TypeDB vs ontology platforms
Put the meaning in the data, not on top of it
Ontology platforms add a layer of meaning above your data. TypeDB makes the model the database itself: open source, yours to own, and ready for your own agents and applications.
Overview
Main differences from ontology platforms
We typically see businesses choose TypeDB when they have large, messy domains, when they are dealing with critical systems that require accuracy, or when they want their data architecture to give them an unfair AI advantage.
Meaning in the data
The model isn't a layer mapped onto tables. It's how the data is stored and checked.
Where meaning lives
A model you own
Open source, a schema you can version, and it runs anywhere you do.
Ownership
Start this week
Install it and model something real in an afternoon, with no platform programme first.
Which to choose
Where the meaning lives
A layer on top, or the data itself
This is the most important difference, and the easiest to miss.
The model is the database. Data is stored in the shape of your domain and checked against it on every write, so every application and agent reads from the same structure.
The ontology is an abstraction mapped onto data that still lives in tables and pipelines underneath. The meaning sits in the application layer, so the data itself is no better structured.
Scope
A platform, or the foundation
The platforms do far more than a database. Whether you need all of it is the question.
The foundation those things are built on. Keep your own pipelines, tools and applications, and connect agents and services to TypeDB directly.
An end-to-end suite: connectors, pipelines, the ontology, applications and governance, usually delivered with the vendor's engineers. If you want one vendor for all of it, that breadth is the point.
Ownership
Your model, on your terms
Once your domain is encoded, the model becomes one of your most valuable assets.
Open source
Read the code, run it yourself, and choose Cloud or Enterprise when you want support.
Runs anywhere
A laptop, your own cloud, or TypeDB Cloud. The model goes where you go.
A schema you can version
Your model is a text file you can review, diff and keep in source control.
Expressiveness
How much the model can say
A model is only as useful as what it can express precisely.
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relation supply,
relates part, relates supplier,
relates site, owns contract-ref;
entity bank, sub company;Relations with any number of named roles, inheritance and polymorphic queries. Agents and developers query it directly, and every query is type-checked.
Typically object types connected by links, queried and acted on through the platform's own tools and APIs.
Use Cases
What teams build on TypeDB
01 · Agent grounding
Give agents a model of your domain they can check their work against.
- Agents query the same typed model your application uses
- Wrong queries come back as errors the agent can fix
- Connect through MCP and a TypeDB skill
match
$c isa customer, has name "Acme";
$p isa policy, links (holder: $c);
fetch { "policy": $p.ref };02 · Knowledge graphs
One connected model of your organisation, built to keep growing.
- Bring data from several systems into one typed model
- Extend the schema as you learn, without rewriting queries
- Ask questions that cross teams, systems and sources
entity employee, sub person,
plays membership:member;
relation membership,
relates member, relates team;03 · Financial relationships
Deals, ownership and exposure, modelled as they really are.
- A deal with a buyer, a target and advisors is one relation
- Ownership chains and guarantees stay queryable end to end
- One definition of exposure, shared by every team
relation deal,
relates buyer, relates target,
relates advisor @card(0..);04 · Manufacturing
Parts, suppliers and assemblies, with every dependency traceable.
- Bills of materials as nested assemblies, at any depth
- Supply relations linking part, supplier, site and contract
- Ask for every component and get every part type back
entity component;
entity fastener, sub component;
relation supply,
relates part, relates supplier, relates site;05 · Threat intelligence
Model actors, campaigns and indicators in one connected graph.
- Map standards like STIX directly onto types and roles
- Ask for every threat actor and get every subtype back
- Keep analyst and agent queries on the same model
entity threat-actor, sub stix-object;
entity intrusion-set, sub threat-actor;
match $a isa threat-actor;06 · Enterprise interoperability
Share data across systems and teams without losing what it means.
- A common model that several systems can write to
- Data that doesn't fit the model is rejected at the door
- Self-host with TypeDB Enterprise
entity person, owns id, plays case:subject;
relation case, relates subject,
relates department, owns status;07 · Digital twins
Assets, sensors and sites, connected in one live model.
- Model plants, systems and components as nested hierarchies
- Link each reading to the asset, sensor and location it belongs to
- Add new equipment types without rewriting queries
entity asset;
entity pump, sub asset;
relation reading,
relates asset, relates sensor, relates site,
owns value;08 · Legal and regulated
Obligations, parties and jurisdictions, modelled precisely.
- An obligation links a party, a rule, a jurisdiction and a deadline in one fact
- Data that breaks the model is rejected before it is stored
- Every answer traces back to typed, explicit relations
relation obligation,
relates party, relates regulation,
relates jurisdiction, owns due-date;09 · Scientific knowledge
Research domains with deep hierarchies and many-sided relationships.
- Inheritance for taxonomies that keep growing
- Reactions, pathways and samples with as many participants as they need
- Used in research in geoscience, biology and robotics
relation reaction,
relates input, relates output,
relates catalyst;Which to choose
Pick the one that fits the problem
Choose TypeDB when
- you want to own your domain model and run it anywhere
- you're building your own agents and applications on top
- your domain needs relationships and hierarchies a simple object model can't express
- you want to start this week, not next quarter
Choose an ontology platform when
- you want one vendor for integration, applications and governance
- you'd rather buy a finished platform than build on a database
- you have the budget and timeline for a platform programme
Reference
The full comparison
| TypeDB | Ontology platforms | |
|---|---|---|
| Where the meaning lives | In the data, stored in the shape of the model | In a layer mapped onto data stored elsewhere |
| What it is | A graph database for your domain model | An end-to-end data and application platform |
| Licence | Open source, with commercial Cloud and Enterprise | Proprietary |
| Where it runs | Anywhere: laptop, your cloud, or TypeDB Cloud | On the vendor's platform |
| Your model | A schema file you own and version | Defined in the vendor's tooling |
| Who can query it | Any service or agent, through drivers or MCP | Mainly the platform's own tools and APIs |
| Getting started | Install it today | A contract and a deployment programme |
