Azure vs AWS vs Google Cloud: How The Big Three Compare In 2026
Dominic Tarn
Head Writer - ReVerb
10.23.2026
Meta description: Azure vs AWS vs Google Cloud in 2026: market share, regions, services, discounts, Microsoft licensing and egress fees, plus when each cloud is the best fit.
In the Azure vs AWS question, AWS is still the largest and broadest cloud, Azure is usually the better fit for organizations already running Windows Server, SQL Server, Microsoft 365 and Microsoft Entra ID, and Google Cloud is strongest for analytics, Kubernetes and AI-heavy workloads. None of the three is objectively best. The right choice depends on your existing licenses, your team’s skills, where your data lives and which managed services you plan to build on.
This guide to Azure vs AWS vs Google Cloud: How The Big Three Compare In 2026 walks through market position, global footprint, a service-by-service mapping, pricing and discount models, licensing, egress fees, enterprise buying, and practical guidance on when to choose each. It is written for IT leaders, finance teams and engineering managers who need a decision they can defend, not a feature dump.
Azure Hybrid Benefit, free ESUs for eligible products on Azure
License included or BYOL with restrictions
License included or BYOL with restrictions
Identity integration
Native Microsoft Entra ID
IAM plus federation to Entra ID or other IdPs
Cloud Identity plus federation
Market Position In 2026
According to Synergy Research Group’s Q2 2026 report, published July 30, 2026, enterprise spending on cloud infrastructure services reached about $143 billion for the quarter, growing 43% year over year, the fastest rate Synergy has recorded in eight years. Amazon held 28% of the worldwide market, Microsoft 20% and Google 15%.
Synergy also noted that Microsoft and Google are growing faster than Amazon, which continues a multi-year trend of the gap narrowing. Much of the recent growth is tied to generative AI demand, which favors providers with large GPU capacity and strong model partnerships.
Market share matters for a buyer in two practical ways: the size of the talent pool and the depth of the partner and software ecosystem. All three are large enough that you will not struggle to find tooling or consultants. The bigger differentiator is usually how well a platform fits what you already own.
Regions And Global Footprint
Each provider counts its infrastructure differently, so raw region numbers are not a like-for-like comparison. As of October 2026, the official pages state:
Azure: more than 80 regions and more than 500 datacenters, according to the Azure global infrastructure page. Not every Azure region offers Availability Zones, so check zone support for the specific regions you plan to use.
AWS: 39 geographic regions and 124 Availability Zones, with two more regions (Saudi Arabia and Chile) and seven more zones announced, per the AWS global infrastructure page.
Google Cloud: 43 regions and 130 zones, plus more than 200 network edge locations, per the Google Cloud locations page.
For most buyers the question is narrower: does the provider have a region, ideally with zone redundancy, in the countries where your data must reside, and does that region offer the specific services (GPU instances, managed databases, AI models) you need? Service availability varies by region on all three clouds, and new AI capacity often lands in a handful of regions first.
Core Service Mapping
Most services have a direct equivalent on each platform. The names differ more than the capabilities, at least at the infrastructure layer. The table below maps the services most teams evaluate first.
Category
Microsoft Azure
AWS
Google Cloud
Virtual machines
Azure Virtual Machines
Amazon EC2
Compute Engine
Managed Kubernetes
Azure Kubernetes Service (AKS)
Amazon EKS
Google Kubernetes Engine (GKE)
Serverless containers
Azure Container Apps
AWS Fargate, AWS App Runner
Cloud Run
Functions
Azure Functions
AWS Lambda
Cloud Run functions
Object storage
Azure Blob Storage
Amazon S3
Cloud Storage
Managed relational databases
Azure SQL Database, Azure SQL Managed Instance, Azure Database for PostgreSQL and MySQL
Amazon RDS, Amazon Aurora
Cloud SQL, AlloyDB, Spanner
NoSQL
Azure Cosmos DB
Amazon DynamoDB
Firestore, Bigtable
Data warehouse and analytics
Microsoft Fabric, Azure Synapse Analytics
Amazon Redshift
BigQuery
AI and model platform
Microsoft Foundry
Amazon Bedrock, Amazon SageMaker
Gemini Enterprise Agent Platform
Identity
Microsoft Entra ID
AWS IAM, IAM Identity Center
Cloud Identity, Cloud IAM
Virtual desktops
Azure Virtual Desktop
Amazon WorkSpaces
Partner solutions
Compute, Containers And Serverless
At the infrastructure layer the three are close. AWS has the widest range of instance families and the longest track record with serverless through Lambda. Azure’s strength is running Windows and .NET workloads with first-party support, and AKS integrates directly with Entra ID for cluster access. Google created Kubernetes, and GKE is often cited by platform teams as the most polished managed Kubernetes experience; Cloud Run is a simple way to run containers without managing clusters.
