Cloud AI Infrastructure & Migration

Why Cloud Infrastructure Decisions Matter for AI

AI workloads are not like traditional application workloads. Training runs and inference at scale consume compute resources that on-premises infrastructure cannot cost-effectively provide. Equally, moving sensitive data to cloud environments without proper architecture creates compliance exposure that is unacceptable in regulated sectors.

The gap between "lift and shift" cloud migrations and properly architected AI infrastructure is significant — in performance, in cost, and in the compliance posture of the result.

What We Design and Build

Cloud Architecture for AI Workloads. We design cloud environments specifically for the compute, storage, and networking requirements of AI systems — not generic enterprise workloads. This includes GPU resource provisioning for training, optimised inference serving configurations, and data pipeline architecture that feeds models efficiently.

Canadian Data Residency Controls. For every client, we document which data assets are subject to residency requirements and architect the cloud environment accordingly. This includes selecting appropriate cloud regions, restricting cross-border data replication, and configuring data loss prevention controls that enforce residency at the infrastructure level.

Security Architecture. We implement security controls aligned with the Canadian Centre for Cyber Security's cloud security guidance. For government clients, this means designing to the CCCS Medium Cloud Profile as a baseline, with additional controls where workloads require it. For healthcare clients, we align with provincial privacy commissioner guidance and applicable health information legislation.

Cost Architecture. AI compute costs can escalate rapidly without proper governance. We implement tagging, budget alerting, auto-scaling policies, and reserved capacity planning to control costs and ensure cloud spend is visible and manageable.

The Government of Canada Cloud Context

Federal departments and Crown corporations operate within the Government of Canada Cloud Adoption Strategy and the Directive on Service and Digital. Cloud migrations must consider Protected classification levels, the GC Cloud Framework assessment process for cloud service providers, and alignment with the enterprise architecture requirements of Shared Services Canada.

We have designed cloud AI infrastructure for this environment. We understand the distinction between SaaS, PaaS, and IaaS procurement under GC frameworks, the implications of each for data sovereignty, and the documentation requirements for departmental security assessments.

Healthcare and Financial Sector Considerations

Provincial health authorities and regulated health information custodians operate under PHIPA, PIPA, and equivalent provincial legislation that imposes strict controls on where and how personal health information may be processed. We architect cloud environments that satisfy these requirements and produce the documentation that privacy officers need for their assessments.

For financial institutions, OSFI's guidance on cloud adoption (B-10) requires that federally regulated financial institutions maintain oversight and control over outsourced functions. We architect cloud environments with the audit logging, contractual controls, and operational oversight mechanisms that satisfy B-10 requirements.

Audit and Strategy Before Implementation

Cloud migrations that skip the architecture design stage produce environments that must be rebuilt. Our approach begins with a cloud readiness audit that assesses current workloads, data classifications, compliance requirements, and existing infrastructure dependencies. The strategy phase produces an architecture specification, migration sequencing plan, and cost model before any implementation work begins.

This front-loaded approach prevents the expensive course corrections that result from moving quickly into cloud environments without proper design.