When evaluating public cloud providers, it is easy to get hung up on the differences. AWS, Microsoft Azure, and Google Cloud each have their own terminology, pricing structures, service catalogs, and purchasing options. But do these differences ultimately matter for most engineering teams?
Compute Options: The Core of Your Cloud Bill
Although comparable products exist across all three major cloud providers, meaningful differences do exist between specific offerings. The sheer volume of services available — AWS alone has published well over a hundred distinct services — makes a comprehensive side-by-side comparison impractical in a single article.
The most useful starting point is compute. Compute resources typically represent the largest portion of a company’s cloud spend, so understanding the similarities and differences here covers the core of most workloads. All three providers offer:
- Virtual machines (VMs): AWS EC2, Azure Virtual Machines, Google Compute Engine
- Managed Kubernetes: AWS EKS, Azure AKS, Google GKE
- Serverless / Function-as-a-Service: AWS Lambda, Azure Functions, Google Cloud Functions
- Managed containers: AWS Fargate / ECS, Azure Container Instances, Google Cloud Run
If your architecture depends heavily on a specific compute paradigm — for example, serverless or GPU-accelerated instances — you should do a detailed comparison of those specific offerings rather than relying on high-level equivalence.
Pricing: Similar Structures, Different Numbers
Pricing comparisons between cloud providers require careful attention because instance types are not always directly equivalent in CPU architecture, memory ratios, or network throughput. That said, for general-purpose workloads, you can often find comparable instance families across the three providers and compare their on-demand rates.
A few important nuances to keep in mind:
- Billing granularity: AWS and Google Cloud both bill per second (after a minimum), while Azure bills per minute. This matters for short-lived workloads.
- Windows licensing: AWS and Azure list separate, higher prices for Windows instances. Google Cloud adds a per-core license charge on top of the base instance cost.
- On-demand as the baseline: Published prices are almost always the most expensive option. Reservations, committed use, and spot pricing can substantially reduce real-world costs.
Always pull current pricing directly from each provider’s published price list, as rates change and vary by region.
Terminology: More Similar Than They Appear
One of the most confusing aspects of comparing cloud providers is terminology. Each provider has branded its services differently, which can make the landscape look more fragmented than it actually is. Once you map equivalent services, the functional overlap is significant. Examples:
| Category | AWS | Azure | Google Cloud |
|---|---|---|---|
| Object Storage | S3 | Blob Storage | Cloud Storage |
| Managed Relational DB | RDS | Azure SQL Database | Cloud SQL |
| Serverless Functions | Lambda | Azure Functions | Cloud Functions |
| Managed Kubernetes | EKS | AKS | GKE |
| CDN | CloudFront | Azure CDN | Cloud CDN |
| DNS | Route 53 | Azure DNS | Cloud DNS |
The naming differences reflect branding decisions more than substantive architectural differences. When evaluating a new provider, mapping its service catalog against one you already know is a productive first step.
Purchasing Options: How to Reduce On-Demand Costs
All three providers offer multiple mechanisms to reduce costs below on-demand prices. Understanding these options is often more impactful than choosing a provider based on list prices alone.
Reservations and Committed Use
Each provider offers a commitment-based discount for customers who agree to use a defined amount of compute capacity over one or three years:
- AWS Reserved Instances (RIs): Customers reserve a specific instance type in a specific region. AWS also offers Convertible Reserved Instances, which can be exchanged across instance families, operating systems, and sizes during the term — useful if your workload profile is likely to change.
- Azure Reserved VM Instances: Azure builds similar flexibility into its standard Reserved VM option, allowing instance size flexibility within the same family.
- Google Committed Use Discounts: Customers commit to a number of vCPUs and a memory allocation rather than a specific instance type, which provides more flexibility around the exact machine configuration used.
Cancellation policies differ meaningfully: AWS customers can resell unused reservations on the Reserved Instance Marketplace; Azure users pay an early termination fee; Google Cloud commitments cannot be cancelled early.
Spot and Preemptible Instances
All three providers sell spare, unused capacity at steep discounts, with the trade-off that these instances can be reclaimed with little notice when demand increases:
- AWS Spot Instances
- Azure Spot VMs (previously called Low-Priority VMs; Azure uses the term “eviction” rather than interruption)
- Google Preemptible VMs (and the newer Spot VMs)
This pricing model is well-suited for fault-tolerant, stateless, or batch workloads: CI/CD pipelines, rendering jobs, financial modeling, and large-scale data processing. It is not appropriate for stateful production services that require guaranteed availability.
Google Cloud Sustained Use Discounts
Google Cloud Platform offers an automatic discount that applies the longer a VM instance runs within a billing month — no reservation or upfront commitment required. The discount increases incrementally with usage. Note that Google’s published on-demand prices often already reflect the assumption of full-month usage with sustained use discounts applied, so direct comparisons with AWS or Azure on-demand prices can be misleading unless you account for this.
Enterprise Contracts
All three providers offer negotiated enterprise agreements for large customers who commit to significant levels of spend. Examples include AWS Enterprise Discount Programs (EDPs) and Azure Enterprise Agreements. Discounts under these agreements vary based on committed spend, contract length, and the customer’s relationship with the provider. These are not published and are negotiated individually.
The Perception Factor
Beyond technical and pricing considerations, teams have real perceptions about each platform that influence adoption:
- AWS is widely regarded as the most mature platform with the broadest service catalog and the largest talent pool in the market.
- Azure is a natural fit for organisations already invested in the Microsoft ecosystem (Active Directory, Office 365, Windows Server, .NET).
- Google Cloud is often associated with data analytics, machine learning infrastructure, and Kubernetes expertise — GKE remains widely considered the most polished managed Kubernetes offering.
Perceptions around ease of use tend to reflect familiarity more than objective usability. Engineers who learn one provider first will naturally find it easier, and any of the three can be learned to a productive level. Enterprise support quality has historically been seen as stronger on AWS and Azure, though Google Cloud has invested substantially in enterprise readiness and support over recent years.
Do the Differences Really Matter?
For the majority of standard workloads — web applications, APIs, containerised microservices, relational databases, object storage — the three major cloud providers are functionally equivalent. The differences in pricing, at scale, are often smaller than the differences in how efficiently your team manages and right-sizes resources on whichever platform you choose.
Where the differences do matter:
- Deep ecosystem integration: If your organisation runs heavily on Microsoft products, Azure’s native integrations reduce friction significantly.
- Specialist services: If a specific managed service (BigQuery, AWS SageMaker, Azure OpenAI Service) is central to your architecture, evaluate that service on its own merits rather than treating it as equivalent across providers.
- Regulatory and data residency requirements: Region availability and compliance certifications vary and may constrain your choice.
- Existing contracts and relationships: Enterprise agreements and existing spend commitments may make switching costly.
Designing for Multi-Cloud Reality
Most organisations of meaningful scale already use more than one cloud provider, either by design or through acquisition. The practical recommendation is to architect accordingly:
- Build applications to avoid tight vendor lock-in where the cost of doing so is reasonable.
- Use cloud-agnostic tooling — Terraform for infrastructure provisioning, Kubernetes for container orchestration, and open standards for data formats — where it does not add unnecessary complexity.
- Containerise workloads to improve portability across environments.
- Invest in your team’s ability to read and manage costs on whichever platform you use; cost management skill often delivers more value than provider selection.
If your organisation has already chosen a provider, or a business requirement drives you to a specific platform, the honest answer is: the differences are unlikely to be the determining factor in your project’s success. The more impactful variables — architecture quality, operational discipline, cost governance, and team expertise — are within your control regardless of which cloud you run on.