Data And Analytics
This is where the platforms diverge most. Google’s BigQuery is a serverless warehouse that many analytics teams choose Google Cloud for in the first place. Microsoft has consolidated much of its analytics stack into Microsoft Fabric, which combines data engineering, warehousing, real-time analytics and Power BI in one SaaS product with OneLake storage. AWS offers Redshift alongside a broad set of separate services (Glue, EMR, Athena) that give more control but require more assembly. If your reporting already runs on Power BI, Fabric shortens the path considerably.
AI Platforms: Microsoft Foundry, Amazon Bedrock And Gemini Enterprise Agent Platform
All three now run multi-model platforms for building generative AI applications and agents, and the product names have changed repeatedly:
Microsoft Foundry is the current name for what was Azure AI Studio and then Azure AI Foundry; the former Azure AI Services are now called Foundry Tools. Microsoft Learn describes access to models from Microsoft, OpenAI, Anthropic, Meta and others. Its main draw is first-party access to OpenAI models inside Azure’s security and compliance boundary.
Amazon Bedrock offers models from a long list of providers, including Amazon, Anthropic, Meta, Mistral AI, Cohere, OpenAI and others, according to the Bedrock model documentation. Bedrock AgentCore adds tooling to deploy, connect and monitor agents.
Gemini Enterprise Agent Platform is the evolution of Vertex AI announced in 2026. It centers on Google’s Gemini models, with a Model Garden that also includes open models such as Gemma and third-party models including Anthropic’s Claude family.
Model choice is now broad on every platform, so the deciding factors tend to be where your data already lives, which identity and governance controls you need, and whether a specific model (OpenAI’s on Azure, Gemini on Google Cloud) is central to your roadmap. For a closer look at the Microsoft side, a dedicated Azure OpenAI vs OpenAI comparison is the logical next read.
Pricing Models And Discounts
All three use pay-as-you-go list pricing with discounts for commitment. Comparing list prices instance by instance rarely settles anything, because real costs depend on commitments, licensing, negotiated agreements and architecture. We do not quote unit prices here; check each provider’s calculator for your regions and instance types.
Azure: Reservations And Savings Plan For Compute
Azure Reservations lock in a specific VM family, size and region for one or three years. The Azure savings plan for compute instead commits you to an hourly spend for one or three years and applies across regions, instance series and operating systems. Microsoft says the savings plan can cut up to 65% off pay-as-you-go prices for select compute services, with typical savings ranging from 11% to 65%. Reservations and the savings plan can be combined, and the plan cannot be cancelled once purchased. Microsoft also sells a one-year savings plan for select database services, which it says saves up to 35%.
AWS: Reserved Instances And Savings Plans
AWS offers Reserved Instances and four types of Savings Plans: Compute, EC2 Instance, Database and Amazon SageMaker. Most run for one or three years with no upfront, partial upfront or all upfront payment; Database Savings Plans are one year only. AWS advertises savings of up to 72% compared with On-Demand pricing. Spot Instances add deep discounts for interruptible workloads.
Google Cloud: Sustained Use And Committed Use Discounts
Google applies sustained use discounts automatically to eligible Compute Engine resources that run more than 25% of a month, reaching up to a 30% net discount for resources that run the whole month. No contract is needed, though not every machine type qualifies. On top of that, committed use discounts come in two forms: resource-based commitments for specific Compute Engine resources in a region, and spend-based commitments that apply across services such as Compute Engine, GKE, Cloud Run, BigQuery and Cloud SQL.
The practical takeaway: buy commitments only for steady baseline usage, keep spiky workloads on demand, and revisit coverage every quarter. On Azure, tagging, budgets and policy from day one make it much easier to see which usage is truly steady enough to commit.
Microsoft Licensing Advantages On Azure
For organizations with a large Microsoft estate, licensing often matters more than compute pricing in the AWS vs Azure decision. Three mechanisms drive most of the difference.
