastructure-as-code orchestration for Terraform, OpenTofu, Pulumi and Kubernetes

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.; Spacelift users are unlimited and private workers are metered, so deployment concurrency rather than headcount drives the bill, which inverts how most engineering budgets are built and is easy to under-forecast.
  • They diverge on capability: Apache Hadoop covers HDFS, Spacelift covers Multi-tool orchestration.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Hadoop and Spacelift differ
AttributeApache HadoopSpacelift
Pricing modelopen-sourceBy private worker and tier, not by user
PlatformsWebWeb, Self-hosted, Cloud
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 Spacelift

  • Multi-tool orchestration
  • Policy as code
  • Private workers
  • Spaces
  • Blueprints
  • Drift detection

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

Spacelift

  • Platform teams running infrastructure changes through approval gates rather than through application CInot Apache Hadoop
  • Organisations standardising Terraform and OpenTofu across many teams with policy enforcementnot Apache Hadoop
  • Estates needing private runners so cloud credentials never leave the networknot Apache Hadoop
  • Teams wanting drift detection and reconciliation rather than plan-time checks alonenot 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.

Spacelift

  • Users are unlimited and private workers are metered, so deployment concurrency rather than headcount drives the bill, which inverts how most engineering budgets are built and is easy to under-forecast.
  • There is no affordable paid entry point. The free tier stops at two users and the next tier is a five-figure commitment with nothing in between.
  • Three of the five tiers are quote-only, so a shortlist cannot be costed without entering a sales process with each vendor on it.
  • The self-hosted build lags the hosted one, has no public worker pool and lacks the Azure and Google Cloud integrations available in the SaaS product, with OIDC federation offered as the workaround.
  • Stacks, Rego policies, Blueprints, Spaces and Spacelift-managed state are proprietary constructs, so leaving means rebuilding the orchestration layer rather than pointing it at a different runner.

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Spacelift

Free
  • FreeFree
    • Two users
    • Public workers only
    • No private workers
  • Starter Plus$undefined/year
    • Unlimited users
    • Two public workers
    • One to two private workers
  • Business$undefined/year
    • Unlimited users
    • Three private workers
    • Quoted
  • Enterprise$undefined/year
    • Unlimited users
    • Five to thirty private workers
    • Self-hosted option

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 Spacelift if

  • You need multi-tool orchestration.
  • You want to start without paying.
  • You work on Web, Self-hosted, Cloud.
  • You also want policy as code.
Spacelift review

Questions people ask

Is Apache Hadoop or Spacelift better?
Neither clearly leads. Apache Hadoop starts at Free and Spacelift at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Spacelift?
Apache Hadoop starts at Free and Spacelift at Free.
Does Apache Hadoop or Spacelift run on more platforms?
Apache Hadoop runs on Web. Spacelift runs on Web, Self-hosted, Cloud.
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 Spacelift is typically brought in for.
What can Apache Hadoop do that Spacelift cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Spacelift covers Multi-tool orchestration, Policy as code, Private workers, Spaces.

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.

Spacelift: How is Spacelift priced?

By tier and by private worker count rather than per user. Users are unlimited on every paid plan. The published figure on the pricing page carries no period label, and the FAQ describes the tier as an annual subscription, so confirm the period in writing.

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.

Spacelift: Does it support OpenTofu?

Yes, and Spacelift sponsors the OpenTofu project, which is a substantive hedge for anyone avoiding Terraform under the Business Source Licence.

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.

Spacelift: Is there a self-hosted version?

Yes, described as carrying Enterprise functionality, but it is priced on request and lags the hosted product on cloud integrations.

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.

Spacelift: Why not just use the CI system we already have?

That is the real comparison. General purpose CI handles state, drift, approval gates and concurrency control for infrastructure poorly, and whether that is worth a five-figure floor depends on how much infrastructure change your organisation actually ships.

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.

Spacelift: What happens to our pipelines if we leave?

They do not port. Rego policies, Blueprints, Spaces and managed state are Spacelift constructs and the orchestration has to be rebuilt elsewhere.

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