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Apache Spark vs Thought Machine

Apache Spark logo

Apache Spark

Technology

A distributed engine for batch, SQL, streaming and machine learning workloads over data that does not fit on one machine.

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 Spark has a free tier, so it costs nothing to try first.
  • Each has a real cost: Apache Spark running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.; 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 Spark covers Unified engine, 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 Spark and Thought Machine actually diverge.

Attributes where Apache Spark and Thought Machine differ
AttributeApache SparkThought 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 Spark

  • Unified engine
  • Catalyst optimiser
  • DataFrame and SQL APIs
  • Structured Streaming
  • Spark Connect
  • Kubernetes and YARN support
  • Table format integration
  • MLlib

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 Spark

  • Nightly ETL over terabytes in object storage, where a single machine would take longer than the batch window allowsnot Thought Machine
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Thought Machine
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot Thought Machine
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Thought Machine

Thought Machine

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

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Spark

  • Running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.
  • The fastest Spark is not open source. Databricks' Photon engine and comparable vendor accelerations are proprietary, so benchmark numbers quoted for Spark frequently describe a fork you can only rent, and moving off that vendor loses the performance you sized your pipelines around.
  • It is a distributed system with distributed overheads, and modern single-node tools such as DuckDB and Polars finish faster on datasets up to hundreds of gigabytes with no cluster to start, so a Spark job below that threshold is paying coordination cost for nothing.
  • Structured Streaming is micro-batch, which puts an end-to-end latency floor in the range of hundreds of milliseconds to seconds; workloads that need genuine per-event latency go to Flink instead, and discovering this after building on Spark means a rewrite.
  • Major upgrades deliberately break jobs: Spark 4.0 turns ANSI SQL mode on by default, so silent overflow and invalid casts that previously produced nulls now raise runtime errors, and a pipeline that worked for years can start failing purely on upgrade.
  • PySpark hides a process boundary, and Python UDFs serialise every row between the JVM and a Python worker; a direct translation of pandas code into PySpark UDFs can run an order of magnitude slower than the equivalent built-in expressions.

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 Spark

Free

No published plan breakdown. See the Apache Spark 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 Spark if

  • You need unified engine.
  • You want to start without paying.
  • You also want catalyst optimiser.
Apache Spark 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 Spark or Thought Machine better?
Neither clearly leads. Apache Spark 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 Spark or Thought Machine?
Apache Spark has a free tier; the other does not. Paid plans start at Free for Apache Spark and On request for Thought Machine.
Does Apache Spark or Thought Machine run on more platforms?
Apache Spark runs on Web. Thought Machine runs on Web, API, Cloud.
Can I use Apache Spark for free?
Yes. Apache Spark has a free tier, so you can try it without paying. Thought Machine starts at On request.
What is Apache Spark best used for?
Apache Spark is most often used for nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows, building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction, feature engineering and model training across datasets too large to fit in pandas on one node, migrating legacy mapreduce or hive workloads onto an engine that is still actively developed and widely supported by cloud vendors. Of those, nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows and building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction are not what Thought Machine is typically brought in for.
What can Apache Spark do that Thought Machine cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Thought Machine covers Smart contract product engine, Cloud native architecture, Real time ledger, Vault Payments.

Answered from the vendors’ own pages

Apache Spark: When is Spark the wrong choice?

When your data fits comfortably on one machine. DuckDB or Polars will process hundreds of gigabytes on a single large node faster than a Spark cluster, without a scheduler, a driver or a shuffle. Spark earns its overhead when the data genuinely does not fit.

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 Spark: Is Spark the same on Databricks as the open source version?

No. Databricks runs its own runtime including the proprietary Photon engine and its own optimisations, so performance figures and some behaviours do not carry over to open source Spark on EMR, Dataproc or your own Kubernetes cluster.

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 Spark: Can I use Spark for real-time processing?

For near-real-time, yes, with Structured Streaming's micro-batch model, which lands in the sub-second to seconds range. For true per-event latency in the low milliseconds, Flink is the usual choice.

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 Spark: Does upgrading between major versions break things?

Yes, by design in some cases. Spark 4.0 makes ANSI SQL mode the default, which converts previously silent overflow and cast failures into runtime errors. Upgrades need a testing pass over production pipelines rather than a version bump.

Apache Spark: Do I need to know Scala?

No. Python covers the vast majority of work and PySpark is the most common interface. Scala still helps when reading the source, writing custom data sources or diagnosing errors that surface as JVM stack traces.

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