Canadian model on external infrastructure
The model is Canadian, but the inference environment may remain under third-party control.
Sovereign AI in Canada
Control more than where the data is stored.
AI systems depend on more than databases. Models, inference providers, cloud infrastructure, external APIs and operational access all help determine who ultimately controls the system.
Underlabs designs AI architectures where data, models, compute and infrastructure can be aligned with an organization’s sovereignty requirements.
Sovereignty is architecture
Control is decided at every layer.
An AI system does not become sovereign simply because its database, vendor or model is Canadian. Those choices can contribute, but none describes the complete system on its own.
The deciding question: who controls the critical layers, and what happens when an external dependency becomes unavailable or changes its terms?
- 01ApplicationThe software people use, including its ownership, maintenance and portability.
- 02DataPrompts, documents, customer information, embeddings, logs and generated outputs.
- 03AI modelThe model producing results and whether it can be replaced or privately deployed.
- 04Inference and computeWhere the model actually runs and who controls that capacity.
- 05InfrastructureStorage, networking, accelerators, credentials and deployment environments.
- 06Operations and governanceWho administers, accesses or disables the system and how continuity is maintained.
Beyond residency
A Canadian server is not the whole answer.
An application and its primary database can both be hosted in Canada while information still travels to a foreign AI API, external vector database, document processor, logging system or inference environment elsewhere.
Canadian data residency is therefore one layer of sovereignty, not the entire architecture.
Learn about Canadian Data ResidencyA spectrum of control
Sovereignty is not binary.
The appropriate level of control depends on workload sensitivity, procurement requirements, continuity needs and acceptable cost.
Lower controlHigher control
- 01Canadian applicationExternal model API and inference
- 02Canadian applicationCanadian model, externally controlled inference
- 03Canadian-hosted applicationOpen-weight model, privately managed Canadian compute
- 04Canadian application and dataPrivate or Canadian model, customer-controlled inference and operations
A high-quality external AI API can be entirely appropriate for many commercial workloads. Government information, strategic intellectual property, regulated environments or critical operations may justify stronger control. Underlabs evaluates the architecture against the requirements without pushing one model or provider.
Origin and control
A Canadian model does not make a sovereign system by itself.
Model origin expands the available options. Deployment determines who controls inference, data and operations.
Foreign open-weight model, privately deployed in Canada
The origin is foreign while the organization may control compute, data, networking, access and deployment.
Canadian open-weight model on private Canadian infrastructure
This combination can bring together Canadian capability, residency, private inference and greater operational independence.
Canadian model providers such as Cohere expand the options available to organizations seeking greater domestic control. The model remains one layer among several.
Why seek stronger control
The reasons are practical and organization-specific.
Not every organization needs a fully sovereign architecture. Some workloads nevertheless justify tighter boundaries.
Sensitive information
Internal documents, contracts, research, customer information or intellectual property may require tighter control.
Procurement requirements
Contractual or institutional requirements may govern where and how processing occurs.
Operational independence
An organization may want to avoid relying entirely on one external AI provider.
Business continuity
Critical systems need explicit dependencies and realistic migration paths.
Model flexibility
Models can change as requirements, costs or technology evolve.
Governance
The organization may need clearer visibility into inference, access and administration.
Connected by choice
Sovereign does not mean isolated.
A modern sovereign architecture can still use cloud infrastructure, open-source software, commercial AI and trusted international partners.
The distinction is intentional control over critical dependencies. A hybrid architecture can reserve the strongest boundaries for the data and operations that actually need them.
- 01Public website
- 02Canadian application infrastructure
- 03Private customer data
- 04Private AI inference
- 05Approved external services where appropriate
The Underlabs approach
Set the right boundary before choosing the technology.
Underlabs connects applications, internal systems, models, data, APIs, authentication, storage and infrastructure. The value is understanding the complete architecture.
- 01
Identify what requires control
Not every workload needs the same level of sovereignty.
- 02
Map data and dependencies
Trace how the application, data, inference and external services interact.
- 03
Define the sovereignty boundary
Decide what must remain in Canada, private, local, isolated or replaceable.
- 04
Select models and infrastructure
Evaluate capability, privacy, cost, latency, deployment flexibility and control requirements.
- 05
Build for portability
Avoid unnecessary lock-in so the architecture can evolve with models and providers.
Different requirements, different architectures
Control should match the workload.
These examples illustrate architecture choices, not packages or compliance tiers. No one pattern is universally superior.
Standard enterprise AI
Where external AI services are appropriate.
- Canadian application
- Canadian database
- External AI API
Residency-focused AI
Where data remains primarily in Canada and external processing is restricted.
- Canadian application
- Canadian data
- Restricted external AI processing
Private AI
Where inference runs in a dedicated or organization-controlled environment.
- Private application
- Private data
- Private model deployment
Sovereign Canadian AI
Where stronger Canadian control requirements apply.
- Canadian application
- Canadian data
- Private or Canadian model
- Canadian compute
- Canadian-controlled operations
Canada’s sovereign AI direction
This architectural concern is now explicit.
Canada’s national AI strategy identifies compute, cloud, connectivity, data and talent as foundations of sovereign Canadian AI and calls for compute infrastructure under Canadian governance.
Shared Services Canada is also deploying a Government of Canada AI Platform that brings compute, storage, models and applications into Canadian systems under Canadian control. Its 2026–27 Departmental Plan lists digital sovereignty among its priorities.
These initiatives address government needs. They nevertheless show that control over data, compute, infrastructure and operations is a concrete architectural concern, not merely a change in vocabulary.
The next technical question
What if inference must remain inside your environment?
Models can increasingly run in private clouds, dedicated VPCs, Canadian infrastructure, enterprise data centres, on-premise systems or edge environments. Air-gapped environments are also possible in specialized cases.
Private deployment answers a more specific question: how can AI operate inside infrastructure directly controlled by the organization?
Explore Private & On-Premise AIStart with the dependencies
Decide which layers must remain under your control.
Share your data, current providers, procurement constraints and continuity requirements. Underlabs can map the system and design an architecture for the level of sovereignty you actually need.