Choosing infrastructure is no longer simply a question of which public cloud has the longest product list. A business may need managed services for rapid delivery, dedicated GPUs for private AI, Canadian data-location controls, predictable performance, integration with Microsoft systems, or a combination of all four.
For most organizations, the practical decision is between:
Broadest managed services ecosystem & AWS Bedrock
Native fit for Microsoft 365, Entra ID & Microsoft Foundry
Best-in-class data engineering, GKE & Vertex AI / Gemini
Dedicated hardware, direct control & private AI/GPU capacity
Combines cloud elasticity with private data control
The short answer is that there is no universally best platform. AWS is often a strong fit for organizations already using AWS or needing its extensive managed-service ecosystem. Azure fits naturally into Microsoft-heavy environments. Google Cloud is compelling for data, Kubernetes and Gemini-based AI. Private rack hosting offers dedicated capacity and greater infrastructure control, but transfers substantially more operational responsibility to your team. Hybrid architecture is frequently the most sensible option when workloads have different security, performance and scaling requirements.
This guide compares the four choices across compute, containers, storage, databases, AI, security, reliability, cost and Canadian data-location considerations. It also provides a practical selection method that businesses can use before committing to a platform.
📑 In this comparison guide
- Executive Summary & Platform Matrix
- What Does Private Rack Hosting Actually Mean?
- Service-by-Service Comparison (13 Key Categories)
- AWS: Mature, Broad Cloud Ecosystem
- Microsoft Azure: Microsoft-Centred Enterprise Environments
- Google Cloud: Data, Kubernetes & Gemini-Based AI
- Private Rack Hosting: Dedicated Control & Stable Capacity
- Cloud vs Private Rack Cost: A 3-Year TCO Method
- Security: Control vs Responsibility
- Canadian Data Residency, PIPEDA & CCCS Considerations
- Reliability and Disaster Recovery
- AI and RAG: Where Infrastructure Decisions Matter Most
- Portability and Avoiding Unintended Lock-In
- Six Common Business Scenarios Evaluated
- A Practical Platform Selection Scorecard
- A Seven-Step Decision Method
- Final Architecture Recommendation
- Frequently Asked Questions (FAQ)
Executive Summary
The table below outlines the core strengths, key trade-offs, and target architectural fit across AWS, Azure, Google Cloud, Private Rack Hosting, and Hybrid environments:
| Platform | Strongest Fit | Main Advantage | Main Trade-Off |
|---|---|---|---|
| AWS | Organizations already operating on AWS; broad application and AI workloads | Large, mature ecosystem of infrastructure and managed services | Service selection, permissions and billing can become complex |
| Microsoft Azure | Microsoft 365, Entra ID, Windows Server, SQL Server and enterprise Microsoft environments | Strong integration with Microsoft identity, data and management tools | Naming, licensing and service architecture require careful planning |
| Google Cloud | Data platforms, Kubernetes, cloud-native applications and Gemini-based AI | Strong integration across GKE, Cloud Run, BigQuery and Vertex AI | Some organizations have a smaller internal GCP skills base than AWS or Azure |
| Private Rack Hosting | Sensitive workloads, dedicated GPU capacity, private AI, stable high utilization or offline environments | Dedicated hardware, direct infrastructure control and predictable capacity | Hardware lifecycle, security, backups, redundancy and operations become your responsibility |
| Hybrid | Mixed workloads with different security, latency, integration or elasticity needs | Places each workload in the environment that fits it best | Networking, identity, monitoring and governance become more complex |
What Does Private Rack Hosting Mean?
Private rack hosting means that computing, storage and network hardware is dedicated to one organization. The physical rack may be located:
In the organization's own office server room or private facility
In a Canadian carrier-neutral colocation data centre facility
In an enterprise-grade private facility with redundant power & cooling
In a dedicated cage or locked rack operated by a trusted hosting partner
The environment can run virtual machines, Kubernetes, Docker, databases, object storage, private LLMs and RAG systems. Common components include Linux, Proxmox or another virtualization platform, Kubernetes, PostgreSQL, MySQL, MongoDB, MinIO or Ceph, Qdrant or pgvector, vLLM, Dify, Prometheus and Grafana.
The NIST Private Cloud Distinction: Private rack hosting is not automatically a private cloud. NIST defines a private cloud as infrastructure provisioned for the exclusive use of one organization; it may be managed by the organization or a third party and may exist on or off premises. A rack becomes cloud-like when it adds self-service provisioning, resource pooling, automation, measured usage and operational processes around the hardware. See the NIST private-cloud definition.
