A Guide to the multicloud strategies of AWS, Azure, and Google Cloud

The big three cloud providers — Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) — all offer the essentials of modern infrastructure, including virtual machines, storage, serverless computing, and managed databases.

But there are key differences in how their services are packaged, priced, and incorporated into an enterprise’s tech stack, whether standalone or as part of a multicloud strategy.

As IDC research VP Dave McCarthy puts it: “Every one of these clouds has a different personality.”

So how do they compare and which one is best for you? Let’s dive in.

What is AWS?

AWS is the most mature of the big three, having started out as an internal platform and launched publicly in 2006.

Originally, AWS’ strategy was straightforward, McCarthy says: Get customers to move their entire infrastructure to the AWS cloud. But Amazon eventually realized that not every workload can, or should, move.

“Latency requirements, data sovereignty laws, and the sheer inertia of legacy systems meant some things needed to stay on-premises,” says McCarthy. The company’s AWS Everywhere is an acknowledgment of this reality. “The strategy has shifted from ‘all-in on the cloud’ to ‘the cloud wherever you need it.’”

AWS Outposts is the physical manifestation of this strategy, he explains. It’s not just a server rack; it’s a fully managed piece of AWS infrastructure that runs native AWS services, APIs, and tools in a customer’s own data center. This allows for a consistent developer and IT experience, extending the AWS environment to on-prem workloads that can’t reside in a public region.

“It’s about bringing the cloud operating model to the customer, rather than forcing the customer to come to the cloud,” says McCarthy.

AWS’ core services

AWS has a number of core offerings that handle storage, compute, and infrastructure. Some of the most widely used include the following:

Amazon S3: A cloud object storage system that can store images, backups, large datasets, and more. Users interact with a simple API to control structure, storage behavior, and access policies, while S3 handles infrastructure, encryption, scaling, and redundancy.

Elastic Compute Cloud (EC2): Allows businesses to run virtual servers in the cloud. Users manage everything above the hardware layer, choose their operating system (OS), configure environments, install dependencies, and deploy applications. EC2 handles the rest.

Amazon RDS: Gives users the ability to launch a relational database with just an API call.

AWS Elastic Beanstalk: A platform-as-a-service (PaaS) offering that provisions servers, establishes load balancers, configures runtimes, and performs scaling duties. Users only need to provide their application code, which could be written in Java, Python, Node.js, or another language.

Amazon API Gateway: A fully managed service to create, deploy, and secure APIs.

Amazon WorkSpaces: A fully managed desktop-as-a-service (DaaS) platform that allows users to provision virtual desktops with storage, access policies, and customized compute.

What is Microsoft Azure?

Launched in 2008, Microsoft Azure takes a hybrid- and multicloud-native approach with Azure Arc, which extends Azure services and management to any infrastructure.

“Microsoft’s DNA has always been in the enterprise data center, so a hybrid approach is natural for them,” says McCarthy.

Arc extends Azure’s management plane, he says. The capability is unique in that it is designed to manage resources anywhere — on-premises, in other clouds, or at the edge — and projects non-Azure resources (servers, Kubernetes clusters, databases) into Azure as if they were native. This allows customers to use familiar services like Azure Policy, Azure Monitor, and Microsoft Defender for Cloud.

“It’s a bold move that decouples Azure services from the Azure cloud, making Azure the management hub for a customer’s entire, messy, heterogeneous environment,” says McCarthy.

Azure’s core services

Azure’s core offerings include the following:

Virtual machines (VMs): An infrastructure-as-a-service (IaaS) offering that provides scalable, on-demand computing resources hosted in Azure’s cloud. Users can select their OS, install software, configure networks and security, and deploy workloads just like they would on a physical server. Computational resources can be scaled up or down based on need.

Azure Blob Storage: A tool for unstructured data such as images, documents, or backups. Users create containers, then upload and access data to those containers, through the web or software development kits (SDKs).

Azure App Service: A fully managed PaaS platform that allows users to deploy apps and APIs from a codebase.

Azure Functions: A serverless tool where users can build apps based on small blocks of code.