Azure Hybrid Benefit
Azure Hybrid Benefit lets you apply existing Windows Server Datacenter and Standard licenses and SQL Server Enterprise and Standard licenses with active Software Assurance (or qualifying subscription licenses) to Azure, so you stop paying for the license portion again. It also covers Red Hat Enterprise Linux and SUSE Linux Enterprise Server subscriptions. Eligible services include Azure VMs, Azure SQL Database (vCore tiers), Azure SQL Managed Instance, AKS, Azure VMware Solution and Azure Local. Microsoft claims savings of up to 80% versus pay-as-you-go for Windows Server when combined with reservations, but actual savings vary by region and workload.
Free Extended Security Updates
Microsoft has a long pattern of giving Extended Security Updates at no extra charge to end-of-support products running in Azure, while customers on other clouds or on-premises pay for them. Microsoft’s lifecycle FAQ describes free ESUs for Windows Server 2012 and 2012 R2 on Azure VMs, Azure Dedicated Host and Azure VMware Solution, with no Software Assurance required. That program has now run its course: the third and final ESU year ended on October 13, 2026.
The benefit is still live for SQL Server 2014. Microsoft’s SQL Server ESU FAQ says SQL Server 2014 instances on Azure virtual machines get ESUs for free until they end on July 8, 2027. Next in line is Windows Server 2016, whose extended support ends on January 12, 2027; Microsoft has announced ESUs for it, so confirm the current terms for Azure and for other environments before you plan around them. If you still run older Microsoft server software, ESU costs belong in your cloud comparison.
Bring-Your-Own-License Limits On Other Clouds
Microsoft’s licensing terms treat Amazon, Google and Alibaba as “Listed Providers,” which restricts how customers can bring certain Microsoft licenses to those clouds, particularly to dedicated hosts. You can still run Windows Server and SQL Server on AWS and Google Cloud with license-included pricing, and some products can move with Software Assurance through License Mobility. The rules are detailed and change over time, so have a licensing specialist review your agreements before you model costs.
Microsoft 365 And Entra ID Integration
If your users already sign in with Microsoft Entra ID for Microsoft 365, Azure uses the same identity directory, conditional access policies and governance controls with no extra federation layer. AWS and Google Cloud both federate with Entra ID well, but it is one more integration to configure and maintain. Security tooling such as Microsoft Defender for Cloud and Microsoft Sentinel can monitor all three clouds, though they are most tightly integrated with Azure.
Egress Fees And Data Transfer
All three providers charge little or nothing for data coming in and charge for data going out to the internet beyond a free allowance. As of October 2026, Azure’s bandwidth pricing page lists the first 100 GB per month of internet egress as free, and AWS states that customers get 100 GB of free data transfer out to the internet each month, aggregated across services and regions. Google Cloud’s network pricing varies by tier and destination, so check its calculator.
In 2024, Google, AWS and Microsoft each announced that they would waive egress fees for customers moving data out to leave their platform, ahead of the EU Data Act. The conditions differ: Petri reported that Azure requires a support request, completion within 60 days and cancellation of the linked subscriptions, while Google’s waiver also requires closing the account. These waivers do not cover the ongoing egress of a normal multicloud setup, which remains a real line item for data-heavy architectures. Cross-region and cross-zone traffic can also add up, so model network flows before you commit to an architecture.
Enterprise Agreements And Marketplaces
Larger buyers rarely pay list price. Each provider offers negotiated commitment contracts: Microsoft through the Microsoft Azure Consumption Commitment (MACC) and enterprise agreements, AWS through private pricing agreements, and Google through committed spend agreements. Smaller organizations often buy Azure through a Cloud Solution Provider (CSP) partner, which can bundle billing, support and management; our explainer on what Microsoft CSP is covers how that model works.
Marketplaces matter because third-party software bought through them can draw down your commitment. According to Microsoft Learn, eligible purchases on Microsoft Marketplace (which replaced Azure Marketplace and AppSource) count toward a MACC when they carry the “Azure benefit eligible” badge and are bought through the Azure portal under your agreement. AWS Marketplace and Google Cloud Marketplace offer similar arrangements; confirm the exact terms with your account team, because they vary by contract.
When To Choose Azure
You run a large Windows Server and SQL Server estate with Software Assurance and want Azure Hybrid Benefit and free ESUs.
Your organization standardizes on Microsoft 365, Entra ID, Intune and Power BI, and you want one identity and governance model.
You need hybrid or on-premises extension through Azure Arc and Azure Local.