This distinction matters. Buying servers creates dedicated infrastructure. It does not automatically create automated provisioning, high availability, disaster recovery, security monitoring or a reliable private-cloud operating model.
Service-by-Service Comparison
The following table maps the closest commonly used services across AWS, Azure, Google Cloud and Private Rack Hosting. These are functional comparisons rather than guarantees of identical behaviour.
| Category | AWS | Microsoft Azure | Google Cloud | Private Rack Hosting |
|---|---|---|---|---|
| Virtual compute | Amazon EC2 | Azure Virtual Machines | Compute Engine | Dedicated CPU/GPU servers, KVM, Proxmox or VMware |
| Serverless compute | AWS Lambda | Azure Functions | Cloud Run and Cloud Run functions | Self-managed functions platform or containers; no provider-managed scaling by default |
| Managed Kubernetes | Amazon EKS | Azure Kubernetes Service (AKS) | Google Kubernetes Engine (GKE) | Self-managed Kubernetes, RKE2, K3s or another distribution |
| Container platform | Amazon ECS and AWS Fargate | Azure Container Apps | Cloud Run | Docker, Kubernetes or Nomad |
| Object storage | Amazon S3 | Azure Blob Storage | Cloud Storage | MinIO, Ceph or another S3-compatible platform |
| Block storage | Amazon EBS | Azure Managed Disks | Persistent Disk | SAN, local NVMe or distributed block storage |
| Relational database | Amazon RDS and Aurora | Azure SQL Database and managed PostgreSQL/MySQL services | Cloud SQL and AlloyDB | PostgreSQL, MySQL or commercial databases managed by your team |
| NoSQL database | DynamoDB | Azure Cosmos DB | Firestore and Bigtable | MongoDB, Cassandra or another self-managed platform |
| Generative AI | Amazon Bedrock | Microsoft Foundry, formerly Azure AI Foundry | Vertex AI and Gemini | Self-hosted models through vLLM or another inference engine |
| Machine learning | Amazon SageMaker AI | Azure Machine Learning and Microsoft Foundry | Vertex AI | Self-managed notebooks, training, inference and MLOps stack |
| Identity | AWS IAM and IAM Identity Center | Microsoft Entra ID and Azure RBAC | Cloud IAM and Cloud Identity | Active Directory, LDAP, Keycloak or another identity system |
| Secrets | AWS Secrets Manager | Azure Key Vault | Secret Manager | HashiCorp Vault, Kubernetes Secrets with external encryption, or another secrets platform |
| CI/CD and operations | CodePipeline, CodeBuild, CloudWatch and CloudTrail | Azure Pipelines, GitHub Actions and Azure Monitor | Cloud Build, Cloud Deploy and Cloud Monitoring | GitLab, Jenkins, Argo CD, Prometheus, Grafana and other self-managed tools |
AWS: Best When You Need a Mature, Broad Cloud Ecosystem
AWS is a strong default for organizations that already operate AWS accounts, VPCs, IAM policies, S3 data, Lambda functions or container workloads. Its services cover conventional applications, event-driven systems, databases, analytics, IoT, AI and global delivery.
🏆 Where AWS is strongest
- Breadth of architecture choices: Teams can use virtual machines, serverless functions, managed containers, Kubernetes or managed application services within one ecosystem.
- Established cloud operations: IAM, CloudWatch, CloudTrail, AWS Organizations, Control Tower and infrastructure-as-code tooling support mature operating models.
- Generative AI inside AWS: Amazon Bedrock provides managed access to foundation models, while Bedrock Knowledge Bases and Agents support RAG and agentic applications. SageMaker AI supports broader model development, training and deployment workflows.
- Partner and skills ecosystem: AWS experience, implementation patterns and third-party integrations are widely available.
- Canadian regions: AWS lists Canada (Central) and Canada West (Calgary) among its active regions. Region and service availability should still be checked for every planned component.
⚠️ AWS limitations to plan for
- The number of overlapping services can make architecture selection difficult.
- IAM policies, account structure, networking and cross-service permissions require disciplined design.
- Data transfer, logging, NAT gateways, managed databases and idle resources can create costs that were not obvious in the initial compute estimate.
- Bedrock model availability and features vary by model and region.
