Model visits, page weight, caching, and growth assumptions. See bandwidth, compute, and storage requirements instantly. Choose better hosting tiers before traffic spikes and costs.
White theme layout with responsive calculator fields, cost breakdown, export tools, formulas, and step-by-step guidance.
| Input | Example Value | Notes |
|---|---|---|
| Monthly Visitors | 250,000 | Includes all sources before bot filtering. |
| Pages Per Visit | 3.6 | Average browsing depth across sessions. |
| Average Page Size | 850 KB | Typical page payload with media assets. |
| Cache Hit / Compression / CDN | 72% / 58% / 70% | Optimization stack lowers origin transfer and compute. |
| Peak Hour Share / Safety | 1.2% / 1.4x | Protects sizing against spikes. |
| Storage + Backups | 94 GB source, 8 copies | Adds media, database, logs, and snapshot retention. |
| Pricing Inputs | BW $0.05, vCPU $24, RAM $5 | Change these to match your provider rate card. |
The calculator estimates hosting capacity and cost from traffic, optimization, compute intensity, storage, and pricing inputs. It converts visitor behavior into requests, then derives transfer and server requirements.
Human Visitors = Monthly Visitors × (1 − Bot %)Monthly Requests = Human Visitors × Pages per VisitRaw Bandwidth (GB) = Requests × Avg Page KB ÷ 1024 ÷ 1024Optimized Bandwidth = Raw Bandwidth × (1 − Compression %)Origin/CDN Split = Optimized Bandwidth × (1 − CDN %) / CDN %Uncached Requests = Monthly Requests × (1 − Cache Hit %)CPU Hours = Uncached Requests × CPU ms ÷ 1000 ÷ 3600Peak RPS = Monthly Requests × Peak Hour % × Safety Factor ÷ 3600Primary Storage = Media + Database + (Daily Logs × Retention)Backup Storage = (Media + Database) × Backup Copies × Backup Delta %Total Cost = Bandwidth + Compute + Primary Storage + Backup Storage + OverheadA growth reserve percentage is applied to bandwidth, storage, and compute to support planned expansion, campaign traffic, or seasonal variation.
Start with measured traffic quality before pricing infrastructure. In the example profile, 250,000 monthly visitors and 18% bot traffic produce 205,000 human visitors. With 3.6 pages per visit, demand rises to 738,000 page requests. This baseline drives monthly bandwidth, cache misses, and compute utilization. Teams should validate analytics filters, exclude internal visits, and align campaign calendars, because weak visitor assumptions distort downstream hosting recommendations and reserve planning.
Page weight and delivery optimization strongly affect hosting cost. Using 850 KB average page size, the calculator converts request volume into raw transfer, then applies 58% compression and 70% CDN offload assumptions. This reduces origin bandwidth pressure and improves response consistency while lowering upstream egress costs. Organizations should review page weight distributions, not only averages, because landing pages, product pages, and media articles create different transfer patterns during campaigns, launches, and seasonal shifts.
Compute sizing is estimated from uncached work and peak hour behavior. A 72% cache hit ratio leaves 28% of requests uncached, and each uncached request consumes configured CPU milliseconds. The calculator converts this into monthly CPU hours and average vCPU load, then checks peak requests per second using peak share and safety factor inputs. This supports practical capacity planning by balancing efficiency, latency targets, and resilience during burst traffic windows and incident recovery.
Storage planning includes media, databases, logs, and backups, which are often priced separately. In the example, primary storage combines 80 GB media, 14 GB database data, and retained logs derived from 350 MB daily volume over 30 days. Backup storage uses copy count and delta percentage, reflecting snapshot efficiency. This structure helps teams compare providers fairly and prevents underbudgeting caused by ignored observability retention, compliance storage, or backup growth across regions.
Pricing inputs translate technical demand into a monthly budget model. The calculator multiplies billable bandwidth, compute, primary storage, and backup storage by unit costs, then adds platform overhead and applies a growth reserve percentage. This reserve protects plans against campaign volatility, regional rollout, or product launches. Finance and engineering teams can export CSV or PDF results, test assumptions quickly, and agree on a hosting tier before procurement commitments are finalized internally confidently.
Use your web analytics, CDN analytics, and server logs together. Start with the last three normal months, remove internal traffic, and separate bot traffic so visitor and page-view assumptions match real demand.
Set peak hour share from hourly analytics. If your best hour usually handles 1.0% of monthly requests, use 1.0 and add a safety factor like 1.2 to 1.5 for campaigns or unexpected spikes.
Use compressed transfer and post-cache figures when available. If you only have page weight from page-speed tools, start there, then adjust compression and CDN offload inputs until estimates align with recent hosting invoices.
Yes. Enter different unit costs, overhead, and optimization assumptions for each provider. Keep traffic and behavior inputs constant so differences in totals reflect pricing and architecture choices rather than changing demand.
Review analytics monthly and update before campaigns, launches, or seasonal events. Recalculate immediately when page weight, cache strategy, media usage, or backup retention changes because those settings can materially shift costs.
No. It is a planning calculator, not a load-testing replacement. Use it for budget forecasting and initial sizing, then validate performance with monitoring, synthetic tests, and peak traffic observations after deployment.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.