Microsoft Power BI: A software-as-a-service (SaaS) business analytics platform that provides visuals and dashboards so enterprises can gain insights from their data.

What is Google Cloud Platform (GCP)?

GCP is the youngest of the big three hyperscalers, having launched in 2011. It takes an open, Kubernetes-centric strategy, with Anthos— essentially an enterprise-grade distribution of Kubernetes designed to run in a customer datacenter, on GCP itself, or on other clouds like AWS and Azure—at the center. McCarthy notes that Anthos is designed for consistent application deployment and management across environments. This differentiates GCP from AWS’s hardware-centric approach with Outposts, and Azure’s management-centric approach with Arc.

Google’s strategy with Anthos is rooted in its own history, McCarthy explains. “Google pioneered Kubernetes, and their driving principle is that open standards create a common, portable foundation for modern applications,” he says. “They’re betting that the future of enterprise IT is built on containers and microservices orchestrated by Kubernetes.”

By standardizing on Kubernetes, Anthos allows for application portability, McCarthy points out. Users can build an application once and deploy it consistently across any environment without needing to refactor in a “software-first, application-centric” vision for multicloud.

“Google’s pitch is: ‘Don’t focus on managing infrastructure; focus on modernizing and managing applications,’” McCarthy notes.

GCP’s core services

GCP’s core offerings include the following:

Compute Engine: Can quickly spin up VMs on Google infrastructure. Users choose their OS, set up CPU and memory, install packages, configure firewalls, and manage deployments as they would on physical machines.

Cloud Storage: An object storage platform for unstructured data that users can interact with via APIs or libraries.

App Engine: A fully-managed platform for building applications. Google provisions, sets up routing, scales, applies security policies and patches, and performs monitoring and load balancing.

Google Cloud database services: Firestore for NoSQL document storage, BigQuery for data warehousing, Cloud SQL for relational databases.

Google Kubernetes Engine (GKE): PaaS service allowing users to run containerized apps using the open-source Kubernetes framework. Users defined images, deployments, and rules, and GKE provisions, manages, patches, and balances loads. GKE is the foundation on which Anthos is built.

How AWS, Azure, and GCP handle cost management and optimization

All three big hyperscalers offer capabilities that help you manage and optimize costs, which should help you keep cloud spending under control.

Amazon’s AWS Budgets and AWS Cost Explorer help customers track spending and usage, while AWS Trusted Advisor provides tips on how to optimize AWS environments to save money and improve performance. (As McCarthy put it, the tips are like the old adage of “At the end of the day, turn the lights off.”)

Microsoft’s Cost Management platform helps customers monitor, allocate, and improve costs. Azure Advisor can also provide personalized recommendations around reducing costs.

Google offers Cloud Billing Reports to help users keep tabs on their cost trends and forecast future costs. The company’s Cost Management tool provides financial governance policies and permissions and intelligent recommendations.

McCarthy noted that third-party vendors offer cost management capabilities, too; the FinOps Foundation has also been championing open standards to define how cost data should be collected and presented, and cloud providers are beginning to take note.

Core security and governance features in AWS, Azure, and GCP

Security is one of the cornerstones of any cloud platform, and the big three providers all incorporate strict security controls. For instance, all of them encrypt data both at rest and as it moves across the cloud and your infrastructure, and all comply with top certifications and standards, including ISO 27001, HIPAA, FedRAMP, and GDPR. But each has its own suite of specific security tools as well.

AWS’ key security features

Identity access management (IAM) to secure users.

Virtual private cloud (VPC) for launching resources in isolated networks.

AWS web application firewall (WAF) and AWS Shield to protect against cyberattacks.

Amazon Inspector, which provides automated security assessments.

Azure’s key security features

Microsoft Defender for Cloud, a security posture management (CSPM) and cloud workload protection (CWP) tool providing threat protection, vulnerability management, and compliance monitoring across Azure, hybrid, and multicloud environments.

Azure Virtual Network, providing secure communication between cloud resources, on-premises networks, and the web.

Azure Security Center for advanced threat protection across hybrid cloud environments.

Azure Active Directory identity and access management.

