Cloud service costs often grow faster than your business if you don’t understand cloud pricing. This article will help you learn how to plan your GCP budget, optimize expenses, and save up to 40%.
As a Google Cloud partner, Elcore Cloud explains the basics of pricing, answers key questions, and offers tools and credits to save on your projects.
Research by Gartner shows that companies overpay 20–40% for cloud services due to poor planning (source: Gartner, “How to Optimize Cloud Costs,” 2023). Google Cloud pricing is flexible but complex because of models like Pay-as-You-Go, Commitment Use discount, and Spot VM. For finance and IT directors, understanding GCP cost distribution is a way to save on projects such as e-commerce or AI analytics.
Google Cloud offers several flexible payment schemes — from Pay-as-You-Go to Spot VM. Understanding these models helps avoid overspending and optimize costs.
You pay only for the resources you use. For example, in Compute Engine the price depends on the number of vCPUs and memory, while in BigQuery it depends on the amount of data processed.
A streaming service uses Cloud Storage with Pay-as-You-Go for video storage. During peak demand (e.g., the release of a new series), the service scales automatically, saving 25% compared to fixed infrastructure (source: Google Cloud Blog, “Media Streaming Case Study,” 2024).
A SaaS company tests Vertex AI for a chatbot, paying only for model training with Pay-as-You-Go. This reduced costs by 30% compared to renting servers (source: Google Cloud Customer Stories, “SaaS AI Adoption,” 2023).
Reserve resources for 1–3 years and save up to 57%. Ideal for stable projects with predictable workloads.
A bank reserved Compute Engine for processing its customer database with a 3-year contract, cutting costs by 40% compared to Pay-as-You-Go (source: Google Cloud Financial Services, “Banking Case Study,” 2024).
An e-commerce platform applied discounts for App Engine to support stable website traffic during seasonal sales, saving 30% (source: Google Cloud Retail, “E-commerce Scaling,” 2023).
Spot VMs (or Spot Virtual Machines) are virtual machines with deep discounts (up to 91%) that Google Cloud offers for non-critical, interruptible tasks. They use excess GCP compute resources, which may be temporarily stopped if Google needs them for other customers.
Spot VMs are ideal for tasks that don’t require constant availability, such as batch data processing or testing. However, if the task is critical, it’s better to choose standard VMs to avoid interruptions.
A clinic uses Spot VMs to process medical images in BigQuery, running analytics at night when demand is lower. This saved 70% compared to standard VMs (source: Google Cloud Healthcare, “Data Analytics Case,” 2024).
A company applies Spot VMs for Cloud Functions to forecast delivery routes. Tasks run in the background, reducing costs by 60% (source: Google Cloud Logistics, “Supply Chain Optimization,” 2023).
GCP offers $300 free credit for new users, Spot VMs with up to 91% savings, and cheaper Vertex AI compared to AWS SageMaker or Azure Machine Learning. For example, a media company migrated from AWS Redshift to BigQuery using BigQuery Data Transfer Service, saving 25% thanks to more efficient data processing. Multi-cloud with Anthos allows combining GCP, AWS, and Azure through Kubernetes, ensuring flexibility (source: Google Cloud Case Study, “Media Migration,” 2024).
Service |
GCP |
AWS |
Asure |
Compute (VM) |
Compute Engine: $0.031/hr for n2-standard-2, Spot VM up to 91% off |
EC2: $0.046/hr for t3.medium, Spot Instances up to 90% off |
Azure VM: $0.041/hr for D2s v5, Spot VM up to 90% off |
Data Storage |
Cloud Storage: $0.02/GB (Standard), regional discounts |
S3: $0.023/GB (Standard), volume discounts |
Blob Storage: $0.018/GB (Hot), reservation discounts |
Data Analytics |
BigQuery: $6/TB processed, optimization via slots |
Redshift: $0.25/hr for dc2.large, harder to scale |
Synapse Analytics: $1.20/TB, more flexible queries but higher base price |
AI/ML |
Vertex AI: $0.03/hr training, cheaper for small models |
SageMaker: $0.042/hr for ml.t3.medium, more expensive for custom models |
Azure ML: $0.04/hr training, more expensive for large models |
Free Credit |
$300 for new users |
$100 for new users |
$200 for new users |
Note: Prices are approximate (as of 2025, region us-central1 for GCP, us-east-1 for AWS, West US for Azure). GCP excels in analytics (BigQuery) and AI (Vertex AI), AWS in EC2 flexibility, Azure in Microsoft ecosystem integration.
Use Spot VMs
Apply for non-critical tasks such as batch processing in Cloud Functions or analytics in BigQuery. A logistics company cut costs by 50% using Spot VMs for route forecasting (source: Google Cloud Logistics, “Cost Optimization,” 2023).
Apply commitment discounts
Reserve Compute Engine or BigQuery slots for stable workloads, such as databases or web apps, saving up to 57%.
Use Cloud Cost Management Recommender
Identify inefficient resources (e.g., unused VMs) and get recommendations for turning them off or scaling them.
Optimize BigQuery queries
Use BigQuery Query Optimization Tool to reduce data scans, lowering analytics costs.
Choose cheaper regions
For example, us-central1 is cheaper than europe-west4, saving up to 20% on Compute Engine or Cloud Storage.
GCP Billing Reports provide detailed analytics by projects, services, and regions, while Cloud Cost Management Recommender suggests optimization actions (e.g., removing inactive VMs). A retailer used Billing Reports together with BigQuery for cost analysis, optimizing queries and cutting costs by 20% (source: Google Cloud Retail, “Cost Transparency,” 2024).
Over-provisioning resources (e.g., VMs with too many vCPUs) or inefficient BigQuery queries (e.g., scanning unnecessary data) increases costs. A SaaS company used the BigQuery Query Optimization Tool to analyze queries, reducing scanned data and saving 30% (source: Google Cloud Customer Stories, “SaaS Optimization,” 2023).
Anthos provides unified management for GCP, AWS, and Azure through Kubernetes and GKE (Google Kubernetes Engine). For example, a financial firm uses Anthos for hybrid databases, distributing workloads between GCP and Azure with Cloud Run for containers, saving 25% thanks to flexible scaling (source: Google Cloud Financial Services, “Hybrid Cloud Case,” 2024).
A retail company was overpaying for Compute Engine and BigQuery. Elcore Cloud:
identified excess VMs through GCP Billing Reports and Cloud Cost Management Recommender
moved analytics to Spot VMs in BigQuery, saving 60%
applied commitment discounts for App Engine, reducing costs by 30%
Google Cloud Pricing Calculator is a tool for quick GCP cost estimates. How to use it:
Open the calculator and select needed services (e.g., Compute Engine, BigQuery, Vertex AI).
Enter parameters: number of vCPUs, memory size, region, or data volume.
Add discounts: Spot VMs (up to 91% savings) or commitment discounts (up to 57%).
Review the estimate with cost breakdown and recommendations.
Understanding Google Cloud pricing is the key to saving. Elcore Cloud will help you optimize expenses using GCP resource distribution and additional Google credits.
