AWS vs Azure Pricing: A Complete Guide to Understanding Cloud Costs

When we talk about cloud pricing, it often feels like walking into a shop where nothing has a label. You know the products are powerful, but figuring out what you will actually pay takes patience and a sharp eye. That is exactly why comparing AWS and Azure pricing matters. Both platforms offer similar services, yet the way they charge for compute, storage, and support can shape your entire cloud budget.

As engineers, we have learned that cloud cost planning is not about guessing which provider is cheaper. It is about understanding how each platform structures its pricing, how you use your resources, and what you can optimise along the way. In this guide, we break everything down in simple terms so you can make confident, informed decisions without getting lost in technical noise.

Let us explore the pricing models, free tiers, cost-saving strategies, and the real differences that shape your cloud bill.

What pricing model does AWS use?

When we look at how AWS structures pricing, the core idea is simple: you pay only for what you use, with no long-term requirement unless you choose one. This flexibility is one reason many organisations prefer AWS early in their cloud journey.

AWS offers several pricing options:

• On-demand: You pay per second or per hour, depending on the service. There is no commitment, making it suitable for unpredictable workloads.
• Reserved Instances: Designed for consistent workloads over 1 or 3 years. Discounts can reach around 70–75% compared to on-demand pricing.
• Savings Plans: Instead of committing to a specific instance type, you commit to an hourly spend. Discounts are similar to Reserved Instances but with more flexibility across compute services.
• Spot Instances: Ideal for workloads that can handle interruptions. Spot Instances use spare AWS capacity and offer discounts of up to 90%.

With this broad mix of pricing choices, you can optimise for flexibility, stability, or maximum savings depending on your workload.

What pricing model does Azure use?

Azure follows a similar pricing philosophy while offering additional advantages for organisations already using Microsoft technologies. You pay only when resources are active or commit to longer terms to reduce cost.

Azure provides these main pricing models:

• Pay-as-you-go: You pay for active resources with no long-term contract. This works well for experimentation or constantly changing workloads.
• Reserved VM Instances: Like AWS, Azure offers 1- or 3-year reservations with discounts of up to about 72%.
• Spot or Preemptible VMs: Azure’s version of Spot Instances. These provide steep discounts but can be reclaimed at any time.
• Azure Hybrid Benefit: One of Azure’s strongest advantages. If you already own Windows Server or SQL Server licences, you can reuse them in Azure and reduce VM costs significantly.

These options offer strong flexibility, especially for Microsoft-centric environments.

AWS vs. Azure cloud pricing: free tiers

Both platforms offer free tiers to help you explore without immediate costs.

Azure provides free credits for new accounts, a set of services free for 12 months, and an always-free list with usage limits.

AWS also offers a 12-month free tier, including small compute, storage, and other basic services, along with an always-free tier.

These free tiers are ideal for learning, proof-of-concept projects, and lightweight development.

AWS vs. Azure pricing comparison: Instances and VMs

Compute resources often represent the largest portion of a cloud bill. Both clouds offer a wide range of compute families, general-purpose, compute-optimised, memory-optimised, storage-optimised, and GPU/HPC machines.

AWS has a very large instance catalogue, while Azure organises its VM series by workload type.

Even when instance sizes appear similar, the final cost depends on the pricing model you choose. On-demand offers flexibility, reservations reduce cost, and Spot/Preemptible options provide the highest discounts for interruptible workloads.

The more stable your workload and the better you use commitment-based discounts, the cheaper either platform becomes.

AWS vs. Azure pricing: support plans

Support costs matter when running production systems.

AWS offers multiple support tiers, from free documentation to premium plans with fast response times. Costs increase based on usage and support level.

Azure also provides several support tiers, and integration with existing Microsoft licensing can simplify enterprise support.

Including cost support planning gives you a more accurate total cloud cost.

Is Azure cheaper than AWS?

This is one of the most common questions, but the answer depends entirely on workload patterns and licensing needs.

Azure can be cheaper for environments using Microsoft products such as Windows Server or SQL Server. Azure Hybrid Benefit allows licence reuse, which significantly lowers VM costs.

AWS may be more cost-efficient for workloads that scale dynamically, change frequently, or require specialised compute types. Spot Instances and broad compute variety often provide strong value.

Rather than asking which cloud is cheaper overall, evaluate which cloud matches your workload behaviour.

Strategies to lower either AWS or Azure costs

To manage cloud costs effectively, consider these proven strategies:

• Right-size VMs and services to meet your actual performance needs.
• Use long-term commitments like Reserved Instances, Reserved VMs, or Savings Plans for stable workloads.
• Use Spot or Preemptible Instances for temporary or flexible tasks.
• Turn off or delete unused resources.
• Reuse licences where possible; Azure Hybrid Benefit is especially valuable.
• Use cost monitoring tools, dashboards, and budget alerts.

Applying these practices consistently makes cloud spending predictable and manageable.

Pricing factors for popular AWS and Azure services

Each cloud service has cost drivers:

• Compute: vCPUs, RAM, VM type, region, uptime, storage, licensing, and data transfer.
• Storage: Tier, data size, API calls, retrieval patterns, lifecycle rules, and egress.
• Databases: Instance class, IOPS, storage size, backups, high availability, and traffic.
• Serverless: Execution count, execution time, memory, and data movement.
• Networking: Cross-region traffic, internet egress, load balancers, NAT gateways.
• Containers: Node VM costs, cluster fees, storage, scaling, and internal traffic.
• Content delivery: Data transfer, geographic distribution, requests, and optional features.

Understanding these factors helps you design more cost-efficient architectures.

How to understand, control, and optimise cloud costs

A simple, effective approach includes:

• Audit your usage to see what runs continuously and what scales.
• Match the correct pricing model to your workload.
• Right-size compute and storage based on actual performance data.
• Use discounts and licensing benefits where available.
• Delete idle or unnecessary resources regularly.
• Use cost tools and alerts to stay within budget.

Review your architecture frequently to keep it efficient as workloads evolve.

Conclusion

In our experience, neither AWS nor Azure wins the pricing debate universally. Each platform becomes cost-effective only when paired with the right workload pattern and pricing model.

If you right-size resources, apply discounts correctly, and monitor usage consistently, both clouds can provide strong value. Instead of asking which cloud is cheaper overall, focus on which cloud best supports your technical goals and long-term growth.

This mindset will help you build cloud environments that stay efficient, scalable, and cost-optimised as your organisation grows.