AWS, Azure, or Google Cloud? A Cost Comparison to Help You Decide

Introduction

If you’re weighing a move to a public cloud environment or selecting the ideal provider for your next project, deciding among AWS, Azure, or Google Cloud Platform (GCP) can be challenging. Each platform offers flexible compute, storage, and networking services, along with core cloud benefits such as self-service provisioning, rapid scalability, and automated resource management.

Yet each provider also brings its own unique strengths and cost structures. Which one is right for you depends on your specific workload requirements, application demands, and overall organizational needs. By clarifying these factors in advance, you’ll be in a better position to navigate the cloud landscape.

This cloud pricing comparison focuses on how AWS, Azure, and Google Cloud each handle storage and compute pricing, highlighting the nuances that can influence your final bill and help you make an informed decision.

Key Advantages of AWS, Azure, and Google Cloud Platform

AWS

AWS often draws developers because of its expansive service lineup. From databases and analytics to IoT and security, AWS has a vast array of tools that cater to a variety of enterprise and startup use cases. Its strong market presence is no surprise, as AWS remains a top choice for organizations looking for a mature and feature-rich platform.

Azure

Some reports suggest that Azure has a slight edge in enterprise adoption compared to AWS.

This success can be attributed to Microsoft’s deep roots in the enterprise space and strong ties to other corporate solutions like Office 365 and Microsoft Teams. As a result, companies often opt for Azure to seamlessly integrate their productivity tools with a dependable cloud infrastructure.

Google Cloud Platform

While AWS and Azure both provide extensive portfolios of cloud services, GCP differentiates itself through Google’s powerhouse research and development, which has led to key open-source innovations like Kubernetes for container orchestration and Istio for service mesh—now industry standards. This strong focus on pioneering technologies makes GCP particularly appealing to startups and organizations that value cutting-edge tools and development practices.

Billing in AWS vs. Azure vs. Google Cloud Platform

All three major cloud providers—AWS, Azure, and Google Cloud—initially offered per-minute billing, and they have since adopted per-second billing for selected services. AWS was the first to roll out per-second billing in 2017, starting with Linux-based EC2 instances and EBS volumes. Over time, the policy has been extended to many other AWS services.

Under AWS’s approach, per-second billing usually requires at least 60 seconds of usage. Azure also supports per-second increments, though it primarily applies to container-based or specialized workloads, rather than being universally available across all service categories.

Google Cloud followed AWS in implementing per-second billing and broadened its coverage, so this pricing model now applies to most VM-based instances—not just Linux. As a result, if your workloads involve brief or frequently scaled processes, per-second billing can provide meaningful cost savings.

Cloud Storage Pricing Comparison

Let’s see how AWS, Azure, and Google Cloud Platform compare in us-west regions. These estimated costs apply to standard storage tiers; actual prices may shift based on data volume or applicable discounts.

All three providers cluster around a similar price range, with Google Cloud often showing a slightly lower base rate for standard storage in the us-west1 region and Azure appearing marginally higher than GCP but still below AWS in this comparison. However, keep in mind that data transfer charges, API request costs, and specialty tiers (e.g., cold or archive) can influence your total monthly bill.

It’s also wise to stay alert to any pricing changes, as ongoing factors like inflation and supply chain constraints can prompt cost adjustments from all providers.

Compute Pricing Comparison

Because compute services often represent the largest share of a cloud bill, they also provide significant opportunities for cost optimization. To illustrate pricing differences, we compared virtual machines across AWS, Azure, and Google Cloud in similar us-west regions.

Example Setup

• Regions: AWS (us-west-2), Azure (West US), Google Cloud (us-west1)
• Operating System: Linux
• vCPUs: 4

We selected both general purpose and compute-optimized instances with similar resources. (Note that some instance families may have slightly different RAM amounts.)

AWS vs. Azure vs. Google Cloud Platform: On-Demand Pricing Comparison

The tables below show approximate hourly On-Demand rates for our selected general purpose and compute optimized instances across AWS, Azure, and Google Cloud. These prices may vary depending on your specific availability zone and any active promotions, so always confirm with the providers’ official calculators.

General Purpose

Compute Optimized

Key Observations

• Azure tends to be the most expensive for the general purpose tier in this comparison, yet it appears more cost-effective for compute-optimized workloads.
• AWS offers competitive rates across both categories, with its Graviton2-based t4g.xlarge instance standing out for moderate workloads.
• Google Cloud has higher prices for its compute-optimized option, but c2-standard-4 also includes more RAM (16 GB) compared to Azure and AWS in this category—potentially benefiting memory-intensive tasks.

Always factor in performance benchmarks and workload profiles to ensure you’re getting both cost efficiency and the right resource fit for your applications.

