Back in 2022, we compared Azure, AWS, and Google Cloud like it was a straight fight between three heavyweights. At the time, AWS led the market on size and breadth of services, Azure was climbing fast thanks to its Microsoft ecosystem advantage, and Google Cloud was the data and analytics specialist with big ambitions.
Three years later, they’re still the big three, but the battleground has shifted. AI is no longer a future bet; it’s baked into so many day-to-day services that it’s easy to take it for granted. The conversation has moved away from ‘who’s got the most servers’ toward ‘whose AI ecosystem is smarter, safer and better integrated with the way we work.’
In this new phase, Microsoft Azure has transitioned from ‘catching up’ to shaping the direction of the market. And while AWS and Google Cloud are far from standing still, the momentum is clearly swinging toward a cloud future where AI, hybrid flexibility, and security are front and centre.
Azure’s Momentum
Microsoft hasn’t been shy about its ambitions. In 2025 alone, it’s investing $80 billion into AI and data centre expansion, with another $30 billion lined up before year’s end. These aren’t just big headline numbers, they’re fuelling tangible, ready-to-use capabilities for organisations of all sizes:
- Copilot across Microsoft 365, making AI part of your inbox, spreadsheets, and documents without needing to leave the apps you already use.
- Azure Arc for running workloads anywhere — from your own data centre to the public cloud — with consistent management and governance.
- Azure AI Foundry for businesses that want to build, fine-tune, and deploy their own AI models without stepping outside the Microsoft ecosystem.
For many organisations, Azure’s value is in that seamlessness. If your business already runs Microsoft tools, Windows Server, SQL, Office 365, Teams. Azure doesn’t feel like ‘another platform’ to learn. It feels like the natural evolution of your existing IT estate.
AI Security and Compliance Built In
With AI adoption accelerating, security has become one of the defining differentiators in cloud choice. AI isn’t just another application you run in the cloud, it’s a process that can touch sensitive data, customer records, intellectual property, and decision-making logic. That makes security, compliance, and trust absolutely critical.
Microsoft has built Azure’s AI services on the same robust security foundations as the rest of its cloud platform:
- Identity protection through Microsoft Entra ID (formerly Azure AD), controlling access down to the dataset and model level.
- Data residency and compliance controls allow AI workloads to stay within specified geographies to meet GDPR, financial services, and healthcare regulations.
- Global threat intelligence, drawing on Microsoft’s massive security network to detect and respond to threats in minutes, not days.
Azure also operates on a zero-trust architecture, where no user or device is implicitly trusted. Every access attempt is verified, logged, and monitored, a vital safeguard as AI-enabled services become part of everyday workflows.
With over 3,500 cybersecurity experts, including dedicated red and blue teams who simulate attacks and defences, Azure is continually stress-tested against real-world threats. For regulated industries, this isn’t optional it’s the reassurance they need to move forward with AI adoption.
If you’re looking to embrace AI without compromising on security, our Fordway Security Services can help assess your risk exposure, implement best practices, and provide ongoing compliance monitoring for your Azure environment.
Google Cloud: AI at Its Core
Google Cloud may not match Azure’s enterprise penetration or AWS’s scale, but it has consistently played to its strengths: AI, data analytics, and developer-centric tools. In 2025, those strengths have become even more pronounced.
The Gemini models, successors to Google’s PaLM language models now sit at the heart of GCP, powering everything from advanced search and content summarisation to AI-powered customer service and custom agents in Google Workspace.
For data-heavy organisations, GCP’s AI capabilities are hard to beat. Its custom Tensor Processing Units (TPUs) are optimised for large-scale model training and inference, delivering performance and cost efficiencies that make a real difference in competitive, data-driven markets.
The result? Google Cloud has carved out a niche with research labs, analytics-driven enterprises, and industries where AI experimentation is as important as day-to-day operations.
AWS: AI on a Massive Scale
AWS remains the most extensive cloud platform by revenue and service count, and it’s brought that same breadth to AI. Its Bedrock platform gives developers access to multiple foundation models, including Amazon’s own Nova family, as well as offerings from Anthropic, AI21 Labs, Meta, and others.
This multi-model approach means AWS customers can choose the right model for the job, whether they’re optimising for cost, speed, or accuracy. It’s a “build-your-own” AI strategy — one that’s attractive to enterprises with diverse workloads or that want to avoid being tied to a single AI vendor.
AWS also continues to invest heavily in infrastructure, over $100 billion globally, ensuring it can support AI workloads at virtually any scale. For businesses that need raw capacity and maximum optionality, AWS is still a compelling choice.
Hybrid Cloud is Now the Default
In 2022, hybrid cloud was a strategic consideration. In 2025, it’s the default operating model for many enterprises. Few organisations are all-in on public cloud or staying entirely on-premises, and most large organisations now employ two or event all three of the hyperscalers. Instead, they mix and match based on compliance needs, performance requirements, and existing investments.
Azure has long led in hybrid deployments with Azure Arc and Azure Stack. These solutions allow organisations to run Azure services from their own data centres with the same management tools, APIs, and governance they’d get in the public cloud. This means workloads can move between environments without rewrites, downtime, or security compromises.
AWS and Google Cloud also have hybrid offerings, AWS Outposts, Google Anthos, but Azure’s decades-long integration with enterprise IT gives it an edge. For companies already running Microsoft infrastructure, hybrid isn’t an experiment… It’s a continuation.
Industry Adoption and Remaining Holdouts
Cloud adoption has surged since 2022. Recent figures suggest 94% of enterprises are using cloud services in some form, and only 3% have no plans to adopt cloud. Even in traditionally cautious industries like finance, healthcare, and government, public cloud usage is rising thanks to advances in compliance and security.
That said, full migration is still a journey. Many organisations are running hybrid setups, with certain legacy systems, sensitive datasets, or latency-critical applications remaining on-premises for now.
So, where does this leave the choice in 2025?
While all three providers have matured, their core strengths remain distinct:
- Azure: The leader in hybrid flexibility, enterprise integration, and built-in AI for Microsoft-first organisations.
- Google Cloud: The AI and analytics powerhouse, ideal for data-driven innovation and research-heavy workloads.
- AWS: The broadest, most flexible platform, offering the widest choice of AI models and unmatched global infrastructure.
Choosing between them isn’t just about cost or features anymore, it’s about aligning with the provider whose vision of AI, security, and integration best fits your own strategy.
The Bottom Line
In 2022, Azure was the obvious choice if you wanted tight integration with Microsoft tools. In 2025, that’s still true, but now it’s also where you’ll find some of the most user-ready AI capabilities on the market. Its combination of AI innovation, hybrid leadership, and enterprise-grade security makes it a natural fit for many organisations looking ahead to the next decade of cloud computing.
Google Cloud continues to set the pace in AI performance and analytics. AWS remains unmatched for scale, variety, and global reach.
The ‘best’ cloud is no longer one-size-fits-all. It’s about which provider’s AI, infrastructure, and integration approach aligns most closely with your needs. For a growing number of enterprises, that vision is looking increasingly Azure.
With our IT-as-a-service (ITaas) model, we can manage your IT operations on a pay as you go basis whilst guiding you through your journey with Microsoft Azure.
The planning stage of Microsoft Azure is vital to enable a successful, bespoke build of your data architecture and unlock the benefits from your Azure deployment. With this in mind, we provide a free Cloud Options Analysis which provides a comprehensive analysis of your existing IT infrastructure and investment, as part of a wider review of your entire skills, processes, and technology.
