While both AWS and Azure offer standard billing approaches, there are significant differences between the two that you need to look out for. And comparing AWS and Azure is just the start: Even after you choose a provider, there’s still more billing complexity to contend with. That’s because the actual cost of running workloads in production rarely matches the numbers from pricing calculators. After all, cloud cost is deeply entangled with configuration choices, infrastructure management approaches, architecture, and usage patterns. Two teams with similar workloads can have drastically different bills based on how they scale, log events, store data, and route traffic.
In this guide, we’ll compare the key cost drivers across AWS and Azure—and then go beyond pricing sheets to explore the factors that determine your actual bill.
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Understanding the pricing models: AWS vs. Azure
At first glance, AWS and Azure look like they have really similar pricing models. Both offer consumption-based billing (pay-as-you-go), discounts for reserved capacity (like AWS Savings Plans and Azure Reservations), and cheaper compute for ephemeral workloads (via AWS Spot Instances or Azure Spot VMs). But once you start digging deeper into contracts and spend commitments, there are crucial differences.
Take billing granularity, for instance: Azure typically uses per-minute billing, and AWS supports per-hour or per-second billing (with a minimum billable duration of 60 seconds), depending on the service type. This difference might not sound significant, but it adds up over time, especially when it comes to short-lived workloads.
In terms of flexibility, discount structures also differ. AWS Savings Plans provide more flexibility across a wide range of compute services, instance families, sizes, and regions. This allows users to shift workloads without losing discount benefits. In contrast, Azure Reservations are more rigid—they lock a discounted rate to a single resource stock keeping unit (SKU) in one region for a 1- to 3-year term. Still, Azure provides a hybrid benefits model that can be stacked with Reserved Instance benefits to save up to 80% for Windows Server.
The bottom line? Managing costs all depends on what exactly your teams need and the engineering effort they’re willing to put into cost mitigation.
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Compute cost comparison for commonly used services
Before diving into the pricing options, it’s important to note that most AWS and Azure prices fluctuate based on the deployed region. Only a handful of global or edge-based services maintain constant pricing worldwide.
AWS EC2 vs. Azure Virtual Machines
AWS and Azure savings opportunities: Both providers lower costs by roughly 40 to 70 percent when you commit to 1- or 3-year terms, and they offer even steeper reductions through spot instances.
Overall comparison: AWS offers greater billing flexibility by combining per-second on-demand billing with size-flexible Reserved Instances, cloud cost savings plans, and Spot capacity, which lets teams match costs to almost any workload. But as we’ve mentioned, Azure can also be cost-effective for Windows workloads because customers can add Azure Hybrid Benefit, a program that lets you reuse existing Windows Server or SQL Server licenses in the cloud.
AWS Lambda vs. Azure Functions
FaaS overall costs: When it comes to function as a service (FaaS), both AWS and Azure are considered dirt cheap and mostly identical.
Pricing for instances: Both AWS Lambda and Azure Functions are charged per number of requests and compute time (measured in GB-seconds). AWS provides more flexible memory configurations and better price-performance for ARM-based Graviton2 functions, while Azure’s Premium Plan reduces cold-start delays but carries a higher base cost.
Long-term discounts: With both providers, you can save up to 17% by committing to a 1- or 3-year term.
Overall comparison: In general, AWS offers more granular tuning, while Azure provides better hybrid deployment support.
Amazon EKS vs. Azure Kubernetes Service (AKS)
Both Amazon EKS and Azure AKS offer managed Kubernetes with control plane charges based on support level:
EKS Auto Mode: Offers more hands-off scaling with EC2-based pricing
Azure’s AKS Automatic (in preview): Simplifies cluster management, particularly for Windows-heavy environments
Overall comparison: Azure offers more generous free-tier options, while AWS supports more fine-grained scaling.
Amazon S3 vs. Azure Blob Storage
Amazon S3 and Azure Blob Storage are designed to store unstructured data like backups, media files, emails, and logs in a flat data environment with all associated metadata. These object storage solutions are highly scalable and cost-effective for large volumes of data.
How AWS and Azure storage costs are calculated: Calculations depend on various components like data volume, data transfer, request and data retrieval, and data redundancy configurations. When it comes to data transfer, moving data from storage to the internet / other regions results in additional egress charges. It’s a hidden cost driver that makes overall storage expenses harder to predict. (More on this later!)
Overall comparison: Amazon S3 offers a flexible, tiered storage system designed for everything from active workloads to deep archive. S3 Intelligent Tiering enables automatic cost optimization by moving objects between tiers based on access frequency. However, S3 doesn’t have reserved capacity plans, which are available on Azure Blob Storage to lower data storage costs.
Amazon EBS vs. Azure Managed Disks
Block storage architecture divides data into blocks of fixed sizes and stores them as individual units with unique identifiers. Compared to object storage, metadata in block storage is limited, resulting in less searchability, and it requires more budget. The upsides? Block storage is highly efficient, with low latency and rapid data transfer rates, making it ideal for mission-critical workloads.
Amazon EBS offers granular volume types tailored to workload profiles. It decouples performance from capacity, allowing you to configure IOPS and throughput.
Azure managed disks have unique disk reservation discounts for a 1-year commitment in premium SSD capacity.
