The Big Three cloud providers are more alike than not

The three major cloud providers—Amazon Web Services, Microsoft Azure, and Google Cloud—offer nearly identical core infrastructure services in compute and storage for most enterprise use cases, making them functionally interchangeable where it matters most. While each provider promotes proprietary AI, database, and serverless services as differentiators, the majority of enterprises primarily rely on foundational virtual machines, block storage, file storage, and object storage, where differences in capability and pricing are minimal. This convergence signals market maturity, shifting the cloud selection decision from technical necessity to operational fit.

Every year, we attend cloud conferences to hear about new features, services, ecosystem expansion, and announcements that promise to reshape enterprise IT. These innovations matter. However, if we step back and look at how most enterprises actually consume public cloud, for all practical purposes, the three big cloud providers are essentially the same where it counts most. This statement can make people uncomfortable because the market encourages us to see dramatic differences in AI services, databases, frameworks, and niche capabilities that each provider would like to position as strategic lock-ins. While valuable and sometimes the right choice, they are not the most important elements for most cloud deployments. The center of gravity remains core infrastructure. When we talk about core infrastructure, we are referring to compute and storage. Compute includes processor options, memory configurations, instance families, operating system support, elasticity models, and the ability to reliably provision capacity at scale. Storage includes block storage, file storage, and the all-important object storage services that now underpin a massive share of enterprise applications and analytics https://www.cio.com/article/228901/what-is-predictive-analytics-transforming-data-into-future-insights.html platforms. If you compare the major providers through that lens, the differences are not profound in most use cases. All three offer a broad menu of virtual machines https://www.networkworld.com/article/969185/what-is-a-virtual-machine-and-why-are-they-so-useful.html . All three provide multiple processor and memory profiles. All three support Linux and Windows environments. All three offer options optimized for general-purpose workloads, compute-intensive processing, memory-intensive applications, storage-heavy patterns, and GPU-driven workloads. The packaging, naming, and tuning options differ. But the practical capability is remarkably close. The same is true for storage. Block storage is solid across the board. File storage is available and increasingly enterprise-ready. Object storage has become highly durable, globally scalable, and central to cloud economics and cloud architecture. Pricing, performance, and operational nuances differ, but for mainstream enterprise requirements, these services fall within a similar economic range. In other words, the choice is often less about whether a provider can do the job and more about which one fits your surrounding requirements slightly better. That is a sign of maturity. Now, let’s be clear about where this argument ends. If you are pursuing value from proprietary services, this article is not for you. If your architecture depends on a specific serverless https://www.infoworld.com/article/2261831/what-is-serverless-serverless-computing-explained.html platform, a cloud-specific analytics engine, a native AI orchestration service, or a database that runs only on a single provider, the differences matter a great deal. In fact, they might be the whole point of your selection process. It’s easy to understand why each provider pushes hard in this area. Proprietary services are where margins improve, customer stickiness increases, and the marketing narrative becomes more compelling. It’s also where technical differentiation can be meaningful. Enterprises should absolutely leverage those capabilities to deliver measurable business value. But this is not how the majority of companies consume cloud. Most enterprises still primarily leverage foundational compute and storage services. They are lifting and reshaping applications, building conventional digital platforms, hosting data, backing up systems, supporting development and testing, and running workloads that do not require a unique service available from only one hyperscaler. This reality is much less glamorous than the conference keynote, but it is also much more representative of how the cloud creates day-to-day value. There is a tendency to assume that the rise of AI completely changes this equation. I would argue that AI reinforces it. Yes, AI introduces specialized services, model ecosystems, vector databases https://www.infoworld.com/article/2335281/vector-databases-in-llms-and-search.html , orchestration frameworks https://www.infoworld.com/article/4122440/what-is-prompt-engineering-the-art-of-ai-orchestration.html , and provider-specific accelerators. Those are important. However, AI also drives greater demand for the basics: scalable compute, fast and durable storage, data pipelines, object stores, network throughput, GPU access, and reliable operational foundations. No matter where enterprises ultimately run their AI systems, whether in the cloud, across multiple clouds, or on-premises, they still need the same underlying capabilities. Training, tuning, retrieval, inference, governance, and data management all sit atop infrastructure that is more similar than different. The conversations about strategy should not be dominated by feature theater. They should be grounded in workload realities. The public cloud providers understand this, even if their messaging often emphasizes the shiny edges. The core business remains the delivery of elastic infrastructure that can support a broad and growing range of enterprise applications. AI does not eliminate that truth. It magnifies it. One of the most persistent and least useful questions in the cloud market is, “Which provider is the best?” The obvious answer hasn’t changed: The best provider is the one that best meets your requirements. But that answer is incomplete unless we also acknowledge that, for many core requirements, the providers are much more alike than they are different. Architects need to be disciplined to cut through the noise, examine actual workload needs, and identify where differentiation is meaningful versus where it is mostly branding and packaging. Commonality matters. Redundancy matters. Commodity thinking matters. There is value in recognizing when a service category has matured enough that the strategic decision should shift away from feature fascination and toward fit, economics, governance, skills, and operational alignment. For the majority of enterprises consuming compute and storage at scale, sameness is a benefit, not a weakness. It means there is real choice in the market. It means architectural decisions can be made with more pragmatism and less mythology. That point is worth calling out. Differentiation has been overstated in the wrong places. If you are choosing among the major providers for core compute and storage, you are not choosing between good and bad. You are choosing among highly capable platforms that have converged in the areas enterprises use most often. Frankly, that fact should make architects a bit more practical and far less anxious.