| Scenario | Reads (M) | Writes (M) | Read data (GiB) | Write data (GiB) | Approx total |
|---|---|---|---|---|---|
| Small app | 2 | 0.8 | 120 | 60 | $1.94 |
| API-heavy service | 40 | 15 | 350 | 180 | $26.30 |
| Analytics ingestion | 8 | 50 | 220 | 1,100 | $37.20 |
| Media delivery | 120 | 5 | 9,000 | 120 | $150.10 |
| Backup snapshots | 1 | 4 | 40 | 900 | $11.90 |
- Data volume (bytes) = operations × average payload bytes, or entered total bytes.
- GiB = bytes ÷ 1024³.
- Billable operations = max(0, adjusted operations − free operations).
- Billable GiB = max(0, adjusted GiB − free GiB).
- Request cost = (billable ops ÷ 1,000,000) × price per 1M.
- Data cost = billable GiB × price per GiB.
- Subtotal = read request + write request + read data + write data.
- Reserve = subtotal × reserve%.
- Tax/fees = (subtotal + reserve) × tax%.
- Total = subtotal + reserve + tax/fees.
- Select your workload period and optionally scale to monthly.
- Enter read/write operations and average payload sizes.
- If you already know totals, enter total read/write data instead.
- Paste your provider rates for requests and data movement.
- Add free tiers, overheads, reserve, and taxes if needed.
- Click Calculate to see totals, breakdowns, and export options.
Cost drivers in read and write workloads
Read/write spend typically splits into request fees and data volume charges. High request rates with tiny objects inflate per‑million charges, while large payloads push GiB costs. Many teams see seasonal bursts: steady writes from ingestion and spiky reads from cache misses. This calculator separates reads and writes, then applies free tiers and overhead to show what becomes billable clearly.
Request fees: small payloads, big impact
Request pricing matters most when average payload sizes are low. For example, millions of 4–16 KB reads can produce modest GiB totals yet meaningful request line items. The model converts operations to billable operations after overhead and free tiers, then multiplies by your “per 1M” rate. Use the effective cost per 1M outputs to validate whether batching, caching, or pagination would reduce request pressure.
Data volume charges: GiB math and compression
Data charges scale with bytes moved, so accurate sizing is critical. If you know total read/write data, enter it directly to avoid guesswork. Otherwise, operations × average size estimates volume, then converts to GiB using 1024³. Compression, protocol framing, and metadata can shift real traffic by 5–30%, so the data overhead field helps you bound outcomes. Compare read versus write GiB to spot asymmetric flows and egress-heavy patterns.
Free tiers, overheads, and budget buffers
Free allowances can materially change unit economics at low volumes, but they disappear quickly under sustained load. This calculator subtracts free operations and free GiB after overhead adjustments, matching how bills often apply thresholds. Add API overhead to model retries, background scans, or multi-part operations. Add reserve percent for contingency and tax/fees for invoicing reality. Together, these controls turn a “best case” estimate into a planning-grade forecast.
Operational use: forecasting, reporting, and optimization
Use the period selector to match your telemetry window, then scale hourly or daily inputs into a monthly view for budgeting. Export CSV to share assumptions with finance or to track changes across releases. For optimization, run scenarios: reduce average object size, lower read operations via caching, or shift write bursts to fewer, larger batches. Small architectural moves often cut both request and GiB components, improving reliability and cost predictability.
1) Should I use per-operation or total-data mode?
Use total-data when you already measure GiB transferred or stored. Use per-operation when you track request counts and average payload size. If both are entered, total-data is applied for that direction.
2) How does monthly scaling work?
When enabled, hourly inputs are multiplied by 730 and daily inputs by 30. This gives a consistent monthly estimate for budgeting. If you already enter monthly numbers, keep scaling off.
3) What do API and data overhead percentages represent?
API overhead increases request counts to model retries, scans, or multi-part actions. Data overhead increases bytes to reflect metadata, protocol framing, or compression differences between logical and on‑wire size.
4) How are free tiers applied?
Free operations and free GiB are subtracted after overhead and (optional) monthly scaling. The remaining billable amounts are priced. Set free tiers to zero if your service has no included usage.
5) Can this model database I/O or object storage?
Yes. The inputs are generic: operations priced per million and data priced per GiB. Map reads and writes to your service’s request types, then paste your published rates and allowances.
6) Why might my bill differ from this estimate?
Real bills can include additional dimensions like region, tiered pricing, minimums, replication, and egress classes. Use this tool for scenario planning, then calibrate by comparing one billing cycle and adjusting overheads.