What Is Cloud Computing?

Cloud computing gives enterprises access to abundant on-demand resources and technologies available in the cloud. So what is a cloud? All clouds are data centers—often multiple data centers—comprised of compute and storage resources connected by a network. These resources are virtualized into a shared pool of resources, which can be intelligently orchestrated to meet the ever-changing demands of enterprise applications and workloads.

Businesses can use cloud resources to enhance operations while reducing costs associated with on-premises hardware. Cloud computing—with a mix of service, deployment, and pricing models—allows businesses to quickly deploy new products and services and accelerate workloads.

However, planning and scrutiny of service level agreements are critical to understand costs, impacts to existing processes, and service metrics before adopting cloud services. You should also continuously monitor costs and performance after adoption to prevent over- or underprovisioning and overspending.

How Does Cloud Computing Work?

Cloud computing works by building out a robust cloud architecture that connects businesses to compute, storage, and networking resources over a network. These resources can come from servers in a privately owned data center, from a cloud service provider, or a mixture of both public and private data centers. This allows operational tasks—including workloads, applications, and data storage—to be moved from on-premises infrastructure to cloud infrastructure, where they can be managed remotely with virtualization tools and cloud management platforms.

Benefits of Cloud Computing

Businesses large and small can benefit from a cloud computing strategy. A retail store might use the cloud for a simple payment processing service or cloud storage, while a global enterprise can draw from a full suite of data center resources for application development and deployment at scale. Using the cloud, companies can:
 

• Reduce up-front costs: Cloud computing allows businesses to get the resources and run the applications they need without the prerequisite of maintaining hardware on-premises. This can help reduce up-front costs by allowing hardware to be consolidated and managed in fewer locations.
• Scale resources: In addition to potential cost savings, cloud computing provides the scalability and dynamism to support fluctuating workloads, giving businesses quick access to the resources they need when they need them.
• Improve collaboration: Cloud-based productivity tools and project management platforms allow team members to collaborate online. This makes teams more flexible and inclusive by enabling dispersed workforces to contribute to projects in real time or asynchronously. It also improves productivity by streamlining workflows.
• Eliminate data silos: By centralizing corporate data in the cloud, data can be shared between dispersed physical locations, eliminating data silos and improving visibility. Furthermore, advanced analytics can be performed on more-comprehensive datasets to provide actionable insights into business operations.

Challenges of Cloud Computing

The main challenge of cloud computing lies in avoiding unnecessary spending. This is best achieved through strategic planning and the use of intelligent tools for rightsizing and automating resource allocation. Causes of overspending include:
 

• Cost overruns: Cloud migrations often go over budget. One reason is overprovisioning—that is, allocating more resources than needed. Underprovisioning, on the other hand, can lead to overspending on supplementary resources to make up for shortfalls in the original resource provisioning. Cloud optimization—the process of rightsizing cloud infrastructure to run all workloads at peak efficiency—and continuous cost management are often necessary to reduce the total cost of ownership (TCO).
• Vendor lock-in: Restrictive cloud service provider (CSP) contracts, data egress fees, and proprietary cloud platforms and tools can limit an enterprise’s ability to migrate and scale workloads and applications, reducing operational agility and inflating TCO.
• Suboptimal performance: CSPs offer a plethora of service options, many of which can seem identical on the surface, and the offerings change often, making it difficult to pair workloads to the optimal instances. AI-enabled tools and automation are often needed to evaluate workloads and inform cloud provisioning decisions.
• Data exposure risk: Sending data to the cloud for processing AI workloads can be expensive, incurring high surcharges for data transfer costs, compute, and storage fees. It can also leave enterprises prone to data exposure risks, such as malicious attacks, data interception, and leaks. A hybrid cloud approach, which strategically uses a mix of on-premises and cloud-based processing, can help mitigate some costs and risks.
  
  It is also important to understand what data security and privacy measures service providers have in place and where responsibility lies to prevent data breaches and possible regulatory compliance issues.

Cloud Deployment Models

There are two basic cloud deployment models—public and private—along with various hybrid models that converge their advantages.

Public Cloud

The public cloud offers pay-as-you-go access to computing, storage, and networking resources. These are delivered through cloud service providers like Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Public cloud services require little up-front cost and can be deployed quickly for a fast time to market. They are also a good fit for workloads that might only run for a short time.

Private Cloud

A private cloud is operated on infrastructure in your data center. It requires a larger up-front cost and ongoing management, but it can deliver potential cost savings over the pay-as-you-go model in the long term. Private clouds offer granular control for mission-critical workloads and legacy applications that are difficult or impossible to move to the public cloud. They are also ideal for keeping sensitive data private and secure and for ensuring compliance with data sovereignty regulations.

Hybrid Cloud

Every workload has its own requirements. Some businesses utilize a hybrid cloud model for this reason. A hybrid cloud gives businesses the option to run workloads in both private and public cloud environments, allowing data and applications to be shared between them as needed. This helps maximize potential cost savings and resource usage, as well as maintains control over sensitive data.

