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Thought Machine vs YugabyteDB

Thought Machine logo

Thought Machine

Technology

Cloud native core banking where products are written as smart contracts

From
On request
Rated
-
Full reviewWebsite
YugabyteDB logo

YugabyteDB

Databases

Open source distributed SQL database for cloud native apps

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only YugabyteDB has a free tier, so it costs nothing to try first.
  • Each has a real cost: 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.; YugabyteDB missing PostgreSQL functions and extensions despite claiming compatibility
  • They diverge on capability: Thought Machine covers Smart contract product engine, YugabyteDB covers PostgreSQL Compatible.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which Thought Machine and YugabyteDB actually diverge.

Attributes where Thought Machine and YugabyteDB differ
AttributeThought MachineYugabyteDB
Starting priceOn requestFree
Pricing modelquoteUnknown
Free tierNoYes
PlatformsWeb, API, CloudCloud, On-premises, Kubernetes
CategoryTechnologyDatabases
FoundedUnknown2016

Identical on both: 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 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

Only in YugabyteDB

  • PostgreSQL Compatible
  • Distributed SQL
  • Geo-distribution
  • Linear Scalability
  • High Availability
  • ACID Transactions
  • CDC Support
  • PostgreSQL

What people use each for

The jobs each tool is most often brought in to do.

Thought Machine

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

YugabyteDB

  • Transaction processingnot Thought Machine
  • Data storagenot Thought Machine
  • Application backendnot Thought Machine
  • Reportingnot Thought Machine
  • Data analyticsnot Thought Machine

Where each one falls short

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

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.

YugabyteDB

  • Missing PostgreSQL functions and extensions despite claiming compatibility
  • Not a true PostgreSQL replacement requiring schema and query compatibility testing before migration
  • Requires careful isolation level management or risk data corruption in production
  • Lacks built-in OLAP capabilities, requiring external systems for analytics
  • Coupled compute and storage scaling reduces optimization flexibility

Pricing, plan by plan

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

YugabyteDB

Free

No published plan breakdown. See the YugabyteDB review.

Which should you pick?

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

Choose YugabyteDB if

  • You need postgresql compatible.
  • You want to start without paying.
  • You work on Cloud, On-premises, Kubernetes.
  • You also want distributed sql.
YugabyteDB review

Questions people ask

Is Thought Machine or YugabyteDB better?
Neither clearly leads. Thought Machine starts at On request and YugabyteDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Thought Machine or YugabyteDB?
YugabyteDB has a free tier; the other does not. Paid plans start at On request for Thought Machine and Free for YugabyteDB.
Does Thought Machine or YugabyteDB run on more platforms?
Thought Machine runs on Web, API, Cloud. YugabyteDB runs on Cloud, On-premises, Kubernetes.
Can I use YugabyteDB for free?
Yes. YugabyteDB has a free tier, so you can try it without paying. Thought Machine starts at On request.
What is Thought Machine best used for?
Thought Machine is most often used for a large bank launching a separate digital brand on a clean core rather than migrating the back book, a bank whose product launches are blocked by vendor change requests on a legacy core, an institution needing real time balances and postings for instant payment obligations, a group consolidating multiple country cores onto one multi entity platform. Of those, a large bank launching a separate digital brand on a clean core rather than migrating the back book and a bank whose product launches are blocked by vendor change requests on a legacy core are not what YugabyteDB is typically brought in for.
What can Thought Machine do that YugabyteDB cannot?
Thought Machine covers Smart contract product engine, Cloud native architecture, Real time ledger, Vault Payments. YugabyteDB covers PostgreSQL Compatible, Distributed SQL, Geo-distribution, Linear Scalability.

Answered from the vendors’ own pages

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.

YugabyteDB: Is YugabyteDB a true drop-in replacement for PostgreSQL?

No, YugabyteDB is PostgreSQL-compatible but not a zero-change drop-in replacement. It requires compatibility testing with queries, stored procedures, and ORM configurations before migration.

Source
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.

YugabyteDB: What isolation levels does YugabyteDB support?

YugabyteDB allows per-query selection between serializable isolation for critical operations and read-committed for analytics. However, this flexibility requires careful management to avoid accidental data corruption.

Source
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.

YugabyteDB: Does YugabyteDB support both SQL and NoSQL workloads?

Yes, YugabyteDB offers YSQL for PostgreSQL-compatible SQL and YCQL for Cassandra-like NoSQL workloads, using the same DocDB storage engine to support both simultaneously.

Source
YugabyteDB: Can YugabyteDB scale compute and storage independently?

No, YugabyteDB couples compute and storage scaling, unlike TiDB which separates them. This means scaling decisions are less flexible and optimization is more complex.

Source
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