91 articles on gcp — what drives the cost, how it is priced, and where the savings actually are.
Hyperdisk is GCP's newer block storage that decouples capacity, IOPS, and throughput, so you provision and pay for each independently. This flexibility avoids over-buying, but over-provisioning any dimension wastes money. Here is the model.
Dataplex bills per data compute unit (DCU) hour for its processing tasks (discovery, quality, tiering) plus shuffle storage, and its catalog metadata. Running unnecessary tasks and over-provisioning DCUs drive the bill. Here is the model.
Cloud Vision bills per image per feature (label, face, text, object, safe-search, and more), so requesting several features on one image bills for each, with a free tier and volume discounts. Analyzing more features than needed drives the bill.
Security Command Center has a free Standard tier and paid Premium and Enterprise tiers priced by subscription or by resource consumption. Enabling premium across an entire organization drives the bill. Here is how the tiers price.
Cloud Workstations bills a management fee plus the underlying Compute Engine VM and storage while a workstation runs, with auto-shutdown when idle. Idle workstations left running are the classic waste. Here is the model and how to control it.
Document AI bills per page processed, with rates by processor, cheap general OCR, more for form and specialized parsers, and higher for custom and generative processors. Running specialized processors on simple documents over-pays. Here is the model.
Speech-to-Text bills per minute of audio (rounded up), with higher rates for premium and specialized models and features like diarization. Long audio and premium models drive the bill. Here is the per-minute model and how to control it.
Apigee is a full API management platform priced by subscription tier (or pay-as-you-go per API call), with a significant baseline for the standard tiers. It is powerful but not cheap. Here is how it prices and when a lighter gateway suffices.
GCP Batch adds no charge of its own; you pay for the Compute Engine VMs it runs your jobs on, including Spot. Spot workers, right-sized machines, and jobs that scale to zero are the levers. Here is how to run batch cheaply.
BigQuery storage bills active data (recently modified) at about $0.02/GB-month and long-term data (untouched 90 days) at about half that, with an option to bill physical (compressed) bytes instead of logical. Here is how to cut storage cost.
Cloud SQL now has two editions: Enterprise and Enterprise Plus. Enterprise Plus costs more per vCPU and memory but adds a data cache, better performance, and near-zero-downtime maintenance. Here is how the editions price and when the upgrade pays off.
AlloyDB bills compute per vCPU-hour and memory, plus regional storage per GB-month with no separate I/O charge, plus backups. It is a PostgreSQL-compatible database aimed at demanding workloads. Here is how it prices versus Cloud SQL.
Cloud TPU bills per chip-hour by TPU version and configuration, with on-demand, Spot (preemptible), and committed-use pricing. Idle TPUs and full-price on-demand for interruptible training are the expensive mistakes. Here is how to cut cost.
Vertex AI bills training per node-hour, online prediction per node-hour for the endpoint you keep deployed, plus AutoML and model API usage. Idle deployed endpoints are the classic surprise. Here is where the money goes and how to cut it.
Cloud Logging charges about $0.50 per GB ingested after a 50 GB monthly free allowance, plus retention beyond the default. Exclusion filters and sampling are the main levers. Here is how the ingestion-based model works and where cost hides.
Cloud Build bills per build-minute by machine type, with a daily free allowance. Slow builds and oversized machines drive the cost. Caching, parallelism, and right-sized machines are the levers. Here is how the per-minute model works.
Cloud Tasks bills per operation (task creation and dispatch), with the first million operations free each month and about $0.40 per million after. It is inexpensive; the cost to watch is the work the tasks trigger. Here is the model.
GCP offers two network service tiers: Premium routes over Google's backbone for best performance, Standard routes over the public internet for cheaper egress. Choosing Standard where performance allows cuts egress cost. Here is the tradeoff.
Dataflow bills per vCPU-hour and GB-hour of worker time, plus a data-processed charge for Shuffle (batch) or Streaming Engine. Right-sizing workers and using the managed shuffle are the main levers. Here is how the model works.
Dataproc adds about $0.01 per vCPU-hour on top of the Compute Engine instances it runs, so the cluster costs more than the VMs alone. Ephemeral clusters, preemptible workers, and Dataproc Serverless are the main levers. Here is how to cut it.
Spanner bills compute as nodes (about $0.90/node/hour) or processing units (100 PU = a tenth of a node), plus $0.30 per GB-month of storage, backup, and egress. Processing units let you start small. Here is how to size and control the bill.
Firestore bills per document operation (reads about $0.06 per 100k, writes about $0.18, deletes about $0.02) plus storage and egress, with a daily free tier. Query patterns, not data size, drive the bill. Here is how to keep operations down.
Memorystore for Redis bills per GB of provisioned capacity by the hour, by tier (Basic or Standard with a replica). Capacity, not usage, sets the bill, so right-sizing the instance and choosing the tier are the levers. Here is how it prices.
Cloud NAT charges an hourly fee plus about $0.045 per GB processed, similar to AWS and Azure NAT. Traffic to Google APIs can skip it with Private Google Access, cutting the data line. Here is how it prices and how to reduce it.