Softwr

Technology · head to head

Apache Hadoop vs Thought Machine

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
Thought Machine logo

Thought Machine

Technology

Cloud native core banking where products are written as smart contracts

From
On request
Rated
-
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.; Thought Machine a core replacement is a two to four year programme at an established bank, and the licence is a minority of total cost against system integrator fees, parallel running and data migration.
  • They diverge on capability: Apache Hadoop covers HDFS, Thought Machine covers Smart contract product engine.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which Apache Hadoop and Thought Machine actually diverge.

Attributes where Apache Hadoop and Thought Machine differ
AttributeApache HadoopThought Machine
Starting priceFreeOn request
Pricing modelopen-sourcequote
Free tierYesNo
PlatformsWebWeb, API, Cloud

Identical on both: 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 Thought Machine

  • Smart contract product engine
  • Cloud native architecture
  • Real time ledger
  • Vault Payments
  • API first design
  • Multi entity and multi currency
  • Product versioning and testing
  • Configurable posting rules

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

Thought Machine

  • A large bank launching a separate digital brand on a clean core rather than migrating the back booknot Apache Hadoop
  • A bank whose product launches are blocked by vendor change requests on a legacy corenot Apache Hadoop
  • An institution needing real time balances and postings for instant payment obligationsnot Apache Hadoop
  • A group consolidating multiple country cores onto one multi entity platformnot 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.

Thought Machine

  • A core replacement is a two to four year programme at an established bank, and the licence is a minority of total cost against system integrator fees, parallel running and data migration.
  • The smart contract model presumes a bank can write and maintain Python financial products, and institutions without that engineering capability end up outsourcing the very flexibility they bought.
  • Cloud infrastructure cost sits with the bank and is not trivial at scale, so total cost of ownership comparisons against a hosted legacy core often miss a large recurring line.
  • Thought Machine has made two rounds of job cuts pursuing profitability, which is a legitimate supplier stability concern for a system a bank expects to run for fifteen years.
  • The functional footprint is core ledger and product engine, so origination, collections, regulatory reporting and channels all come from other vendors, and the integration estate around Vault is the bank's problem to design and own.

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Thought Machine

On request
  • Vault Core$undefined/year
    • Quoted per institution, typically on account volumes or annual contract value
    • Implementation and system integrator costs commonly exceed the licence fee
    • Cloud infrastructure costs are the bank's and are not included

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 Thought Machine if

  • You need smart contract product engine.
  • You work on Web, API, Cloud.
  • You also want cloud native architecture.
Thought Machine review

Questions people ask

Is Apache Hadoop or Thought Machine better?
Neither clearly leads. Apache Hadoop starts at Free and Thought Machine at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Thought Machine?
Apache Hadoop has a free tier; the other does not. Paid plans start at Free for Apache Hadoop and On request for Thought Machine.
Does Apache Hadoop or Thought Machine run on more platforms?
Apache Hadoop runs on Web. Thought Machine runs on Web, API, Cloud.
Can I use Apache Hadoop for free?
Yes. Apache Hadoop has a free tier, so you can try it without paying. Thought Machine 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 Thought Machine is typically brought in for.
What can Apache Hadoop do that Thought Machine cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Thought Machine covers Smart contract product engine, Cloud native architecture, Real time ledger, Vault Payments.

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.

Thought Machine: Is Vault Core genuinely cloud native?

Yes. It is containerised, runs on Kubernetes on public cloud, and posts in real time rather than in overnight batch.

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.

Thought Machine: How long does a migration take?

Eighteen months at the absolute fastest for a narrow greenfield launch; two to four years for a phased migration at an established bank.

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.

Thought Machine: Does the licence fee represent the total cost?

No. Implementation, system integration, parallel running and cloud infrastructure typically cost more than the licence over the programme.

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.

Share

Related pages

More on Apache Hadoop

More on Thought Machine

Keep looking

Other head to heads