Cloud Deployment Models · Concept · 360° | Nexonoma

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Cloud Deployment Models

Overview of cloud resource deployment models (public, private, hybrid, community) and their effects on architecture, operations, and governance.

Cloud deployment models describe how cloud resources are provisioned and managed (public, private, hybrid, community).

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaTechnical
Decision typeArchitectural
Organizational maturityIntermediate

Technical context

Integrations

On-premise Active Directory and identity servicesCI/CD tools and infrastructure automationMonitoring and observability platforms

Principles & goals

Principles

Choose the model based on compliance, cost, and scalability.Separate data ownership and operational control by clear responsibilities.Consider network latency and data movement in architectures.

Value stream stage

Discovery

Organizational level

Enterprise, Domain

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Missing network security between environments can cause data leaks.
Cost overruns due to unplanned usage and data movement.
Unaddressed compliance requirements can lead to legal issues.

Best practices

Automate infrastructure provisioning and configuration management.
Define clear responsibilities for data ownership and operations.
Regularly assess costs and optimize resource consumption.

I/O & resources

Inputs

Workload profiles and performance requirements
Compliance and data protection requirements
Network, security, and integration requirements

Outputs

Recommended deployment model with architectural principles
Cost and risk assessment
Implementation and operational requirements

Resources

Description

Cloud deployment models describe how cloud resources are provisioned and managed (public, private, hybrid, community). They influence architecture, governance, security, and operations. The concept guides decisions about hosting strategies, balancing cost, compliance, and scalability. It provides practical guidance for selecting models based on workload and operational requirements.

Benefits

Enables targeted control of cost and performance.
Supports compliance via appropriate data residency and isolation.
Allows flexible scaling and faster delivery of services.

Limitations

Private clouds can incur higher operational costs and complexity.
Hybrid models require additional integration effort.
Public clouds introduce provider dependencies.

Trade-offs

Metrics

Total cost of ownership (TCO)
Measurement of all direct and indirect costs across the solution lifecycle.
Availability
Percentage of uptime of services within defined SLAs.
Data transfer costs
Monetary evaluation of the volumes of data moved between environments.

Examples & implementations

Government infrastructure in a private cloud

A public authority runs sensitive systems in a private cloud to meet data protection requirements.

Retail platform on public cloud

An online retailer uses public cloud services for elastic scaling during sales events.

Bank with hybrid architecture

Core banking systems remain on-premise while customer-facing interfaces are hosted in the cloud.

Implementation steps

Conduct analysis of workloads and requirements.

Evaluate models (public/private/hybrid/community) against defined criteria.

Implement a proof-of-concept, run tests, and define the operating model.

Technical debt & bottlenecks

Technical debt

Unmodernized legacy systems impede cloud portability.
Ad-hoc network integration without documentation increases maintenance costs.
Insufficient automation leads to manual configuration overhead.

Known bottlenecks

Network throughput between on-premise and cloudData transfer and storage costsOperational skills for private cloud administration

Misuse examples

Migrating sensitive customer data to a public cloud without encryption and compliance checks.
Using a private cloud without sufficient operational resources, leading to outages.
Splitting critical components across providers without consistent security policies.

Typical traps

Underestimating network and integration costs between environments.
Lack of automation leads to increased manual effort and errors.
Ignoring organizational impacts such as changes in operations and support.

Required skills

Cloud architecture and designNetwork and security administrationCost management and basic Cloud FinOps

Architectural drivers

Scalability and elasticityData residency and complianceNetwork architecture and latency requirements

Constraints

• Regulatory requirements for data localization
• Existing legacy systems with limited portability
• Budget and capital constraints for infrastructure