Tools / Cloud & Server Sizing Recommender

Cloud & Server Sizing Recommender

Estimate the vCPU, RAM, and storage your app actually needs based on user count and workload type, plus a realistic monthly hosting cost range — before you provision anything.

Your Workload

Daily active users, or concurrent sessions for internal tools
Headroom above average load for traffic spikes. 30% is a reasonable default.

Typical CRUD web app or REST API. Request-driven, modest per-request compute.

Two instances for failover. Standard for anything customer-facing in production.

Recommended Sizing

vCPU
0
RAM
0 GB
Storage
0 GB
Topology

Estimated Monthly Cost

$0$0
per month, standard cloud pricing
Annual (Low)
$0
Annual (High)
$0
Provider tierMonthly range

How This Estimate Works

Per-user footprint

Each workload type has a typical vCPU/RAM/storage cost per active user, based on common production deployments. Database-heavy and ML workloads need more resources per user than a simple web API.

Peak buffer

Average load isn't peak load. A 30% buffer is a sane default; raise it if you have spiky traffic (flash sales, viral content, batch windows).

Environment multiplier

A single instance has no failover. An HA pair roughly doubles compute for redundancy. A cluster adds more nodes for horizontal scale — storage doesn't scale 1:1 with compute since it's often shared or replicated more efficiently.

Provider tiers are ranges, not quotes

Budget VPS providers, major hyperscalers, and managed/premium tiers all price vCPU, RAM, and storage differently. Use this range to sanity-check a vendor quote or budget line, not as a final number.

Frequently Asked Questions

Is this an exact quote from a cloud provider?

No — this is a planning estimate based on typical per-user resource needs and published instance pricing bands. Actual cost depends on your specific app, region, reserved-instance discounts, and provider.

Why does workload type change the sizing so much?

A request-driven web API spends very little CPU per request. A database-heavy app holds larger working sets in memory and does more I/O. ML inference workloads are CPU or GPU intensive per request. Picking the wrong profile under- or over-sizes your infrastructure.

Should I start with Single, HA Pair, or Cluster?

Use Single for dev/staging or truly low-stakes internal tools. Use HA Pair for anything customer-facing in production — it's the minimum for avoiding a single point of failure. Use Cluster once you need horizontal scale beyond what two nodes can handle, or have strict uptime SLAs.

What's a reasonable peak traffic buffer?

30% is a sane default for steady-state apps. Raise it to 50-100%+ if you have predictable spikes (marketing campaigns, month-end batch jobs) or unpredictable viral traffic risk.

How do I convert this into an actual instance type?

Take the recommended vCPU and RAM and match it to the closest general-purpose instance size from your chosen provider (e.g. AWS m-series, GCP e2, DigitalOcean general-purpose droplets), then add the recommended storage as attached block storage.