AWS vs. Azure vs. Google Cloud: Discounted Pricing with a 1-Year Upfront Commitment

AWS, Azure, and Google Cloud each offer discounted pricing for customers willing to commit for at least one year. These options—Reserved Instances (AWS), Reserved VM Instances (Azure), and Committed Use Discounts (Google Cloud)—can significantly reduce hourly rates compared to On-Demand.

General Purpose

Compute Optimized

Key Observations

• AWS and Azure generally offer comparable discount percentages in these examples, but AWS may end up with a slightly lower final rate for general purpose instances.
• Google Cloud often advertises some of the highest percentage discounts, yet the c2-standard-4 instance can still be more expensive overall due to additional RAM and higher baseline costs.
• Even with a one-year commitment, actual savings will depend on how closely your usage aligns with the pledged resources; in some cases, spot/preemptible instances (if applicable to your workload) may result in even larger savings.

Always measure your real-world resource usage and performance demands before locking into a one-year plan. While these discounts can be substantial, under- or over-provisioning can reduce your overall benefits.

AWS vs. Azure vs. Google Cloud: Comparing Spot Instances and Preemptible VMs

Using spare capacity that cloud providers aren’t currently utilizing can lead to significant cost savings—up to 80% or more off On-Demand rates. AWS offers Spot Instances, while Google Cloud provides Preemptible VMs, each with potential variations in discounts. Azure also has spot pricing for certain VM families.

General Purpose

*Azure’s B-series doesn’t currently support spot, so A4 v2 is chosen for comparison.

Compute Optimized

Key Observations

• AWS tends not to have the deepest discounts in these examples, but offers stable spot pricing across a wide range of instance families.
• Azure can showcase very steep discounts for compute-optimized workloads (F4s v2), making it notably cost-effective if your application can handle preemption.
• Google Cloud’s Preemptible VMs can also be highly economical, though the base costs for c2-standard-4 remain higher due to extra RAM and performance features.

Remember that spot or preemptible capacity can be reclaimed by the provider at short notice. This makes them best suited for stateless or fault-tolerant workloads that can tolerate sudden interruptions (e.g., batch processing, CI/CD tasks, data analytics, or microservices designed for autoscaling). Always confirm real-time spot availability and pricing within your chosen region for the most accurate cost estimates.

How Cloudchipr Can Help with Multi-Cloud Management

Effective cloud resource management depends on the ability to balance performance, cost, and scalability across multiple platforms. Cloudchipr streamlines multi-cloud environments in AWS, Azure, and Google Cloud, enabling teams to reduce unnecessary spending, optimize resource usage, and maintain a stable infrastructure.

Key Features

• Automated Resource Management: Detect and eliminate idle or underutilized resources through no-code automation workflows, shrinking operational overhead while maximizing resource efficiency.
• Rightsizing Recommendations: Receive data-driven insights for instance sizes, storage tiers, and compute resources, ensuring you meet performance goals without overpaying.
• Commitments Tracking: Monitor Reserved Instances, Savings Plans, and Committed Use Discounts in one place to avoid gaps in utilization and prevent cost overruns.
• Live Usage & Management: Gain real-time visibility into cloud consumption and performance metrics across AWS, Azure, and GCP, facilitating better decision-making and swift troubleshooting.

Sign up for a 14-day free trial to experience the full range of Cloudchipr’s multi-cloud management, cost optimization, and automation capabilities—risk-free and with no long-term commitment.

Conclusion

When choosing a cloud provider, there’s no single “winner” that outperforms all others across every dimension. AWS, Azure, and Google Cloud each excel in specific areas:

• AWS offers a vast ecosystem of services, global reach, and mature tooling.
• Azure integrates seamlessly with Microsoft products and provides strong enterprise features.
• Google Cloud prioritizes innovation, open-source technologies, and robust analytics capabilities.

Yet, pricing can vary widely depending on the regions, instance families, and discount models you select. Beyond standard On-Demand pricing, it’s essential to consider discounts like Reserved Instances or Committed Use Discounts, and even Spot or Preemptible instances if your workloads can tolerate interruptions. Continually monitoring and optimizing these variables will help you avoid overspending and maintain performance.

For organizations aiming to leverage multiple providers, a multi-cloud strategy can combine the best features of each platform. Tools like Cloudchipr streamline this process by offering automated resource management, rightsizing recommendations, and real-time usage insights—enabling you to reduce costs, prevent misconfigurations, and simplify day-to-day operations across AWS, Azure, and GCP.

Whether you’re comparing AWS vs. Azure, AWS vs. GCP, or GCP vs. Azure, the key is to align your choice with your specific workloads, budget, and performance goals. With a proactive approach to cloud cost management, you’ll ensure an optimal balance between price and performance—no matter which provider(s) you choose.