Cloud Deployment Model
Describes patterns for delivering IT resources in public, private, hybrid or community clouds and across SaaS/PaaS/IaaS service variants.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Lack of operational capabilities leads to high operating costs
- Wrong data locality can create legal and compliance risks
- Unclear responsibilities between provider and customer
- Involve compliance and security owners early
- Automate provisioning, security and monitoring
- Ensure cost transparency via tagging and budget reporting
I/O & resources
- Application requirements and SLOs
- Data classification and legal requirements
- Budget constraints and existing infrastructure
- Recommended deployment model
- Implications for operations and security
- Migration and integration plan
Description
The cloud deployment model defines patterns for delivering IT resources across public, private, hybrid or community clouds and includes SaaS/PaaS/IaaS variants. It supports architectural decisions, governance, operations and compliance mapping. Decision criteria include cost, security, control, scalability and operational capabilities.
✔Benefits
- Enables targeted trade-offs between flexibility and control
- Supports compliance via deliberate data locality and isolation
- Improves cost transparency through mapping to service models
✖Limitations
- No silver bullet: each model carries specific restrictions
- Hybrid solutions often increase complexity and integration effort
- Vendor-specific services can limit portability
Trade-offs
Metrics
- Total cost of ownership (TCO)
Measure of all direct and indirect costs over the lifecycle
- Availability / uptime
Percentage of time the service is available
- Mean time to recovery (MTTR)
Average time to recover after an outage
Examples & implementations
Global SaaS startup
Product initially ran entirely in the public cloud; later introduced regional private tenants for legal compliance.
Financial institution with private cloud
Critical core banking systems in a private cloud, less critical services operated as SaaS.
University hybrid architecture
Research and sensitive data on-premise, teaching platforms in the public cloud for scalability.
Implementation steps
Conduct requirements gathering and data classification
Evaluate deployment options and document trade-offs
Run a pilot in the selected model
Implement operational concepts, SLAs and disaster plans
⚠️ Technical debt & bottlenecks
Technical debt
- Monolithic applications without cloud-native design
- Manual provisioning and missing IaC artifacts
- Insufficient observability and alerting configuration
Known bottlenecks
Misuse examples
- Storing sensitive personal data in public cloud without encryption
- Building a private cloud but not defining operational processes
- Hybrid integration without clear network separation and authentication
Typical traps
- Underestimating integration effort between clouds
- Ignoring hidden recurring costs
- Missing SLA alignment between provider and customer
Required skills
Architectural drivers
Constraints
- • Regulatory requirements for data locality
- • Existing legacy systems with limited portability
- • Budget and personnel constraints for self-hosting