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Cloud infrastructure

Cloud architecture refers to how computing resources, services, and applications are organized and connected to deliver functionality over a network.Cloud architecture refers to how computing resources, services, and applications are organized and connected to deliver functionality over a network. The most common architectural patterns include monolithic designs, where everything runs as a single unit, microservices, where applications are broken into smaller independent components that communicate through APIs, and serverless, where the provider handles all the underlying infrastructure and developers focus only on code execution. Modern architectures often blend these approaches, layering compute, storage, networking, and security services to balance performance, cost, and resilience.

Deployment models describe where and how those architectures actually run. Public cloud places workloads on shared infrastructure managed by a third party, which is typically the most cost-effective and scalable option. Private cloud keeps everything on dedicated infrastructure, either on-premise or hosted, and is often chosen for sensitive data or strict compliance requirements. Hybrid cloud combines both, letting organizations keep critical workloads private while leveraging the public cloud for elasticity, and multi-cloud spreads workloads across more than one provider to avoid lock-in or take advantage of specific strengths. Increasingly, edge deployments push compute closer to where data is generated, reducing latency for things like IoT and real-time analytics. The provider landscape is dominated by three hyperscalers. Amazon Web Services remains the largest by market share and offers the broadest catalog of services, making it the default choice for many enterprises. Microsoft Azure has grown rapidly thanks to its tight integration with Microsoft's enterprise software ecosystem and strong hybrid cloud story. Google Cloud Platform tends to lead in data analytics, machine learning, and Kubernetes-native tooling, and is a natural fit for organizations already invested in Google's data stack. Beyond the big three, providers like Oracle Cloud, IBM Cloud, and Alibaba Cloud serve specific niches, while platforms like DigitalOcean, Linode, and Vultr offer simpler, developer-friendly alternatives for smaller workloads.

Cloud infrastructure, architecture, and beyond

Cloud infrastructure is the foundation of everything else, the physical and virtual resources that make cloud computing possible.Cloud infrastructure is the foundation of everything else, the physical and virtual resources that make cloud computing possible. At the hardware level, this means data centers full of servers, storage arrays, and networking equipment distributed across regions and availability zones for redundancy. On top of that hardware sits a virtualization layer, typically hypervisors or container runtimes, which abstracts the physical resources into pools that can be allocated on demand. Supporting services like load balancers, firewalls, identity management, and monitoring round out the picture, giving users programmable building blocks rather than raw machines.

Cloud architecture is how those building blocks get assembled into working systems. Traditional monolithic designs run an entire application as a single unit, which is simple but harder to scale and maintain. Microservices break applications into smaller independent components that communicate over APIs, allowing teams to scale and deploy pieces individually. Serverless takes this further by letting developers run code in response to events without managing any underlying servers at all. Most real-world systems mix these patterns, layering compute, storage, networking, and security services to balance performance, cost, and resilience, and increasingly relying on infrastructure as code to make the whole stack reproducible.

Deployment models describe where those architectures actually live. Public cloud runs workloads on shared infrastructure operated by a third party and is usually the cheapest and most scalable option. Private cloud uses dedicated infrastructure, either on-premise or hosted elsewhere, and tends to be chosen for sensitive data or strict compliance needs. Hybrid cloud combines the two, keeping critical workloads private while bursting into the public cloud for elasticity, and multi-cloud spreads workloads across more than one provider to avoid lock-in or take advantage of specific strengths. Edge deployments push compute closer to where data is generated, which matters for things like IoT, real-time analytics, and anything where latency is the bottleneck.

The provider landscape is dominated by three hyperscalers. Amazon Web Services is the largest by market share and has the broadest catalog of services, which makes it the default choice for many enterprises. Microsoft Azure has grown quickly thanks to its tight integration with Microsoft's enterprise software ecosystem and a strong hybrid cloud story that resonates with organizations already running Windows Server and Active Directory. Google Cloud Platform tends to lead in data analytics, machine learning, and Kubernetes-native tooling, and fits naturally for teams already invested in Google's data stack. Beyond the big three, Oracle Cloud, IBM Cloud, and Alibaba Cloud serve specific niches, while platforms like DigitalOcean, Linode, and Vultr offer simpler, developer-friendly alternatives for smaller workloads.