GCP’s key security features

Google Unified Security, an AI-powered platform offering a scalable, searchable security data fabric.

Cloud IAM to control user access.

Google Cloud VPC with firewall rules and route controls.

Google Cloud Armor Network Security, providing network security to defend against attacks.

How AWS, Azure, and GCP provide visibility and management

All three platforms aim to provide a “single pane of glass” control panel, but their focus differs, McCarthy explains.

AWS uses AWS Systems Manager, allowing users to view and control infrastructure on AWS and on-premises. McCarthy says it’s strong on operational tasks like patch management and automation for EC2 instances and on-prem servers.

Microsoft’s Azure Arc is its “centerpiece,” projecting all resources into the Azure portal. This provides a “truly unified view,” McCarthy says, for applying governance, security, and monitoring across Azure, on-prem servers, and other clouds.

GCP Anthos offers a unified control plane to manage Kubernetes clusters and workloads. Its dashboard provides visibility into services, configurations, and traffic across all environments.

But truly seamless cross-cloud management remains a work in progress for all three providers. “The ‘single pane of glass’ often feels more like a ‘single pane of glass to look at other panes of glass,’” says McCarthy. “Deep, provider-specific integrations are often required, and abstracting away the underlying differences without losing functionality is the core challenge they are all still trying to perfect.”

Advantages of each cloud platform

Of course, each provider has advantages over others. Here are the strengths of each based on user and analyst sentiment.

Advantages of AWS

AWS is the most mature of the three, with an extensive service catalog covering a wide range of use cases, a strong developer and partner ecosystem, and integration with numerous third-party tools and APIs.

Users can pay for only the resources they use, making it an affordable option.

Strong global infrastructure, meaning users can access and store data with low latency.

User-friendly and easy to set up.

Provides highly-customizable and scalable environments.

Advantages of Microsoft Azure

Like AWS, Microsoft’s global network of data centers allows for easy access and low latency.

Can typically be cheaper for larger enterprises.

Considered to have the best hybrid cloud strategy.

Strong enterprise support.

Integrates existing Microsoft licenses to prevent additional costs.

A natural fit for existing Microsoft users.

Advantages of Google Cloud Platform

Strong support for Kubernetes, containers, and serverless computing.

Like AWS and Microsoft, has a global network of data centers supporting low latency.

Strong open-source support, as GCP participates extensively in the open-source community.

Pricing model that is considered more user-friendly and straightforward than its competitors.

Pushes strong into AI and ML, which can provide for more advanced capabilities.

A natural fit for companies already using Google Workspace and other Google tools.

When to use each cloud platform

So, with all that in mind, how can you decide which of the big three platforms is right for you?

Experts advise choosing AWS if you want a broad array of services, require the latency afforded by global infrastructure networks, flexibility to build more complex cloud environments and highly customized apps, and are looking for a wider variety of tools.

Azure is a good fit for Microsoft shops using tools like Microsoft 365, Active Directory, or Windows Server. Existing customers also get the benefit of better pricing. Experts say Azure presents the easiest migration path and is highly supportive of hybrid environments.

GCP is a good fit if an enterprise’s work is AI-driven and containerized. GCP is known for its breakthroughs in analytics, ML, automation, and open source; it is also considered developer-friendly, particularly for those already using Kubernetes, TensorFlow, or BigQuery. Overall, it is more specialized, and its offerings are considered thorough, even if it has fewer of them than AWS or Azure.

What’s in the future for cloud computing?

Looking ahead for the big three — as well as the many smaller, more specialized cloud computing providers —  enterprise leaders should keep abreast of several critical trends, including the folllowing:

Even greater abstraction — that is, users will see less and less of what’s going on behind the scenes — and serverless integrations across clouds.

Increased use of AI/ML in cloud operations: AI and ML are becoming integral to the cloud environment, quietly working in the background to help systems run smoothly, optimally, and securely. Expect agentic AI, which can work autonomously, to play an increasing role in cloud operations.

Edge computing: The internet of things (IoT), 5G networks, and AI PCs require data to be processed as close as possible to its source. Edge and cloud will increasingly optimize one another.