- Native services can increase switching costs if the application depends heavily on proprietary APIs and event formats.
- The organization is already invested in AWS.
- The application needs a combination of serverless, containers, object storage and managed databases.
- The team wants managed generative AI through Bedrock.
- Multi-account governance and infrastructure automation are already part of the operating model.
Microsoft Azure: Best for Microsoft-Centred Enterprise Environments
Azure is often the most natural choice when employees, devices, identities and business systems already rely on Microsoft 365, Microsoft Entra ID, Windows Server, SQL Server, Power Platform or other Microsoft products.
🏆 Where Azure is strongest
- Identity integration: Microsoft Entra ID can provide a familiar identity foundation for employees, applications and cloud resources.
- Microsoft application ecosystem: Azure supports Windows and Linux workloads while integrating with Microsoft databases, management tools, GitHub and enterprise software.
- Hybrid operating models: Azure services and management tools are designed for organizations retaining significant on-premises Microsoft infrastructure.
- Enterprise AI: Microsoft Foundry brings together model deployment, agent orchestration and observability. Azure Machine Learning supports broader machine-learning workflows.
- Canadian regions: Microsoft documents Canada Central and Canada East as an Azure region pair. Availability zones and individual services vary by region.
⚠️ Azure limitations to plan for
- Microsoft licensing, reservations, hybrid benefits and service plans can complicate cost comparisons.
- Product naming and portals evolve, so documentation and operational standards require regular maintenance.
- A Microsoft-heavy environment may become more tightly coupled to Entra ID, Azure networking and Microsoft data services.
- Model, GPU and AI feature availability must be checked in the selected Canadian region rather than assumed from global availability.
- Microsoft identity and endpoint management are central to the organization.
- The application uses SQL Server, Windows Server or Microsoft development tooling.
- The team wants cloud and on-premises resources managed through a Microsoft-centred operating model.
- Microsoft Foundry or Azure Machine Learning aligns with the AI roadmap.
Google Cloud: Best for Data, Kubernetes and Gemini-Based AI
Google Cloud is a strong option for organizations building cloud-native applications, data platforms, analytics systems, Kubernetes environments or AI applications around Vertex AI and Gemini.
🏆 Where Google Cloud is strongest
- Kubernetes: GKE is a mature managed Kubernetes platform, while Cloud Run offers a simpler serverless container model for teams that do not need to operate clusters.
- Data and analytics: BigQuery, Cloud Storage, Pub/Sub, Dataflow and related services create a coherent data platform.
- Generative AI: Vertex AI provides enterprise access to Gemini and other models, along with model management, evaluation, RAG and agent capabilities.
- Developer experience: Cloud Run, Cloud Build and managed data services can reduce infrastructure work for container-based applications.
- Canadian presence: Google Cloud publishes Canadian locations including Montreal and Toronto. Product availability remains service- and region-specific.
Google's current documentation also shows how Cloud Run can host AI applications, agents, APIs and self-deployed inference workloads, including GPU-enabled services. See the official Cloud Run AI overview.
⚠️ Google Cloud limitations to plan for
- An organization may need to build GCP-specific skills if its team is primarily experienced with AWS, Azure or traditional infrastructure.
- Not every global product, Gemini model or GPU configuration is available in every Canadian region.
- Using BigQuery, Vertex AI, Pub/Sub and other native services can create platform dependency.
- Cost still requires active management; serverless scaling does not guarantee a lower bill for continuously busy workloads.
- Data engineering, analytics or AI is central to the workload.
- The team wants GKE or Cloud Run for containerized applications.
- Gemini and Vertex AI align with the enterprise AI plan.
- The application benefits from integration with Google Cloud's data services.
Private Rack Hosting: Best for Dedicated Control and Stable Capacity
Private rack hosting changes the operating model. Instead of renting abstract resources from a cloud provider, the organization owns or leases dedicated servers, storage and networking.
🏆 Where private rack hosting is strongest
- Dedicated performance: CPU, memory, storage and GPUs are not shared with unrelated tenants.
- Infrastructure control: The organization selects the server, GPU, firmware, hypervisor, operating system, network and storage architecture.
- Private AI: Open models, vector databases, document stores and RAG applications can run without sending inference requests to an external model API.
- Stable high utilization: Continuously busy workloads may justify dedicated hardware when the full three-to-five-year cost is favourable.
