91 articles on gcp — what drives the cost, how it is priced, and where the savings actually are.
The three big managed Kubernetes services charge similar control-plane fees but diverge sharply on node pricing, load balancers, and free tiers. A like-for-like cluster can differ by 15 to 20 percent across them. Here is the comparison with real numbers.
The T4 has been the default cheap inference GPU for years, but the L4 costs about 53 percent more per hour while delivering two to three times the throughput on modern models. For inference fleets, that inversion is worth real money.
Hourly rates hide enormous differences in delivered compute. Dividing price by teraflops and by memory bandwidth turns a confusing price list into a ranking you can actually reason about.
On Google Cloud you can run models on Vertex AI or on your own GKE cluster with GPU node pools. The hardware rates are similar, so the decision comes down to cluster fees, discount eligibility, and who operates it.
Pub/Sub prices throughput at a flat rate per TiB with no shards to provision, which is either a bargain or a trap depending on your fan out factor and retention settings. Here is the full meter list.
Not all outbound traffic is billable. Every major cloud has a list of destinations where egress is free, and routing through them is one of the cheapest optimizations available. Here is the list and how to use it.
Private Service Connect gives you a private IP inside your VPC for a Google or third-party service. It charges per endpoint hour and per gigabyte, and it usually beats routing the same traffic through Cloud NAT.
Google prices Cloud SQL by vCPU and GB separately; AWS prices RDS by fixed instance shape. That single difference changes which cloud is cheaper for your particular ratio of CPU to memory. Here is the comparison at real prices.
GCP has its own waste profile: idle persistent disks, unattached external IPs, snapshot sprawl, oversized machine types, and BigQuery scans. Here is the sequenced playbook with real rates and expected savings per lever.
A week has 168 hours and most non-production workloads need about 45. Turning things off for the rest is the highest ratio saving in cloud. Here is the arithmetic, the implementation, and the resources that keep billing anyway.
Google Cloud applies sustained use discounts automatically to Compute Engine VMs that run for a large fraction of the month, cutting the effective rate by up to 30 percent with no commitment. Here is how they work and how they stack with committed use discounts.
Compute Engine custom machine types let you pick vCPU and memory independently instead of taking a predefined shape, which can cut cost when your workload does not match a standard ratio. Here is how the pricing works and when custom pays off.
Every GKE cluster carries a flat management fee of about 0.10 dollars per hour, roughly 73 dollars a month, on top of the compute it runs, with one free cluster per billing account. Here is how the fee works and when it matters for your cost.
GKE Autopilot bills you for the CPU, memory, and ephemeral storage your pods request, not for nodes, so cost tracks your resource requests directly. Here is how the per-pod rates work and how to keep requests honest.
Cloud Run scales to zero by default, but setting a minimum number of instances keeps them warm to avoid cold starts, and those idle instances bill continuously at a reduced rate. Here is what min instances cost and when they are worth it.
Cloud Run offers two CPU allocation modes, one that bills CPU only during requests and one that keeps CPU always allocated, and the choice changes both cost and behavior. Here is how each is priced and which fits your workload.
Cloud Functions 2nd gen runs on Cloud Run and Eventarc under the hood, which changes its pricing from the 1st gen model. Here is how 2nd gen bills, how it differs from 1st gen, and how to keep function cost low.
Enabling high availability on Cloud SQL provisions a standby instance in another zone, which roughly doubles the compute and storage cost of the database. Here is exactly what HA adds and when the resilience is worth the price.
Cloud Spanner is priced by processing units, where 1000 units equal one node, letting you provision capacity in increments as small as 100 units instead of whole nodes. Here is how processing units are billed and how to size them.
Bigtable lets you pick SSD or HDD storage per cluster, and the choice changes both storage cost and performance significantly, with HDD storage far cheaper but much slower. Here is how the two compare and when each fits.
BigQuery lets you stream rows in for immediate querying, but streaming ingestion is billed per gigabyte on top of storage and query costs, unlike free batch loads. Here is what streaming costs and how the Storage Write API changes the math.
BigQuery editions let you buy query capacity as slots with autoscaling, so you pay for compute that follows demand instead of a flat commitment or unpredictable on-demand scans. Here is how slot autoscaling is priced and when it beats on-demand.
Persistent Disk snapshots are incremental and billed per gigabyte of stored data, so their cost depends on how much changes between snapshots, not the disk size. Here is how snapshot billing works and how to stop snapshots creeping up your bill.
Filestore comes in tiers from Basic HDD to Enterprise, with per-gigabyte prices that vary several-fold and minimum capacities that set a cost floor. Here is how the tiers compare on cost and which fits your file workload.