Cloud Cost Estimator
Compare rough monthly VM costs across AWS, Azure and Google Cloud by selecting CPU, RAM, region and commitment type. Great for quick architecture cost comparisons and FinOps planning. Always verify with official pricing calculators before purchase.
What is a Cloud Cost Estimator?
A cloud cost estimator lets engineers quickly calculate the approximate monthly spend for common infrastructure resources — virtual machines, storage volumes, and data transfer — across AWS, Azure, and Google Cloud Platform, without needing to log into each provider's console or navigate their complex pricing pages. For DevOps teams, having a fast sanity-check number before provisioning new infrastructure is invaluable for avoiding budget surprises at month end.
Cloud pricing is notoriously opaque. On-demand rates vary by region, instance family, and operating system. Add in data egress charges, managed service markups, and the difference between reserved and spot pricing, and even experienced engineers find it difficult to predict a monthly bill. This tool provides a fast first-order estimate based on published list prices so you can compare options and identify the right starting point before diving into each provider's full pricing calculator.
When to Use This Tool
- Architecture decisions: Compare the cost of running the same workload on different instance types or across different cloud providers before committing to an architecture.
- Budget forecasting: Generate a rough monthly cost figure to include in a project proposal or engineering RFC when finance asks for an infrastructure estimate.
- Right-sizing reviews: Evaluate whether upgrading or downgrading an instance type is worth the performance trade-off by seeing the cost delta immediately.
- Multi-cloud analysis: Quickly identify whether a workload that is currently on AWS would be materially cheaper on GCP or Azure before investing in a migration assessment.
How It Works
The estimator applies publicly available list prices for common instance families and storage tiers from AWS, Azure, and GCP. You select your resource configuration — vCPUs, memory, storage, and monthly data transfer — and the tool multiplies hours-per-month by the per-hour rate to produce a monthly total. All calculations happen in the browser using current published rates; no API calls are made to the cloud providers. Because list prices change regularly and vary by region, treat the output as a directional estimate rather than a definitive quote.
Frequently Asked Questions
How accurate are these cloud cost estimates?
The estimates use publicly available list prices for rough comparison across providers. Actual costs can vary significantly based on reserved instances, savings plans, enterprise discounts, spot pricing, and regional differences. Data transfer costs in particular are highly dependent on your specific traffic patterns — egress to the internet, cross-region transfers, and CDN offload all affect the real bill. Always validate against the official AWS Pricing Calculator, Azure Pricing Calculator, or GCP Pricing Calculator before making purchasing decisions or committing to a budget.
Which cloud provider is cheapest?
The answer depends heavily on the workload, region, and commitment level. GCP tends to be slightly cheaper for general-purpose compute on an on-demand basis and offers sustained-use discounts automatically. AWS has the widest selection of instance types and the most mature spot market. Azure typically integrates best with Microsoft enterprise licensing and Active Directory environments, and hybrid benefits can dramatically reduce Windows Server costs. For most workloads the on-demand price difference between providers is less than 10%, meaning architectural and operational fit matter more than raw price.
How can I reduce my cloud compute costs?
The biggest savings come from commitment discounts: AWS Reserved Instances and Savings Plans, Azure Reserved VM Instances, and GCP Committed Use Discounts can reduce compute costs by 30–72% compared to on-demand pricing for workloads with predictable usage. Right-sizing instances to match actual CPU and memory utilisation — often revealed by enabling cloud-native monitoring and looking at p95 utilisation over 30 days — is another high-impact tactic. For fault-tolerant workloads such as batch processing, CI runners, or stateless microservices, spot and preemptible instances offer discounts of 60–90%. Finally, scheduling non-production environments to shut down outside business hours can cut idle compute spend by more than half.