VM Resource Calculator

Estimates vCPU, RAM and storage for a virtual machine based on workload type and expected load — useful before deploying on vSphere, Hyper-V, EC2, Azure VMs or GCE, when you'd rather size it properly up front than watch it crawl under production load and scramble to fix it live.

The actual cost of getting sizing wrong

Oversizing wastes money directly — cloud providers bill for what's allocated, not what's actually used. Undersizing costs more indirectly but often worse: performance problems on a VM are genuinely harder to diagnose than a straightforward hardware failure, because everything looks like it's "just slow" rather than obviously broken. The sensible approach is starting conservative, watching real utilisation for a couple of weeks, and adjusting from actual data rather than guessing twice.

Rough instance sizing, as a reference point

• 2 vCPU / 1GB RAM — dev/test, very light traffic
• 2 vCPU / 4GB RAM — small web apps, light databases
• 2 vCPU / 8GB RAM — general-purpose production workloads
• 4 vCPU / 8GB RAM — compute-heavy: web servers, API layers
• 4 vCPU / 32GB RAM — memory-heavy: databases, caching layers

Rules of thumb that hold up in practice

• Add roughly 30% headroom above expected peak load, not average load
• For databases, RAM is usually the more important lever than CPU — the more of the working set that fits in memory, the fewer disk reads the workload needs
• For web and app servers, CPU and network bandwidth are the more typical bottlenecks
• On Kubernetes worker nodes, leave 10–20% of resources reserved for the kubelet, container runtime and OS itself — forgetting this is a common cause of nodes that look undersized when the real problem is nothing was left for the system processes
• Monitor with Prometheus + Grafana, CloudWatch or Azure Monitor after deployment and actually right-size based on what you see — the initial sizing is a starting point, not the final answer