OpenAI models inside your cloud tenancy, or Microsoft Fabric for analytics, are central to your plans.
You have an existing Microsoft enterprise agreement that gives you negotiating leverage.
When To Choose AWS
You want the widest catalog of managed services and instance types, and the largest talent and partner pool.
Your engineering team is already cloud-native on Linux and has AWS experience.
You want broad model choice on Amazon Bedrock without tying your AI strategy to one model family.
Your Microsoft licensing footprint is small, so Azure’s licensing advantages carry little weight.
When To Choose Google Cloud
Analytics is the core workload and BigQuery fits how your data team works.
You run Kubernetes at scale and want GKE and Cloud Run as your platform.
Gemini models and Google’s AI tooling are central to your product roadmap.
You use Google Workspace rather than Microsoft 365 and want to keep one vendor relationship.
In the Azure vs Google Cloud matchup specifically, the split is usually clear: Microsoft-centric enterprises lean Azure, while data and AI teams without a heavy Microsoft licensing stake often lean Google.
Multicloud: When It Makes Sense
Many large organizations already use more than one cloud, often because of acquisitions or because different teams chose differently. Deliberate multicloud makes sense when a specific service is clearly better elsewhere (for example, BigQuery for analytics alongside Azure for core business apps), when regulation or customers require it, or when you need negotiating leverage on large commitments.
The costs are real: duplicated skills, separate security tooling, more complex identity and networking, and egress charges between clouds. A sensible default is one primary cloud for most workloads, with secondary clouds used for specific, justified purposes. Tools such as Azure Arc, Kubernetes and Terraform help keep operations consistent, but they do not remove the need for platform expertise on each cloud.
Migration And Adoption Considerations
Whichever cloud you pick, the migration plan matters more than the logo. Start with an inventory of applications, dependencies and licenses; decide per workload whether to rehost, replatform or rebuild; and set up a landing zone with identity, networking, policy and cost controls before moving production systems. Each provider offers migration tooling (Azure Migrate, AWS Application Migration Service, Google Cloud Migration Center) and funding programs for qualifying customers, which your partner or account team can help you access.
If you are leaning toward Azure, our guide to Azure landing zones explains how to set up that foundation.
What To Ask A Cloud Partner
Which clouds do you hold verified partner designations or competencies on, and how many certified engineers work on each?
Can you model our total cost on each cloud including licensing, commitments and egress, not just compute list prices?
How will you handle Windows Server and SQL Server licensing, including Azure Hybrid Benefit and BYOL restrictions elsewhere?
Can you help us access provider migration funding or credits?
What does ongoing management look like after migration, and who owns cost optimization?
It depends on the workload. For Windows Server and SQL Server workloads where you own licenses with Software Assurance, Azure Hybrid Benefit and free ESUs often make Azure cheaper. For Linux workloads with no Microsoft licensing, list prices are close and the outcome depends on commitments, architecture and negotiated discounts.
Which Cloud Has The Largest Market Share?
AWS. Synergy Research Group’s Q2 2026 data put Amazon at 28% of worldwide cloud infrastructure services, Microsoft at 20% and Google at 15%, with Microsoft and Google growing faster.
What Is The Difference Between Azure vs Google Cloud For AI?
Azure’s Microsoft Foundry offers OpenAI models alongside Anthropic, Meta and Microsoft models, with Azure’s identity and compliance controls. Google’s Gemini Enterprise Agent Platform centers on Gemini models with access to open and third-party models through Model Garden. Both are capable; the better fit usually follows where your data and identity already live.
Can I Run Microsoft Workloads On AWS Or Google Cloud?
Yes. Both offer Windows Server and SQL Server with license-included pricing, and both federate with Microsoft Entra ID. The limits come from Microsoft’s licensing terms on bringing your own licenses, so review your agreements before assuming existing licenses can move.
Should We Go Multicloud?
Only for a clear reason, such as a best-in-class service on another platform, regulatory requirements or commercial leverage. Most organizations do better with one primary cloud and targeted use of a second.
Conclusion
The Azure vs AWS vs Google Cloud decision is less about which platform is best and more about which one fits your licenses, skills, data and AI plans. AWS leads on breadth and market share, Azure leads for Microsoft-centric organizations thanks to licensing and identity integration, and Google Cloud stands out for analytics, Kubernetes and Gemini. Model total cost with licensing and egress included, and pilot a real workload before you sign a multiyear commitment.
