HiAtoms Utility Lab

Cloud Architecture Cost & Capacity Planner

Design your infrastructure, estimate capacity requirements, and compare provider costs transparently before you build.

Capacity PlanningCloud Cost ModelingArchitecture Comparison

Architecture Configuration

Define traffic, compute, storage and availability assumptions.

How the Cloud Cost Model Works

The HiAtoms Cloud Architecture Cost & Capacity Planner uses a deterministic modeling engine to simulate workload demands against current public pricing data for major cloud providers.

Traffic Simulation

We calculate baseline continuous load and peak load using your MAU and request assumptions.

Monthly Requests = MAU × Req/User

Capacity Provisioning

Compute instances are right-sized based on average request duration and peak RPS, ensuring enough concurrency to avoid queuing.

Architecture Planning Guide

Understanding Requests vs MAU

Many teams attempt to size infrastructure based solely on Monthly Active Users. However, 10,000 MAU on a B2B SaaS dashboard generates a vastly different load profile than 10,000 MAU on a real-time multiplayer game. The planner translates users into actual network requests, which is the true metric that dictates CPU concurrency and network I/O requirements.

How Egress Changes Cloud Costs

For media-rich applications, bandwidth (Internet Egress) is often the most expensive line item on a cloud bill. AWS and Google Cloud charge a premium for data leaving their networks. By offloading static assets to a dedicated CDN, you can bypass these premium egress rates, often reducing total bandwidth costs by over 70%.

Serverless vs Containers vs VPS

Serverless (AWS Lambda, Cloud Run): Zero scale-to-zero cost, infinite scaling. However, sustained high-traffic workloads become prohibitively expensive due to per-request pricing.

Containers (ECS, GKE): Excellent for microservices and consistent workloads. You pay for the underlying compute capacity regardless of utilization.

VPS (DigitalOcean, Hetzner): The most cost-effective raw compute. Lacks the deeply integrated managed services of hyperscalers, requiring more internal operational overhead.

Frequently Asked Questions

How does MAU translate into infrastructure demand?

Monthly Active Users (MAU) is a business metric, not an engineering metric. To model infrastructure, you must translate MAU into Monthly Requests (MAU × requests per user) and Peak RPS. A highly interactive SPA might generate 1,000 requests per MAU, while a content site might generate 10. Understanding this multiplier is the first step in capacity planning.

How should I estimate peak RPS?

Peak Requests Per Second (RPS) rarely follows an even distribution. For most consumer applications, peak RPS is 2x to 5x the average RPS. For event-driven applications (e.g., ticket sales, live streaming), peak can be 20x to 50x the average. If you lack historical data, assuming a 3x multiplier from average is a safe starting baseline.

Why is egress often underestimated?

Cloud providers charge premium rates for data leaving their network (Internet Egress). While compute and storage costs are predictable, egress scales linearly with user engagement. Media-heavy applications, unoptimized API payloads, and missing CDN caching can cause egress costs to eclipse compute costs entirely.

When does serverless become expensive?

Serverless architectures (like AWS Lambda) are highly cost-effective for spiky, unpredictable, or low-volume workloads. However, for sustained, high-volume baseline traffic (e.g., 50+ constant RPS), the per-request and compute-duration pricing of serverless often exceeds the cost of provisioned containers or VPS instances by a significant margin.

How should database costs be modeled?

Database costs consist of three components: Compute (instance size), Storage (data volume), and I/O (read/write operations). Managed databases (RDS, Cloud SQL) include a significant premium for automated backups, high availability (Multi-AZ), and patching. Never model database costs based purely on storage volume.

What does high availability add to cost?

True High Availability (HA) requires redundancy across distinct physical locations (Availability Zones). This immediately doubles compute and database instance costs, and often introduces cross-AZ data transfer fees. For non-critical workloads, single-AZ deployments with automated backups offer a massive cost reduction.

How should backups be estimated?

Database and volume snapshots are typically billed per GB/month. A common mistake is estimating backup costs based on the active dataset size. A 7-day rolling backup strategy with daily snapshots and transaction logs can easily consume 3x to 5x the storage volume of the primary database.

When should I use CDN caching?

Always. A Content Delivery Network (CDN) offloads static asset delivery (images, CSS, JS) from your primary infrastructure. CDN bandwidth is significantly cheaper than cloud provider internet egress (often 80% cheaper). Additionally, edge caching improves global latency and protects origin servers from traffic spikes.

Cloud Architecture Cost & Capacity Planner

Architecture Configuration

Traffic Model

Compute & Architecture

Data & Bandwidth

How the Cloud Cost Model Works

Traffic Simulation

Capacity Provisioning

Architecture Planning Guide

Understanding Requests vs MAU

How Egress Changes Cloud Costs

Serverless vs Containers vs VPS

Frequently Asked Questions

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