- Offline or air-gapped architecture: A properly designed environment can operate with restricted or no external connectivity.
- Specific data-location policies: A rack in a selected Canadian facility provides a clear physical hosting location, subject to backup, administration and vendor-access design.
⚠️ Private rack limitations to plan for
- Hardware procurement delays capacity expansion.
- The organization must patch firmware, hypervisors, operating systems, Kubernetes, databases and applications.
- One rack or one data centre is not disaster recovery. Equivalent resilience may require a second site, replicated storage, tested backups and documented recovery procedures.
- Spare parts, warranties, remote hands, monitoring and after-hours incident response have real costs.
- GPUs can become obsolete or undersized while cloud providers continue adding newer instance types.
- Physical isolation does not prevent compromised credentials, vulnerable software, insider threats, ransomware or inadequate backups.
- The workload contains sensitive data and the organization requires direct control over processing infrastructure.
- Private LLM inference or RAG must operate without an external AI provider.
- Demand is stable enough to keep hardware productively used.
- Low and predictable local latency is more important than global elasticity.
- The organization has the team or managed partner required to operate the full stack.
Cloud vs Private Rack Cost: Compare Total Cost, Not Server Price
There is no credible universal answer to which option is cheapest. A small application with irregular demand may cost less on serverless cloud services. A continuously utilized GPU inference cluster may have a stronger dedicated-hardware case. The result depends on usage, architecture, discount commitments, staffing and recovery requirements.
☁️ Public-Cloud Cost Categories
- Compute runtime, vCPU, memory and GPUs
- Managed Kubernetes or container charges
- Database instances, storage, I/O and backups
- Object and block storage
- API requests and serverless invocations
- Internet egress and inter-region traffic
- Load balancers, public IP addresses, NAT and network services
- Logs, metrics, traces and security monitoring
- Support plan
- Engineering and operational labour
- Savings plans, reservations or committed-use discounts
🖥️ Private-Rack Cost Categories
- Server, GPU, memory, storage and network hardware
- Hardware financing or depreciation
- Rack units, cage space or private suite
- Power and cooling
- Internet transit, cross-connects and DDoS protection
- Firewalls, switches and redundant network equipment
- Virtualization, backup, database or support licences
- Warranties, replacement parts and remote hands
- Monitoring and security tools
- Secondary-site backup and disaster recovery
- Hardware refresh and secure disposal
- Engineering and 24/7 operational coverage
📊 A Practical Three-Year TCO Method
Use the same 36-month workload assumptions for all four options:
- Establish baseline metrics: Average and peak CPU, memory, GPU, storage and network use.
- Model all environments: Production, staging, development, backup and disaster-recovery environments.
- Include organic growth: Model future expansion rather than pricing only today's workload.
- Add operational overhead: Support plans, monitoring, security tooling and engineering labour.
- Account for business continuity: Downtime tolerance and recovery capability requirements.
- Scenario testing: Run optimistic, expected and high-growth scenarios.
- Explicit exclusions: Document what is excluded from the estimate.
💡 Takeaway: The relevant comparison is not monthly cloud cost versus the purchase price of one server. It is the total cost of delivering the same capacity, security, availability, support and recovery capability over the same period.
Security: More Control Also Means More Responsibility
All three public-cloud providers use a shared-responsibility model. The provider secures the physical facilities and underlying cloud infrastructure, while the customer remains responsible for areas such as identities, data, application security and the services it configures. The exact boundary changes between IaaS, PaaS and SaaS. Review the official models from AWS, Microsoft and Google Cloud.
Private rack hosting does not remove shared responsibility if a colocation, managed-service, internet, hardware-support or software vendor is involved. It also moves more of the stack under your control:
Canadian Data Residency and Privacy Considerations
Canadian organizations frequently ask whether selecting a Canadian cloud region is enough to meet privacy or contractual requirements. It is an important control, but it is not the whole analysis.
AWS lists Canada Central and Canada West (Calgary). Azure provides Canada Central and Canada East. Google Cloud publishes Canadian regions in Montreal and Toronto. However, organizations must verify each planned service, model and feature in the selected region.
