GCP cost guides
Use this hub when the provider boundary is the real question, not just the category label.
What this hub helps you route
Start here when a GCP-specific path like inter-zone traffic, serverless retries, or managed analytics/storage behavior needs to be separated before you can trust the estimate.
- Pick the next guide by boundary first, not by whichever service name looks closest.
- Move to calculators when you already have request, GB, GB-month, or runtime inputs.
- Use the service guide directly once the provider-specific unit path is clear enough to model.
GCP-specific estimate failures
GCP estimates usually drift when inter-zone transfer, serverless retry multiplication, and logging growth are modeled after the main service price instead of before it.
- Transfer paths are blended instead of separating internet, inter-zone, and cross-region movement.
- Serverless estimates stop at successful requests and ignore retries, cold-start mitigation, or timeout spillover.
- Observability is budgeted as a side effect even when label cardinality and query patterns are driving spend.
- Storage and analytics workloads skip lifecycle, retention, and replay/backfill scenarios.
Best first routes inside GCP
Transfer and edge
Start here when inter-zone, cross-region, CDN origin, or load-balancer traffic is the part most likely to distort the estimate.
Serverless and request-heavy
Start here when Cloud Run, Cloud Functions, Pub/Sub, or retry multiplication is more important than the headline service price.
Storage, databases, and observability
Start here when Cloud Storage, Cloud SQL, BigQuery, Bigtable, Logging, or Monitoring are compounding in the background.
Networking and delivery
Use these when transfer boundaries, traffic processed, or origin-vs-edge behavior are the real pricing problem.
Artifact Registry pricing (GCP): storage + downloads + egress (practical estimate)
A practical Artifact Registry cost model: stored GB-month baseline, download volume from CI/CD and cluster churn, and outbound transfer. Includes a workflow to estimate GB-month from retention and validate layer sharing and peak pull storms.
Bigtable cost estimation: nodes, storage growth, and transfer (practical model)
A driver-based Bigtable estimate: provisioned capacity (node-hours), stored GB-month + growth, and network transfer. Includes validation steps for hotspots, compactions, and peak throughput that force over-provisioning.
Cloud CDN pricing (GCP): bandwidth, requests, and origin egress (cache fill)
A practical Cloud CDN cost model: edge bandwidth, request volume, and origin egress (cache fill). Includes validation steps for hit rate by path, heavy-tail endpoints, and purge/deploy events that reduce hit rate.
Cloud Functions pricing (GCP): invocations, duration, egress, and log volume
A practical Cloud Functions cost model: invocations, execution time, outbound transfer, and logs. Includes a workflow to estimate baseline + peak and validate retries, cold starts, and log bytes per invocation.
Cloud NAT cost (GCP): why it spikes and how to model outbound traffic
A practical Cloud NAT estimate: baseline configuration + outbound GB processed through NAT, with a peak scenario for retries, node churn, and dependency storms. Includes a validation checklist and cost-reduction levers.
Cloud SQL pricing: instance-hours, storage, backups, and network (practical estimate)
A driver-based Cloud SQL estimate: instance-hours (HA + replicas), storage GB-month, backups/retention, and data transfer. Includes a worked template, common pitfalls, and validation steps for peak sizing and growth.
GCP Cloud Run Pricing Guide: Cost Calculator Inputs for Requests, CPU, and Egress
Estimate Cloud Run cost using requests, duration, concurrency, transfer, and logs. Includes practical calculator inputs and validation steps.
GCP Cloud Storage Pricing & Cost Guide
Understand Cloud Storage cost drivers: storage class, operations, retrieval, and egress with estimation steps.
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.
GCP VPC egress costs: estimate outbound transfer by destination (practical workflow)
A practical method to estimate GCP outbound transfer: split by destination (internet, cross-region, inter-zone, CDN origin), convert usage to GB/month, and validate boundaries. Includes a worked template, pitfalls, and optimization levers.
Google Kubernetes Engine (GKE) pricing: nodes, networking, storage, and observability
GKE cost is not just nodes: include node pools, autoscaling, requests/limits (bin packing), load balancing/egress, storage, and logs/metrics. Includes a worked estimate template, pitfalls, and validation steps to keep clusters right-sized.
Inter-zone transfer costs on GCP: identify flows, estimate GB/month, and reduce churn
A practical checklist to estimate cross-zone data transfer: load balancers, multi-zone clusters, east-west chatter, and storage/database access patterns. Includes a worked template, validation steps, and control levers.
Private Service Connect costs: endpoint-hours and data processed (practical model)
A practical private connectivity estimate: endpoint-hours plus data processed (GB). Includes a worked template, pitfalls, and validation steps to compare PSC vs NAT/internet egress and avoid paying for both paths.
Pub/Sub pricing: deliveries, retries, fan-out, and payload transfer (practical estimate)
A practical Pub/Sub estimate: publish volume, fan-out (subscriptions), delivery attempts (retries), retention/replay scenarios, and payload transfer. Includes a worked template, pitfalls, and validation steps.
Runtime, analytics, and data services
Use these when managed runtime choice, workload shape, worker scaling, or database/storage growth is what actually moves the bill.
Artifact Registry pricing (GCP): storage + downloads + egress (practical estimate)
A practical Artifact Registry cost model: stored GB-month baseline, download volume from CI/CD and cluster churn, and outbound transfer. Includes a workflow to estimate GB-month from retention and validate layer sharing and peak pull storms.
BigQuery cost estimation: storage, bytes scanned, and the dashboard trap
Estimate BigQuery-style analytics costs with measurable drivers: stored data (GB-month), bytes scanned (per query), and streaming/exports. Includes a workflow to model baseline vs peak and validate partition pruning and dashboard refresh behavior.
