| Scenario | Visits | Growth | Min–Max | Instance $/hr | Egress GB | Storage GB | Typical Monthly Total |
|---|---|---|---|---|---|---|---|
| Startup launch | 80,000 | 12% | 1–6 | 0.08 | 900 | 200 | Varies by region and add-ons |
| SaaS steady growth | 250,000 | 8% | 2–10 | 0.12 | 3,000 | 500 | Often predictable month to month |
| Media traffic bursts | 1,200,000 | 5% | 8–40 | 0.16 | 18,000 | 1,500 | Spikes dominate compute and egress |
This calculator estimates monthly cost by combining compute, storage, bandwidth, and operational add-ons. Core steps are shown below.
- avgInstances = min + (max − min) × utilization%
- adjInstances = avgInstances × spikeMultiplier × (1 + HA%) ÷ efficiency
- instanceHours = adjInstances × hoursPerMonth − freeTierHours
- computeCost = coveredHours × rate × (1 − discount%) + onDemandHours × rate
- subtotal = compute + storage + backups + egress + cdn + db + lb + monitoring
- total = subtotal + support + contingency + tax
- Enter today’s monthly visits, growth rate, and forecast duration.
- Set autoscaling min and max, then choose average utilization.
- Add spike multiplier and high-availability overhead for resilience.
- Fill in unit prices for compute, storage, egress, CDN, and database.
- Choose a support model, add buffer percent, then calculate.
- Download CSV or PDF to share a budget-ready report.
Workload assumptions and forecast horizon
Forecasting starts with monthly visits, average page weight, and expected growth. For example, 200,000 visits with 8% monthly growth becomes about 503,000 visits by month 12 (200,000 × 1.08^12). The calculator converts demand into compute pressure using a scaling exponent, so workloads that cache well may grow at 0.70× traffic, while CPU-bound apps may track closer to 1.00×.
Autoscaling baseline and utilization
Autoscaling cost is driven by the average instance count, not the maximum. The model uses min instances plus the utilization share of the (max − min) range. If min=2, max=10, and utilization=55%, the baseline averages 6.4 instances. A spike multiplier captures burstiness; 2.0 doubles compute needs during peak windows. High availability adds overhead for redundancy; 25% is common for multi-zone capacity planning.
Commitment discounts and efficiency
Compute is priced by effective instance-hours. The calculator assumes 730 hours per month and subtracts any free-tier hours. It then splits hours into committed coverage and on-demand. If you cover 60% with a 30% discount, the blended rate falls materially. Efficiency represents tuning gains like right-sizing and autoscaling policies; setting efficiency to 1.15 means you deliver the same load with 15% fewer paid hours.
Data transfer, storage, and managed services
Bandwidth often dominates at scale, so the calculator multiplies projected GB egress by your per‑GB rate and adds optional CDN charges. Storage uses provisioned GB, plus backup percentage for snapshots. Managed services are shown separately to make tradeoffs visible: database instances, load balancers, and monitoring. This separation helps you compare ‘lift-and-shift’ versus ‘managed-first’ plans using the same traffic forecast.
Risk buffers and budget-ready exports
Final totals include support level, contingency buffer, and tax, producing a realistic planning number. A 10% contingency is typical when traffic variance is high or pricing is uncertain. After calculation, you can export the monthly breakdown to CSV for spreadsheets and generate a PDF summary for approvals, ensuring assumptions, unit rates, and scenario results stay auditable. Run at least three scenarios: conservative, expected, and aggressive. Comparing totals highlights whether limits on max instances, egress caps, or reserved coverage deliver better ROI than adding capacity today quickly.
It adjusts how compute grows versus traffic. Values below 1.0 assume caching and queueing reduce linear growth. Use 0.70–0.85 for cache-heavy sites, and 0.90–1.00 for CPU-bound applications.
Use 1.0 for steady workloads. Set 1.5–2.5 when promotions or events cause bursts. If your max instances already reflect peak, keep the multiplier closer to 1.0 to avoid double-counting.
Redundancy requires extra capacity for failover, maintenance, and zone imbalance. Common planning ranges are 10–30%. If you run active-active across zones, choose a higher value than single-zone deployments.
Coverage percent defines what share of instance-hours receive the discount. The model prices covered hours at the discounted rate and the remainder at on-demand. Use your provider’s actual commitment terms for accuracy.
Efficiency represents optimization gains from right-sizing, autoscaling policies, better caching, and performance tuning. Values above 1.0 reduce paid hours; 1.10 means roughly 10% fewer compute hours for the same load.
Egress is charged per GB and grows directly with user activity and page size. When compute is discounted or optimized, data transfer can become the largest line item, especially for media, downloads, and API-heavy traffic.