🍁 Key Residency Verification Checklist
- Where primary data, replicas and backups are stored
- Whether logs and security telemetry use a different location
- Where encryption keys and HSM services operate
- Whether a service has regional, multi-region or global control-plane behaviour
- Whether support personnel or subprocessors may access data
- Where disaster-recovery copies are placed
- Whether AI prompts, responses, embeddings and evaluation data remain in the intended location
- Contractual, industry and public-sector requirements
PIPEDA does not generally prohibit outsourcing personal-information processing to another country. The accountable organization remains responsible for protecting the information and must assess the provider's safeguards and practices. The Office of the Privacy Commissioner of Canada provides practical cloud guidance for small and medium-sized enterprises and outsourcing guidance.
The Canadian Centre for Cyber Security also recommends risk-based cloud assessment and layered controls. Its cloud security assessment guidance (ITSP.50.105) applies across cloud deployment models.
Reliability and Disaster Recovery
Cloud platforms make redundant infrastructure available, but applications are not automatically highly available merely because they run in a cloud region. Teams still need to design across availability zones, select appropriate database replication, test backups and plan for regional or service failures.
Private rack hosting requires the same discipline with fewer managed building blocks. A production design should consider:
Hybrid recovery can be practical: the primary workload remains on private infrastructure while encrypted backups, a warm standby or infrastructure templates are maintained in a Canadian cloud region. The reverse is also possible, with cloud production and a private copy of critical data. Both approaches require tested procedures rather than an unverified backup checkbox.
AI and RAG: Where the Infrastructure Decision Changes Most
AI workloads make this comparison more complicated because model access, GPUs, vector search, document pipelines and security requirements may point to different environments.
AWS AI Architecture
Amazon Bedrock is a managed path for using supported foundation models and building knowledge bases or agents inside an AWS architecture. SageMaker AI is useful when the workload requires custom model development, training, deployment or broader MLOps. AWS is particularly practical when source documents already live in S3 and identity, networking, logging and operations already use AWS.
Azure AI Architecture
Microsoft Foundry is a strong option when enterprise identity, applications and data are centred on Microsoft. It can combine model deployment, agents and observability with Entra ID, Azure networking and other Microsoft services. Azure Machine Learning remains relevant for conventional machine-learning development and operations.
Google Cloud AI Architecture
Vertex AI integrates Gemini, model tooling and managed AI capabilities with Google Cloud data and application services. It is especially practical when the organization already uses BigQuery, GKE, Cloud Run, Cloud Storage or Google Workspace integrations.
🖥️ Private AI and RAG Architecture Stack
A private rack can host the complete end-to-end AI pipeline:
The Trade-off: The main benefit is control over where model inference and organizational data are processed. The main cost is that the organization must size GPUs, operate the inference engine, evaluate models, patch the platform, monitor capacity and plan replacement hardware.
🧩 A Practical Hybrid AI Pattern
Many organizations do not need to place every AI component in the same environment. A hybrid RAG design might keep sensitive documents, permissions and retrieval on private infrastructure while sending only an approved, minimized context to a managed cloud model. A stricter design can keep the complete pipeline private. Another design may use cloud AI for normal business data and private inference for a restricted document class.
The correct boundary should be based on data classification and threat modelling—not a general belief that all cloud AI is unsafe or that self-hosting is automatically secure.
Learn more about Nala Networks' enterprise RAG and private LLM solutions, AWS Bedrock consulting and Google Vertex AI consulting.
Portability and Vendor Lock-In
Containers, Kubernetes, Terraform and open databases improve portability, but they do not make platforms interchangeable. Applications can still depend on:
- Provider identity and permission models
- Managed databases and proprietary APIs
- Event systems and queue semantics
- Monitoring and logging formats
- AI model endpoints and agent frameworks
- Network topology and security services
- Data-egress cost and migration time
Avoiding every managed service can make an application more portable but also transfers more work to the engineering team. The goal should be deliberate dependency, not dependency avoidance at any cost.
A useful strategy is to keep the business domain and core application logic separate from provider integrations. Define infrastructure through code, maintain export and restore procedures, use open data formats where practical, and test recovery into a different environment before a real incident requires it.
Six Common Business Scenarios
Small business launching a new web application
Start with managed cloud services rather than buying hardware. Cloud Run, Azure Container Apps, AWS Fargate or a small virtual-machine architecture can reduce initial operations. Focus on backups, identity, cost alerts and deployment automation.
Microsoft-centred organization modernizing internal systems
Azure deserves the first evaluation because Entra ID, SQL Server, Microsoft 365 and Windows integration may reduce operational friction. Compare it with AWS or GCP only after documenting the value of those existing integrations.