Multicloud

A multicloud approach involves a combination of services from different cloud providers. This gives businesses the most options when it comes to services and pricing. A multicloud strategy relies on software to manage and orchestrate resources across different providers, resulting in a flexible, cost-optimized cloud environment.

Hybrid Multicloud

Encompassing elements from all of the above, a hybrid multicloud strategy places workloads wherever they run best. Unified management platforms help simplify managing resource usage across multiple public and private clouds, on-premises hardware, and the edge, creating consistency with centralized control and visibility. This strategy can help enterprises tailor cloud resources to their needs to avoid overprovisioning and to navigate governance issues.

Types of Cloud Computing Services

One of the main factors in choosing a cloud computing service model is control vs. convenience. For example, an Infrastructure as a Service (IaaS) model will offer businesses greater control over cloud resource use but require more active management and oversight. In contrast, a Platform as a Service (PaaS) model provides convenient access to a development environment but limited control over the infrastructure and integrated tools.

Infrastructure as a Service

IaaS models allow businesses to host servers, networks, and storage and are a better choice for organizations looking to optimize their resource usage. IaaS models rely on virtualization technology—specialized software that creates virtual versions of physical resources—to consolidate resources onto fewer servers. As a result, the hardware can be better used, helping to optimize costs. This model offers the most control and flexibility of all service models, allowing resources to scale as needed, and often makes use of block storage technology for low latency and enhanced resiliency.

Platform as a Service

In PaaS models, service providers manage the underlying infrastructure and provide pay-as-you-go access to their environment—or platform—along with online application development, testing, and deployment tools. Microsoft Azure and Google Cloud are examples of PaaS platforms. This model lends itself well to cloud DevOps principles and practices, allowing enterprises to develop, deploy, and manage applications across the entire life cycle of an application. It can also help streamline workflows, since multiple users can access the development application simultaneously.

Software as a Service (SaaS)

Most businesses rely on a variety of SaaS products for their everyday operations. These are cloud-based applications like customer relationship management (CRM) software, project management software, and email marketing platforms. In a SaaS model, users are only responsible for managing their data; licenses are purchased on a subscription basis.

With any model, before selecting a cloud service provider, it is important to understand their data security practices and any process changes required to integrate cloud services into existing workflows and to scrutinize service level agreements, as these contracts stipulate key service metrics like uptime and performance.

Cloud Security

Security in the cloud is a business imperative. Secure cloud infrastructure helps to ensure that sensitive company data remains confidential and that business operations carry on without disruption. Hardware- and software-based cloud security technologies, such as confidential computing, help businesses retain privacy and control over their data and applications so they can leverage the advantages of the cloud resources with less risk.

Cloud Computing Use Cases

Cloud computing has given rise to many business use cases.

Remote Collaboration

From communication services to productivity tools and project management suites, cloud-based tools enable hybrid workforces to communicate, share documents, contribute to projects, and stay in sync from afar, improving collaboration, creativity, and efficiency.

Scalable Resources

Cloud computing provides the scalability to support fluctuating workloads. Rules-based dynamic scaling, for instance, adjusts resource allocations depending on the workload demands at any given time—such as when an online retailer experiences a spike in traffic. Cloud bursting is a similar concept, allowing applications to tap into public cloud resources as on-premises resources reach capacity. Both strategies keep business operations flowing smoothly while avoiding overprovisioning.

Data and Analytics

Cloud computing allows for the creation of databases that centralize business data, helping businesses stay on top of day-to-day operations and run analytics to inform decision-making. Cloud infrastructure provides the scalability and resources for processing large datasets and big data analytics.

Application Development and Management

Cloud computing gives businesses on-demand access to the resources they need throughout the application life cycle. Additional resources can be deployed as needed, particularly during the application development and testing phases. Meanwhile, cloud automation and orchestration tools help improve manageability and automate IT tasks at scale.

AI Deployments

Cloud architecture is integral to AI deployments, providing the complex compute, data storage, and networking resources necessary to integrate computer vision, natural language processing, and generative AI into business operations. A hybrid cloud approach helps to facilitate cost-efficacy, allowing for models to run on local hardware when expensive cloud resources are unnecessary, such as during the extensive inference phase of AI deployment.

Disaster Recovery

Backups of critical data, applications, and virtual architecture can be stored in the cloud, forestalling downtime and the loss of valuable data in the event of hardware failure.

The Future of Cloud Computing

From email to the big data era, cloud computing has had a huge impact on the business landscape and is expected to be a big driver of innovation going forward. Cloud computing is essential in managing AI life cycles and edge-to-cloud deployments and may one day play an expanded role in quantum computing. The advent of AI-driven cloud-native tools—including orchestration, resource management, and threat response—is also expected to further automate IT operations. These advances could allow enterprises to drive substantial efficiencies and growth.