Inputs
Example Data Table
| Scenario | Requests | Hourly Rate | Egress (GB) | Total Cost | Cost / Request |
|---|---|---|---|---|---|
| Baseline | 1,000,000 | 0.1200 | 250 | USD 4,923.10 | 0.004923 |
| Reserved -30% | 1,000,000 | 0.1200 | 250 | USD 4,304.54 | 0.004305 |
| Higher Egress | 1,000,000 | 0.1200 | 600 | USD 5,957.10 | 0.005957 |
Formula Used
- RPS = Requests ÷ Seconds (seconds: day = 86,400; month = 30×86,400).
- RequiredConcurrency = RPS × (CPUms ÷ 1000) × (1 + Buffer%).
- EffectiveConcurrency = Concurrency × Utilization%.
- NeededInstances = max(MinInstances, ceil(MinInstances × max(1, RequiredConcurrency ÷ EffectiveConcurrency))).
- ComputeCost = InstanceHours × HourlyRate × (1 − ReservedDiscount%).
- StorageCost = StorageGB × RateGBMonth (daily divides by 30).
- EgressCost = EgressGB × RateGB × (1 − CDNSavings%).
- GrandTotal = DirectSubtotal + Support% + Overheads% + Contingency%.
- CostPerRequest = GrandTotal ÷ Requests.
How to Use This Calculator
- Choose your billing period and enter expected requests.
- Enter average CPU time and typical concurrency.
- Set utilization and autoscale buffer for headroom.
- Provide compute, storage, and egress rates from invoices.
- Add managed services, observability, and percent add-ons.
- Press Submit to see totals and per-request cost.
- Export CSV or PDF to share with stakeholders.
Traffic Forecasting and Service Demand
Serving spend follows request volume and how bursty traffic becomes. The calculator turns your period total into requests per second, then uses average CPU time to estimate required concurrency. When demand doubles, compute often scales near linear unless batching, caching, or model distillation lowers CPU per request. Use logs, growth targets, and launch calendars to set request counts, and run separate estimates for peak days versus typical days. For real-time APIs, include retries and background calls, and align the request definition with your billing and telemetry conventions across all environments.
Capacity Planning with Utilization and Headroom
Utilization reflects how much slack you keep for latency and failures. Lower utilization increases needed instances because each instance carries less effective concurrency. The autoscale buffer adds headroom for spikes, warm capacity, and noisy neighbors. For multi-region or tight SLOs, raise minimum instances and buffer, then compare instance-hours. The memory indicator helps validate that models, caches, and request context fit without thrashing.
Cost Drivers: Compute, Storage, and Egress
Compute cost equals instance-hours times your hourly rate, with reserved discounts applied only to compute. Storage estimates persistent artifacts and logs using a monthly per‑GB rate, scaled down for daily views. Egress multiplies outbound gigabytes by your per‑GB rate and optionally reduces it using CDN savings. In inference systems, egress can rival compute when responses include embeddings, images, or verbose payloads.
Scenario Analysis and Budget Sensitivity
Build three cases: baseline, conservative, and aggressive. Conservative budgets increase requests, lower utilization, and raise egress to reflect peak behavior. Add reserved discounts to test commitment benefits. Adjust managed services and observability for production readiness, then apply overhead and contingency percentages to match internal policy. Track both total cost and cost per request; if cost per request is high, target CPU time, cache hit rate, and payload size.
Operational Reporting and Stakeholder Communication
Export CSV for deeper analysis and use the PDF for review meetings. Record key assumptions such as utilization, buffer, and unit rates because they explain most variance across scenarios. Refresh inputs after load tests, major releases, or provider price changes, and keep snapshots so teams can compare quarter-over-quarter efficiency and reliability tradeoffs.
FAQs
1) What does cost per request represent?
It is the estimated total serving cost for the selected period divided by total requests. It helps compare optimizations and pricing models, but it depends on your utilization, buffer, and rate assumptions.
2) Why does utilization change the instance count?
Utilization approximates how much effective capacity you can safely use. Lower utilization means more reserved slack for latency and failures, so each instance carries fewer concurrent requests and you need more instances.
3) How should I choose the autoscale buffer?
Start with 10–20% for stable workloads. Increase it for spiky traffic, cold starts, strict latency targets, or multi-tenant systems. Validate with load tests and production incident reviews.
4) Do reserved discounts apply to all costs?
In this model, reserved discounts reduce compute only, because storage, egress, and tool subscriptions are typically billed separately. If your provider bundles discounts, adjust the relevant unit rates accordingly.
5) How can I reduce data egress costs?
Use compression, trim payload fields, prefer streaming or pagination, and add caching or a CDN where appropriate. Also consider regional placement to reduce cross-region transfer and repeated downloads.
6) Is this suitable for model inference services?
Yes. Enter the average CPU time for preprocessing and inference, include managed components like vector stores, and model egress for responses. For GPUs or specialized hardware, set the hourly rate to your blended cost.