Cloud Compute Cost Comparison

How to use: How This Cloud Cost Calculator Works

The calculator models your virtual machine cost as the sum of a CPU component and a memory component. For each provider, we apply a representative per-hour rate to both CPU and RAM, then scale by your monthly runtime.

At a high level, for any cloud provider the monthly compute cost is:

In plain language: you pay a certain hourly price for each vCPU and each GB of RAM, and then multiply by how long the VM runs each month.

This page uses the following representative average rates:

• AWS: $0.046 per vCPU-hour and $0.005 per GB-hour
• Azure: $0.050 per vCPU-hour and $0.006 per GB-hour
• Google Cloud: $0.031 per vCPU-hour and $0.004 per GB-hour

Rate Comparison: AWS vs Azure vs Google Cloud

The table below summarizes the hourly rates used in the calculator so you can quickly see how providers compare on CPU and memory pricing.

These are intentionally simple averages chosen to resemble mid-range instances in popular regions. Real-world pricing varies by instance family, region, operating system, and discount programs.

Interpreting the Results

After you enter your vCPU count, memory size, and hours per month, the calculator outputs an estimated monthly compute cost for each provider. Use these numbers as a directional comparison rather than a quote.

• Relative differences matter most: Look at which provider is lower or higher and by how much, rather than focusing on the exact dollar value.
• CPU-heavy vs memory-heavy: Workloads with many vCPUs but modest RAM will be driven mostly by the vCPU rate; in-memory databases or analytics nodes with large RAM but modest CPU will be more sensitive to GB-hour pricing.
• Usage pattern: Always-on production systems (close to 720 hours per month) magnify cost differences. Development and test workloads that run only during the day or on weekdays will show a smaller monthly gap.

Remember that compute is often only part of your total cloud bill. Storage, databases, managed services, egress bandwidth, and support plans can easily exceed VM charges depending on your architecture.

Worked Example: 4 vCPUs, 16 GB, 24/7

Suppose you run a web application that needs 4 vCPUs and 16 GB of memory, operating 24/7. That is roughly 720 hours per month.

Using the formula above, we first compute the hourly cost for each provider, then scale by 720 hours.

• AWS hourly cost: (4 × $0.046) + (16 × $0.005) = $0.184 + $0.080 = $0.264 per hour
• Azure hourly cost: (4 × $0.050) + (16 × $0.006) = $0.200 + $0.096 = $0.296 per hour
• Google Cloud hourly cost: (4 × $0.031) + (16 × $0.004) = $0.124 + $0.064 = $0.188 per hour

Multiply by 720 hours per month to get estimated monthly compute costs:

• AWS: $0.264 × 720 ≈ $190
• Azure: $0.296 × 720 ≈ $213
• Google Cloud: $0.188 × 720 ≈ $135

Your actual bill will differ, but this illustrates how a lower per-hour rate on CPU and memory can lead to significant monthly savings when workloads are always on.

Example Workload Patterns

To make the calculator more useful, think about how your workload behaves over time. Here are a few common scenarios and how they typically impact provider comparisons.

1. Always-On Database Server

A production database that must be available 24/7 is both CPU- and memory-intensive and usually runs close to 720 hours per month. In this case, even small rate differences compound over time. A provider with lower base compute pricing (often Google Cloud in this model) can yield noticeable savings, especially when combined with committed-use discounts.

2. Weekday-Only Development Environment

Development and test environments are often needed only during business hours. For example, running from 9:00–18:00, Monday through Friday, is roughly 9 hours × 5 days × 4.3 weeks ≈ 194 hours per month. If you plug the same vCPU and memory into the calculator but reduce hours from 720 to around 200, total spend drops dramatically. At that point, operational practices (shutting VMs down when not in use) often matter more than small rate differences.

3. Short-Lived Batch or ML Jobs

Batch workloads and machine learning training jobs may run at high intensity but for a limited number of hours. Here, your main trade-offs are between hourly price and performance. A provider with slightly higher hourly pricing but significantly faster hardware or better accelerators might still be cheaper in total if jobs complete faster.

Summary Comparison for a Sample Configuration

The table below summarizes the estimated monthly compute-only cost for the worked example (4 vCPUs, 16 GB, 720 hours). Use it as a reference point when experimenting with your own values.

When you change inputs in the calculator form, the relationships may shift, especially for configurations that are extremely CPU-heavy or memory-heavy.

Assumptions and Limitations

This tool is intentionally simplified. Keep these assumptions and caveats in mind when interpreting the results:

• Representative averages only: The vCPU-hour and GB-hour rates are rough averages for mid-range instances in common regions. Actual prices depend on instance family, size, OS, region, and generation.
• No discounts modeled: Reserved instances, savings plans, committed-use discounts, and sustained-use discounts are not included. These can reduce real costs significantly.
• Compute-only view: Storage (block and object), database services, load balancers, data transfer, and support are all excluded. In many architectures, these non-compute items are a large portion of the bill.
• Single-region assumption: Regional price differences are not modeled. You can approximate them by adjusting the implied rates in your own spreadsheet or by comparing against provider calculators in your target region.
• No performance differences: The tool assumes equivalent performance per vCPU across providers. In practice, CPU generations and networking speeds can differ, which may change the cost/performance balance.

For precise budgeting, always validate the outputs here against each provider’s official pricing documentation or calculator. Use this tool as a quick way to reason about orders of magnitude, trade-offs, and how workload shape (CPU, memory, hours) affects relative costs.