Cloud cost estimation checklist: build a model Google (and finance) will trust

A good cloud estimate is not a perfect number on day 1. It's a model with explicit drivers, clear assumptions, and a validation loop. This checklist helps you avoid "thin estimates" that ignore the parts of the bill that often dominate at scale: requests, transfer, and observability.

0) Output artifacts (what you should produce)

• Line-item table: each item has (driver, unit price, baseline, peak, notes).
• Assumptions list: what you assumed and how to measure later.
• Validation plan: which metrics/billing reports you will compare against after launch.

1) Choose primary drivers (measure first)

If you cannot name a driver, you cannot validate. Pick the smallest set of drivers that explain most of the cost.

• Requests/month: APIs, queues, databases, CDN requests.
• GB/day or GB/month: egress, CDN bandwidth, replication, backups, scan volume.
• Hours: instances, managed capacity, always-on gateways.
• GB-month stored: storage, logs retained, snapshots/backups.
• Time series / cardinality: metrics scale with series count and retention.

2) Model the big five buckets (with calculators)

1. Compute: instance-hours or vCPU/RAM hours (include headroom). Tool: Compute instance cost.
2. Requests: request-based services add up (per 10k, per 1M, per 100k). Tools: API request cost, CDN request cost, RPS to monthly requests.
3. Network transfer: internet egress, cross-region, cross-zone. Tools: Egress cost, Cross-region transfer.
4. Storage: base GB-month plus growth and replication. Tools: Object storage cost, Storage growth.
5. Observability: logs, metrics, traces (ingestion + retention + scan/search). Tools: Log ingestion, Tiered log storage, Log scan/search, Metrics time series.

3) Add the multipliers most teams forget

• Baseline vs peak: peak windows (deploys, incidents) drive real spend and capacity decisions.
• Retries/timeouts: multiply requests, transfer, and downstream dependency calls.
• Cache hit rate: affects origin egress and origin request volume behind a CDN.
• Region mix: a blended effective $/GB across regions is more accurate than one global number.
• Growth: "flat storage" is usually wrong; model growth and average GB-month.

4) Avoid double counting (the most common trap)

Most estimate errors are not missing a line item. They are counting the same bytes or requests twice under different names.

• CDN bandwidth vs origin egress: edge GB delivered is not the same as origin GB on cache misses.
• Ingestion vs storage vs scan: logs can have three separate charges; do not treat them as one.
• Request fees vs transfer fees: request-based pricing does not include GB unless the vendor says it does.
• Replication transfer vs storage: replication can be both extra transfer and extra stored GB.
• Backup retention vs primary storage: backup copies are not free by default; model retention explicitly.

5) Worksheet template (copy/paste)

Use one row per line item. The important part is explicit drivers and explicit units.

• Line item: name (e.g., "CDN requests", "Log ingestion", "Cross-region transfer")
• Driver: requests/month OR GB/day OR hours/month OR GB-month OR series-month
• Baseline: numeric value + explanation of where it comes from
• Peak: numeric value + what causes it (deploy, incident, batch job)
• Unit price: $ per unit (note the unit: per 10k, per 1M, per GB, per GB-month)
• Owner: who will validate and own the lever (app team, infra, data)

6) Validation loop (what to do after launch)

• Week 1: compare estimate drivers to real metrics (requests/day, GB/day, retained GB).
• Week 2: compare estimate totals to billing exports; reconcile mismatches by line item.
• Monthly: re-estimate with growth trends and update baseline/peak assumptions.

Use Unit converter to sanity-check GB vs GiB and Mbps vs MB/s conversions.

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