Overall comparison: Both services charge based on provisioned capacity (not usage), so right-sizing is essential to eliminate cloud waste.
Real-world cost factors beyond pricing sheets
It's hard to determine what a final bill is going to look like just from the pricing tables, especially because of these hidden expenses that often go unnoticed:
Overprovisioned resources: Provisioning large instances “just in case” or setting the autoscaling thresholds too high can unnecessarily increase spend. Right-sizing compute and storage to match demand prevents resource underutilization.
Data egress fees: Egress is when data leaves the cloud environment to go to the public internet, another region, or another availability zone (AZ) within the same region. Even though inbound data transfer is typically free in both AWS and Azure, outbound egress is billed almost always. This is one of the most unpredictable cost factors in cloud workloads, as the rates can vary widely by geography and service.
For example, transferring data between regions to maintain replication or disaster recovery can quickly add up charges bi-directionally. Also, internet egress like serving files to global end users and internal traffic like cross-AZ microservice communication can silently accumulate substantial fees.
Network Address Translation (NAT) gateway costs: A NAT gateway lets resources in a private subnet initiate outbound connections to the internet or other services while preventing unsolicited inbound traffic (ingress). But it easily becomes a hidden cost driver because it charges a per-GB processing fee for all the traffic passing through, in addition to a flat hourly fee for the provisioned gateway availability.
Both AWS and Azure follow similar pricing approaches, and as these charges stack with standard region-specific data transfer costs, it can significantly inflate cloud bills. Consider using VPC endpoints, minimizing unnecessary internet-bound traffic, and consolidating traffic through fewer gateways when possible to cut down the costs.
Region and availability zone selection: Identical services have different prices across different regions and zones due to infrastructure and operational costs. Also, keep in mind that distributed architectures can multiply network transfer charges because cross-AZ traffic is billed separately.
Idle workloads and orphaned assets: Having idle workloads means you’re paying for uptime you don’t use. The same is applicable if there are unused services still running.
Misconfigured pricing models: Not taking advantage of cloud cost savings plans and optimum pricing models leads to missed discounts. On the other hand, committing to the wrong plans and reservations can result in underutilization and sunk costs.
Poor tagging and ownership: Assigning metadata stating ownership and environment to resources in the form of tags can improve accountability. Otherwise, it’s harder to trace cost to workloads, especially when there are many.
One important takeaway? Issues rarely happen in isolation. An overprovisioned database might also be exposed to the internet, storing sensitive data, and running on an unpatched image—making it not just a cost issue, but a risk one too. That’s why teams need cost visibility enriched with configuration and security context—so they can optimize what actually matters.
Tools for estimating and managing costs
Between AWS and Azure, there are several provider-specific cloud spend management tools that should be on your radar.
AWS cost management tools
Cost Explorer: Allows you to visualize usage patterns, filter and group data, forecast cost and usage, and generate custom reports
Compute Optimizer: Provides AI- and ML-based analytics for cloud cost optimization, resource right-sizing, and cleaning up unused resources
Trusted Advisor: Offers real-time checks for cost, performance, and security
Instance Scheduler on AWS: Lets you automate instance starting and stopping for EC2 and RDS and adjusts the desired capacity of EC2 Auto Scaling groups
Billing Conductor: Facilitates customizable pricing and cost visibility
Pricing Calculator: Estimates workload costs based on historical usage
Azure cost management tools
Microsoft Cost Management: Offers budget tracking, detailed cost and usage reporting, and AI-driven recommendations to eliminate idle resources and right-size workloads
Azure Migrate: Provides tools for planning migrations and estimating future cloud spend
Azure Advisor: Gives you actionable insights for cost, performance, and security
Pricing Calculator: Helps you estimate workload costs
A word about cross-platform and engineering-centric cloud cost management tools
Today, many organizations are operating their workloads across multiple clouds and in hybrid environments, making visibility and governance more challenging. Beyond the vendor dashboards we’ve discussed, managing costs requires specialized tools to automate cross-platform cost visualization and optimization.
Open-source tools can help track spending at a granular level. The drawback? Most tools focus on a single dimension (e.g., OpenCost tracks only Kubernetes). In complex environments, it’s important to have a cloud cost management and optimization solution like Wiz Cloud Cost that combines cost intelligence with cloud architecture, usage, and security posture context in one unified view.
Wiz: A single plane of glass unifying cost, configuration, usage, and security posture context
Most of the time, you won’t see the most significant cloud cost drivers in pricing calculators or documentation. That’s because cost effectiveness depends on aligning the right service choice (like reserved instances for predictable workloads or spot capacity for bursty tasks) with clear visibility into how they’re configured and used. Without a consolidated view, hidden costs from idle workloads, misconfigured storage, and uncontrolled egress can quickly overwhelm even well-planned budgets.
Enter Wiz. Wiz provides a unified perspective with end-to-end cloud visibility across containers, virtual machines, and serverless environments. For example, the Wiz cost optimization framework contains cloud configuration rules that identify EKS clusters running on extended support versions. These insights help teams identify workloads eligible for cost optimization and take action proactively. Better yet? Wiz continues to expand its capabilities to help organizations secure and optimize their cloud environments while keeping an eye on spend.
Ready to see for yourself? Schedule a demo today to learn more about how Wiz can help control costs while protecting everything you build and run in the cloud!
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