7 Uses of Cloud Computing and the Cost Test for Each

Cloud computing remains most useful in seven practical roles: delivering software, running customer applications, creating development environments, analyzing data, operating AI workloads, storing and distributing content, and supporting recovery. The technology’s on-demand model is still relevant, but the business case now depends less on simply “moving to the cloud” and more on matching each workload to the right economics and controls.

The important update is that AI and machine learning merit a category of their own, while IaaS, PaaS and SaaS should not be presented as three separate uses: they are service models through which different jobs are delivered. Cost discipline is also central. Flexera’s 2026 State of the Cloud survey of 753 technical professionals and executives found that 85% regarded cost as a leading cloud challenge, 82% cited security and 73% used a hybrid-cloud model.

Service models describe delivery, not the business job

A useful cloud plan begins by separating what an organization wants to accomplish from how a provider supplies the technology. The NIST cloud-computing definition describes on-demand access to a shared pool of configurable resources that can be provisioned and released rapidly; it also distinguishes three service models and four deployment models.

IaaS gives the customer relatively direct control over rented compute, networking and storage. PaaS removes more infrastructure administration from the application team, while SaaS delivers a finished application. Public, private and hybrid clouds answer a different question: where and across whose infrastructure the workload operates.

Consequently, “using PaaS” is not a business outcome. A company may use PaaS to deploy a web application, process transactions or expose an API. The seven categories below focus on those concrete jobs.

1. Delivering business software as SaaS

SaaS is the most visible use of cloud computing for many employees. Email, collaboration, customer management, accounting and human-resources applications can be accessed without each organization operating the application stack itself. Central updates also avoid separate software rollouts to every managed device.

The cost test is broader than the subscription price. Buyers should compare active-user utilization, administrative effort, integration requirements, data-export options and the price of premium security or compliance features. A per-seat product can be economical for a distributed workforce yet become expensive when unused accounts, overlapping applications or separate add-ons accumulate.

2. Hosting and scaling customer-facing applications

Cloud infrastructure can run websites, APIs, mobile back ends and transaction systems whose demand changes over time. Virtual machines, containers and serverless services offer different levels of operational control, but all can replace a long hardware-procurement cycle with capacity that teams provision through software.

This use is strongest when traffic is variable, deployment speed matters or the organization serves users in several regions. It is less compelling when a stable workload already runs efficiently on owned equipment and gains little from elasticity. The comparison must include database charges, network traffic, observability, support and the engineering time required to operate the chosen architecture—not only the advertised compute rate.

3. Creating development and test environments

Development teams use cloud resources to create temporary test systems, build software, run automated checks and reproduce production-like conditions. Environments can be defined as code, recreated consistently and removed after a branch, experiment or release is complete. This reduces the need to reserve permanent hardware for work that happens intermittently.

The economic advantage depends on enforcing that temporary character. Forgotten test databases, oversized virtual machines and idle environments can turn convenience into recurring waste. Automatic shutdown schedules, expiration tags, project budgets and ownership records make the model more defensible. Sensitive production data should not be copied into testing merely because storage is easy to provision; teams need masked or synthetic data and appropriate access controls.

4. Processing data and running analytics

Cloud data warehouses, data lakes and managed processing engines let organizations combine operational records, event streams and external datasets without first building a fixed-capacity analytics cluster. The same environment can support scheduled reporting, interactive analysis and high-volume batch processing, although those patterns have different performance and cost profiles.

The cost test concerns data movement and query behavior. Centralizing data may simplify analysis, but frequent transfers between regions, providers or on-premises systems can add latency and charges. Poorly governed queries can also scan far more data than the result requires. Retention rules, workload separation, query controls and a clear data-location policy should therefore be part of the design rather than later cleanup.

5. Training models and serving AI applications

AI is now a distinct cloud workload because it can require specialized accelerators, large datasets, managed model platforms and a separate inference path for production requests. Renting that capacity can make experimentation possible without purchasing hardware that may be scarce, quickly superseded or idle between training runs.

However, training and inference have different economics. Training may create an intense but finite demand for compute, while inference produces continuing costs tied to requests, tokens, model size or reserved capacity. Teams should measure cost per useful business transaction and include data preparation, evaluation, monitoring and human review. A demonstration that works technically does not establish that the production service will be affordable, accurate or compliant.

6. Storing and distributing data

Object storage is used for documents, media, logs, archives, application assets and data exchanged between services. Organizations can select storage classes according to expected access frequency and connect stored objects to analytics, content-delivery or lifecycle-management systems. The current Google Cloud explanation of common use cases likewise identifies data storage, infrastructure scaling, application development, analytics and disaster recovery while distinguishing them from IaaS, PaaS and SaaS.

Low storage prices do not make every design inexpensive. Retrieval, API operations, replication and outbound transfer may materially affect the bill, especially for media distribution or data shared across clouds. Before choosing a location or storage class, estimate how often information will be read, where its consumers run, how long it must be retained and how quickly it must be restored.

7. Backup, resilience and disaster recovery

Cloud services can hold backup copies, replicate systems to another location and supply capacity for recovery after an outage. These are related but different functions: a backup preserves recoverable data, while disaster recovery covers the applications, dependencies, procedures and infrastructure needed to resume operations.

Cloud storage alone does not prove recoverability. A workable design specifies a recovery point objective for acceptable data loss and a recovery time objective for acceptable interruption. It also isolates credentials and copies appropriately, documents dependencies and tests restoration. Replication can reproduce accidental deletion or corruption, so it should not automatically be treated as an independent backup.

How to decide whether a workload belongs in the cloud

The strongest choice starts with the workload rather than a provider. Identify demand variability, geographic reach, recovery requirements, data sensitivity, latency, portability and the skills available to operate it. Then compare the full operating model with a realistic alternative, including licenses, support, networking, security controls, migration work and the cost of leaving later.

Hybrid architecture can be a valid result of that assessment: one application may remain on owned infrastructure while development, backups or burst capacity use public services. It is a deployment decision spanning several uses, not an eighth use by itself. The cloud earns its place when on-demand access, managed capabilities or resilience produce measurable value after recurring cost and operational risk are counted.

Seven Uses of Cloud Computing—and the Cost Test Each One Must Pass