A data and analytics platform
Google Cloud should be shortlisted when BigQuery, data pipelines and Vertex AI align with the project. AWS and Azure also have extensive data services, so evaluate the team's existing skills, data gravity and required integrations.
Private RAG assistant for sensitive engineering documents
Compare a fully private rack with a Canadian-region cloud architecture and a hybrid design. Evaluate document sensitivity, access controls, model quality, GPU utilization, air-gap requirements, source citations and operational support. Do not select the model before defining the data boundary.
Seasonal ecommerce or campaign workload
Public cloud is usually more practical because capacity can expand and contract. Dedicated hardware sized for the peak may remain underused for much of the year. Cost testing is still required because traffic and data transfer can be significant.
Stable, continuously utilized GPU workload
Private rack economics may become competitive when GPUs remain highly utilized and the organization can operate them reliably. Compare hardware refresh, power, colocation, redundancy and staffing with cloud reserved or committed GPU capacity—not only on-demand hourly prices.
A Practical Platform Selection Scorecard
| Decision Factor | Diagnostic Questions to Ask |
|---|---|
| Existing environment | Which cloud, identity system, databases and deployment tools does the team already operate? |
| Data sensitivity | What data classes exist, and where may each class be stored, processed, logged and backed up? |
| Elasticity | Is demand steady, seasonal, unpredictable or rapidly growing? |
| AI requirements | Do you need managed models, specific open models, private inference, training, RAG or agents? |
| Latency | Must processing occur close to a facility, machine, user group or private data source? |
| Availability | What RPO and RTO does the business require? Is a second region or facility necessary? |
| Operations | Who patches, monitors, responds after hours and performs recovery tests? |
| Cost | What is the three-year TCO under expected and high-growth scenarios? |
| Compliance | Which contractual, privacy, security or public-sector requirements apply? |
| Portability | Which proprietary services are acceptable, and what is the tested exit plan? |
A Seven-Step Decision Method
Document applications, users, integrations, data sources, traffic, storage, database activity, batch jobs, AI inference and operational dependencies.
Identify public, internal, confidential, regulated and highly restricted data. For AI agents, also classify what actions the system may perform and which require human approval.
Specify Canadian location requirements, air-gap needs, identity integration, latency, RPO, RTO, model requirements and budget boundaries.
Do not compare unrelated designs. Each option should meet the same functional, security, availability and recovery requirements.
Include infrastructure, licences, support, data transfer, monitoring, staff, backup, disaster recovery and hardware refresh.
Run a focused proof of concept. Test real data, real integrations, expected concurrency, failure behaviour, security boundaries and operating cost.
Name the team responsible for patching, monitoring, incident response, backups, recovery testing, cost control and architecture review. An excellent platform without operational ownership will still produce an unreliable system.
Final Recommendation
Choose AWS when its managed-service ecosystem, existing account structure and Bedrock integration fit the workload. Choose Azure when Microsoft identity, applications and enterprise management are central. Choose Google Cloud when data, Kubernetes, Cloud Run and Vertex AI create the strongest architectural fit. Choose private rack hosting when dedicated capacity, private AI, direct infrastructure control or restricted connectivity justifies the additional operational responsibility.
Do not force every workload into one environment. A hybrid design is often the most defensible result: public cloud for elasticity and managed services, private infrastructure for stable or sensitive workloads, and clear controls governing how data and identity cross the boundary. NIST defines hybrid cloud as distinct cloud environments bound together by technology that enables data and application portability; it is an architecture to operate deliberately, not simply two disconnected environments. See the NIST hybrid-cloud definition.
How Nala Networks Can Help
Nala Networks helps Toronto and Canadian organizations evaluate cloud and private infrastructure around the actual business workload—not around a preferred vendor. We can assess application architecture, private AI and RAG requirements, Canadian data-location controls, security responsibilities, operational readiness and three-year cost.
Depending on the result, the implementation may use private infrastructure, AWS, Google Cloud or a controlled hybrid architecture. Azure can be included in the assessment when Microsoft integration is central to the decision.
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Frequently Asked Questions
AWS Documentation • Amazon Bedrock • AWS Global Infrastructure • AWS Shared Responsibility • Azure Documentation • Microsoft Foundry • Azure Regions • Azure Security • GCP Products • GCP Locations • GCP Shared Fate • NIST SP 800-145 • OPC Cloud Guidance for SMEs • CCCS ITSP.50.105
