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Machine type

As explained in Infrastructure as Code, Patterns and Practices: With examples in Python and Terraform

A machine type is a predefined cloud virtual-machine configuration specifying the computational resources allocated to an instance.

A machine type is a predefined virtual-machine shape that allocates a specific ratio of vCPUs and memory to an instance. GCP calls it a machine type; AWS uses instance type, and Azure uses size. section 12.1.1

Machine types turn resource sizing into a controllable infrastructure choice. A server configured with more vCPUs than its workload needs can increase the bill without improving use, so infrastructure-as-code tests can enforce a maximum. section 12.1.1

A practical control workflow is:

  • Inspect observed vCPU and memory usage to identify a suitable capacity limit. section 12.1.1
  • Query the provider catalog or API for current vCPU and memory values instead of maintaining a complete local catalog. section 12.1.1

Capacity and cost guardrails

For a production environment capped at 32 vCPUs, a policy test rejects an oversized server after retrieving the selected machine type’s current vCPU count from the GCP API. A separate estimate can price five n2d-standard-16 servers and compare the result with a monthly budget. section 12.1.1 section 12.1.2

How Infrastructure as Code, Patterns and Practices: With examples in Python and Terraform covers it

How deep it goes
Explains vCPU and memory sizing, provider lookups, IaC parsing, and policy tests, but omits complete provider catalog details.
Examples
Uses a 32-vCPU production limit, n2d-standard-16 servers, GCP catalog queries, and a $4,500 monthly compute budget.
What it assumes
Uses infrastructure-as-code configurations, cloud-provider APIs, and automated tests as the operating context.
What you can do after
Apply it via Python, provider APIs, and IaC tests to enforce capacity limits and estimate machine-related spending.
How it connects
Connects virtual machines, infrastructure as code, unit testing, policy enforcement, cloud APIs, and cost estimation.

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This explainer was generated by Manning's knowledge graph from the text of Infrastructure as Code, Patterns and Practices: With examples in Python and Terraform. The section links point to the parts of the book it draws on.