Cloud Infrastructure AWS vs. GCP vs. Azure Cost Estimator
Model monthly virtual machine hosting budgets, storage costs, and network data egress fees side-by-side across three major cloud platforms.
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3,600 Instance Hours
| Cloud Provider | Compute Cost | Storage/Net Cost | Total Monthly |
|---|---|---|---|
| Amazon Web Services (AWS) | $360.00 | $90.00 | $450.00 |
| Google Cloud Platform (GCP) | $310.00 | $70.00 | $380.00 |
| Microsoft Azure | $335.00 | $85.00 | $420.00 |
The Complexities of Cloud Infrastructure Sizing
For cloud architects, DevOps engineers, and startup CTOs, sizing and budgeting infrastructure resources represents a significant operational challenge. Because the major cloud service providers (Amazon Web Services, Google Cloud Platform, and Microsoft Azure) monetize their virtual machines (VMs), persistent storage disks, and network data egress flows using distinct pricing algorithms, running side-by-side cost audits is a complex task. Our Cloud Infrastructure AWS vs. GCP vs. Azure Cost Estimator provides a side-by-side on-demand budget projection.
Key Cloud Pricing Cost Drivers
To estimate hosting budgets, teams analyze three core resource categories:
- Compute Instance Hours: The base cost of running virtual machines (VMs). Compute is charged per core-hour (vCPU) and per gigabyte of memory (RAM) allocated.
- SSD persistent Storage: Persistent blocks allocated to VMs (such as AWS EBS, GCP Persistent Disk, or Azure Managed Disks). Charged as a flat monthly rate per GB provisioned.
- Network Data Egress (Egress Fees): Outbound internet data transfer bandwidth. Providers charge a premium per gigabyte transferred outside their data center networks.
Strategies to Reduce Cloud Spend
- Commit to Savings Plans: If your compute usage is consistent, committing to 1-year or 3-year Reserved Instances (RIs) can reduce on-demand compute rates by up to **72%**.
- Leverage Spot / Preemptible VMs: For non-production queues, batch processing, or background workloads, utilizing spot instances can save up to **90%** compared to on-demand pricing.