95 articles on gcp — what drives the cost, how it is priced, and where the savings actually are.
Dataproc Serverless runs Spark jobs without a standing cluster, billing per compute unit for the duration of each job, which eliminates idle cluster cost. Here is how it is priced and when it beats a managed Dataproc cluster.
Pub/Sub bills per gigabyte of message throughput with no capacity planning, while Pub/Sub Lite bills for provisioned capacity at a much lower unit rate. Here is how the two compare on cost and when Lite's savings justify its extra management.
Moving data between Google Cloud regions is billed per gigabyte, with rates that depend on whether the regions are on the same continent or across the world. Here is how inter-region egress is priced and how to design around it.
Google Cloud offers two kinds of committed use discount, resource-based and spend-based flexible commitments, and the flexible kind trades some discount depth for the freedom to change machine types and regions. Here is how they compare on cost.
GCP's billing export streams detailed cost data straight into BigQuery, making SQL your cost analysis tool. It is powerful and easy to start, but the queries themselves can get expensive. Here is how to use it well.
EKS and GKE both charge a control-plane fee and node compute, but their pricing details, autopilot options, and free-tier treatment differ enough to matter. Which cloud is cheaper for Kubernetes depends on cluster count and how you run nodes. Here is the comparison.
Vertex AI cost spans training jobs, online prediction endpoints, notebooks, and pipelines, and the biggest drains are always-on endpoints and idle notebooks. Here is how to optimize each part of GCP's managed ML platform.
BigQuery cost splits into query processing (bytes scanned or slots) and storage. Partitioning, clustering, avoiding SELECT star, and choosing on-demand versus capacity pricing are the biggest levers. Here is the detailed playbook.
GCP prices Compute Engine and other services differently by region, with US regions often cheapest. Choosing a low-cost region for latency-tolerant workloads is simple savings. Here is how to find and use the cheapest GCP region.
Google Cloud billing reports break spend down by project, service, SKU, and label, with filters and grouping to find what drives cost. Here is how to read GCP billing reports, use SKUs and labels, and export to BigQuery for deeper analysis.
Google Cloud committed use discounts (CUDs) cut compute and other costs by roughly 20 to 57 percent in exchange for a one or three year commitment. Resource-based and spend-based CUDs work differently. Here is how CUDs work and when they save the most.
Google Cloud has two free offerings: a 90-day, roughly 300 dollar credit for new accounts, and an always-free tier with monthly allowances for specific services. Here is what is truly free, where the limits are, and how charges begin.
Google Cloud prices the same resources differently by Region, sometimes by 20 percent or more, and egress between Regions and to the internet varies too. Here is how GCP Region pricing works and how to pick Regions that balance cost, latency, and compliance.
Google Cloud Storage has four classes trading storage price against retrieval cost and minimum durations. Standard is cheapest to access, Archive cheapest to store. Here is how the classes are priced and how to pick the right one for each data set.
GKE Standard bills for the nodes you provision; GKE Autopilot bills for the pod resources you request. Autopilot removes node management and idle waste but charges a premium per resource. Here is how the two pricing models compare and when each wins.
Secret Manager bills about $0.06 per active secret version per location per month and about $0.03 per 10,000 access operations. It is cheap unless you store many versions or access secrets on every request. Here is the model and the caching lever.
Cloud Composer bills environment compute (per-hour, always on) plus the underlying GKE, database, and storage. The always-on environment fee is significant, so consolidating DAGs and right-sizing the environment matter. Here is the breakdown.
Cloud Run jobs bill per vCPU-second and memory-GiB-second while the job runs, with no idle cost since jobs run to completion and stop. It suits batch and scheduled work. Here is how the model works and how it differs from Cloud Run services.
Filestore bills per GB-month of provisioned capacity by tier (Basic HDD, Basic SSD, Zonal, Enterprise), with tier minimums. You pay for the capacity you allocate, not what you use, so tier and size choice drive the bill. Here is how to pick.
Cloud DNS bills about $0.20 per managed zone per month plus about $0.40 per million queries, similar to Route 53. For most domains it is a few dollars. Here is the breakdown and where cost hides for high-query or many-zone setups.
Vertex AI cost spans custom training (compute-hours), prediction endpoints (always-on nodes), pipelines, and notebooks. Idle endpoints and workbench instances are the biggest waste. Here is how to cut it.
Datastream bills per GB of change-data-capture (CDC) data processed from source databases, plus backfill of existing data. High-change-rate sources and large backfills drive the bill. Here is the CDC replication model.
Google Cloud Workflows bills per step executed, with internal steps very cheap (and a free tier) and external HTTP calls slightly more, so orchestration itself is nearly free. The real cost is what the steps call. Here is the model.
Google-managed public TLS certificates are free; the cost is Certificate Authority Service for private CAs, billed per CA per month plus per certificate issued. For public TLS, there is nothing to pay. Here is the model.