Cloud CDN pricing (GCP): bandwidth, requests, and origin egress (cache fill)

Reviewed by CloudCostKit Editorial Team. Last updated: 2026-01-27. Editorial policy and methodology.

Start with a calculator if you need a first-pass estimate, then use this guide to validate the assumptions and catch the billing traps.

CDN Cost Calculator CDN Bandwidth Cost Calculator CDN Request Cost Calculator Data Egress Cost Calculator

Cloud CDN pricing becomes predictable when you break it into three drivers, but the real budgeting decision is whether Cloud CDN cost is mainly an edge bandwidth problem, a request-shape problem, or a cache-fill problem caused by weak hit rate. Most surprise bills are caused by cache misses and cold-cache events, not by steady-state hits.

0) Define the boundary (edge vs origin)

Edge bandwidth: bytes delivered from CDN to end users.
Origin egress: bytes pulled from origin to CDN on cache miss/fill.
Requests: request count at the edge; keep a separate count for origin if your origin pricing cares.

1) Edge bandwidth (GB/month)

Use a representative traffic window. If you only have RPS, estimate transfer from response size and request volume. If you have a few very large endpoints (downloads, media), model them separately.

Tools: CDN bandwidth, Response transfer, Unit converter.

2) Requests (per month)

Convert RPS to monthly requests and keep units explicit (per 10k vs per 1M). Request fees often dominate for API-like traffic with small payloads.

Tools: RPS to monthly requests, CDN request fees.

Keep a peak scenario for bot spikes and incident traffic.
If you have many small objects per page, request counts scale faster than bandwidth.

3) Origin egress (cache fill)

Origin egress is driven by cache misses. A practical estimate is: origin GB ~= edge GB * (1 - hit rate). Purges, deploys, and low TTLs reduce hit rate and increase cache fill.

Tool: Data egress cost.

Model large objects separately: one download path can dominate origin GB even when overall hit rate looks healthy.
Track cold cache events: a purge plus a large deploy can temporarily turn most traffic into cache fill.

Worked estimate template (copy/paste)

Edge requests/month = baseline + peak
Edge GB/month = requests/month * avg response size (GB) (split big endpoints)
Origin GB/month ~= edge GB/month * (1 - hit rate)

Common pitfalls

Double-counting edge bandwidth and origin egress.
Using one blended response size when a few endpoints dominate bytes.
Ignoring purge/deploy behavior and cold-cache events.
Mixing request pricing units (per 10k vs per 1M).
Assuming hit rate is stable; it varies by path, TTL, and cache key configuration.

How to validate

Validate cache hit rate overall and by path, and measure changes during deploys/purges.
Validate top endpoints by bytes and request volume (avoid blended averages).
Validate peak windows where retries or bot traffic increase requests and bandwidth.

Sources

A practical Azure CDN estimate: edge bandwidth, request volume, and origin egress (cache fill). Includes validation steps for hit rate, purge behavior, and big endpoints.

CloudFront vs Cloudflare CDN cost: compare the right line items (bandwidth, requests, origin egress)

A practical comparison checklist for CloudFront vs Cloudflare pricing. Compare bandwidth ($/GB), request fees, region mix, origin egress (cache fill), and add-ons like WAF, logs, and edge compute. Includes a modeling template and validation steps.

GCP Cloud Run Pricing: Estimate requests, CPU-seconds, memory-seconds, concurrency, and egress

Estimate Cloud Run pricing from requests, CPU-seconds, memory-seconds, concurrency behavior, egress, and logs so you can explain whether spend is coming from scale, slow handlers, retries, or large responses.

GCP load balancing pricing: hours, requests, traffic processed, and egress

A driver-based approach to load balancer cost: hours, request volume, traffic processed, and (separately) outbound egress. Includes a worked estimate template, pitfalls, and a workflow to estimate GB from RPS and response size.

Azure Front Door pricing: model requests, bandwidth, and origin traffic

A practical Azure Front Door cost model: edge bandwidth, request volume, logging, and origin traffic (cache fill). Includes a checklist to validate hit rate and avoid double-counting egress.

CloudFront cache hit rate: how it changes origin egress cost

Cache hit rate strongly influences origin requests and origin egress (cache fill). Learn a simple model, what breaks hit rate, and the practical levers to improve it safely.

Related calculators

CDN Cost Calculator

Estimate total CDN cost from bandwidth and request pricing.

CDN Bandwidth Cost Calculator

Estimate monthly CDN bandwidth cost from GB transferred and $/GB pricing.

CDN Request Cost Calculator

Estimate CDN request fees from monthly requests and $ per 10k/1M pricing.

Data Egress Cost Calculator

Estimate monthly egress spend from GB transferred and $/GB pricing.

API Response Size Transfer Calculator

Estimate monthly transfer from request volume and average response size.

VPC Data Transfer Cost Calculator

Estimate data transfer spend from GB/month and $/GB assumptions.

FAQ

What usually drives CDN cost?

Bandwidth is usually the biggest driver. Requests matter for API-like traffic and for sites with many small objects. Origin egress matters when hit rate is low or purges/deploys cause cold cache.

How do I estimate quickly?

Estimate monthly edge GB and edge requests, then estimate origin GB from cache hit rate (misses drive cache fill). Keep peak scenarios for bot spikes and cold cache events.

How do I validate?

Validate cache hit rate overall and by path, validate top endpoints by bytes, and validate purge/deploy behavior that temporarily increases cache misses.

What is the most common mistake?

Double-counting edge bandwidth and origin egress as the same GB, or using one blended response size when a few endpoints dominate bytes.

Last updated: 2026-01-27. Reviewed against CloudCostKit methodology and current provider documentation. See the Editorial Policy .