Bigtable cost estimation: nodes, storage growth, and transfer (practical model)
A driver-based Bigtable estimate: provisioned capacity (node-hours), stored GB-month + growth, and network transfer. Includes validation steps for hotspots, compactions, and peak throughput that force over-provisioning.
Cloud Functions pricing (GCP): invocations, duration, egress, and log volume
A practical Cloud Functions cost model: invocations, execution time, outbound transfer, and logs. Includes a workflow to estimate baseline + peak and validate retries, cold starts, and log bytes per invocation.
Cloud Logging pricing (GCP): ingestion, retention, and query scans
A practical model for Cloud Logging costs: GB ingested, retention storage (GB-month), and query/scan behavior. Includes a fast method to estimate GB/day from events/sec × bytes/event and a checklist to find dominant sources.
Cloud Spanner cost estimation: capacity, storage, backups, and multi-region traffic
Estimate Spanner cost using measurable drivers: provisioned capacity (baseline + peak), stored GB-month (data + indexes), backups/retention, and multi-region/network patterns. Includes a worked template, common pitfalls, and validation steps.
Cloud SQL pricing: instance-hours, storage, backups, and network (practical estimate)
A driver-based Cloud SQL estimate: instance-hours (HA + replicas), storage GB-month, backups/retention, and data transfer. Includes a worked template, common pitfalls, and validation steps for peak sizing and growth.
Dataflow pricing: worker hours, backlog catch-up, and observability (practical model)
Estimate Dataflow cost using measurable drivers: worker compute-hours, backlog catch-up scenarios (replays/backfills), data processed, and logs/metrics. Includes a worked template, pitfalls, and validation steps for autoscaling and replay patterns.
GCP Cloud Run Pricing Guide: Cost Calculator Inputs for Requests, CPU, and Egress
Estimate Cloud Run cost using requests, duration, concurrency, transfer, and logs. Includes practical calculator inputs and validation steps.
GCP Cloud Storage Pricing & Cost Guide
Understand Cloud Storage cost drivers: storage class, operations, retrieval, and egress with estimation steps.
Google Kubernetes Engine (GKE) pricing: nodes, networking, storage, and observability
GKE cost is not just nodes: include node pools, autoscaling, requests/limits (bin packing), load balancing/egress, storage, and logs/metrics. Includes a worked estimate template, pitfalls, and validation steps to keep clusters right-sized.
Inter-zone transfer costs on GCP: identify flows, estimate GB/month, and reduce churn
A practical checklist to estimate cross-zone data transfer: load balancers, multi-zone clusters, east-west chatter, and storage/database access patterns. Includes a worked template, validation steps, and control levers.
Observability, Kubernetes, and messaging
Use these when GKE overhead, logging growth, monitoring cardinality, or message delivery amplification is the part you need to validate next.
Artifact Registry pricing (GCP): storage + downloads + egress (practical estimate)
A practical Artifact Registry cost model: stored GB-month baseline, download volume from CI/CD and cluster churn, and outbound transfer. Includes a workflow to estimate GB-month from retention and validate layer sharing and peak pull storms.
Cloud Armor pricing (GCP): model baseline traffic, attack spikes, and logging
A practical Cloud Armor estimate: baseline request volume plus an attack scenario (peak RPS × duration). Includes validation steps for spikes, rule footprint, and the secondary cost driver most teams miss: logs and analytics during incidents.
Cloud CDN pricing (GCP): bandwidth, requests, and origin egress (cache fill)
A practical Cloud CDN cost model: edge bandwidth, request volume, and origin egress (cache fill). Includes validation steps for hit rate by path, heavy-tail endpoints, and purge/deploy events that reduce hit rate.
Cloud Functions pricing (GCP): invocations, duration, egress, and log volume
A practical Cloud Functions cost model: invocations, execution time, outbound transfer, and logs. Includes a workflow to estimate baseline + peak and validate retries, cold starts, and log bytes per invocation.
Cloud Logging pricing (GCP): ingestion, retention, and query scans
A practical model for Cloud Logging costs: GB ingested, retention storage (GB-month), and query/scan behavior. Includes a fast method to estimate GB/day from events/sec × bytes/event and a checklist to find dominant sources.
Cloud Monitoring metrics pricing (GCP): time series, sample rate, and retention
A practical metrics cost model: time series count (cardinality), sample rate, retention, and dashboard/alert query behavior. Includes validation steps to prevent high-cardinality explosions and excessive refresh patterns.
Dataflow pricing: worker hours, backlog catch-up, and observability (practical model)
Estimate Dataflow cost using measurable drivers: worker compute-hours, backlog catch-up scenarios (replays/backfills), data processed, and logs/metrics. Includes a worked template, pitfalls, and validation steps for autoscaling and replay patterns.
GCP Cloud Run Pricing Guide: Cost Calculator Inputs for Requests, CPU, and Egress
Estimate Cloud Run cost using requests, duration, concurrency, transfer, and logs. Includes practical calculator inputs and validation steps.
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.
Google Kubernetes Engine (GKE) pricing: nodes, networking, storage, and observability
GKE cost is not just nodes: include node pools, autoscaling, requests/limits (bin packing), load balancing/egress, storage, and logs/metrics. Includes a worked estimate template, pitfalls, and validation steps to keep clusters right-sized.
Pub/Sub pricing: deliveries, retries, fan-out, and payload transfer (practical estimate)
A practical Pub/Sub estimate: publish volume, fan-out (subscriptions), delivery attempts (retries), retention/replay scenarios, and payload transfer. Includes a worked template, pitfalls, and validation steps.
When to skip this hub
- If the estimate problem is still generic storage, egress, or logging math, start from the broader category guide.
- If you already know the exact GCP service and cost unit, go straight to that page.
- If you already have inputs, use GCP calculators first and return here for boundary review.