What Is Public Cloud? How It Works, Benefits, Costs, Security & Choosing the Right Provider (2026)
Public cloud now underpins everything from e-commerce checkouts to hospital scheduling systems, yet most explanations stop at a one-line definition. Knowing what public cloud is matters less than knowing how its architecture actually works, who is responsible for security once you sign up, why the bill on your dashboard rarely matches the sticker price of a single virtual machine, and which of the major providers fits your organization's specific workload, skills, and compliance obligations. This guide works through all of it, from the underlying mechanics to a practical framework for choosing a provider.
TL;DR
Public cloud is computing infrastructure owned and operated by a third-party provider and made available to multiple customers over the internet, with each customer's workloads kept logically isolated.
It works through virtualization, containers, and serverless computing layered over provider-owned data centers, accessed through APIs and automated by a control plane that provisions and meters resources.
The biggest benefits are elastic scaling and reduced hardware ownership; the biggest risks are unpredictable costs, misconfiguration, and vendor dependence.
Security and compliance follow a shared responsibility model: the provider secures the underlying cloud, and the customer secures what they put in it.
Choosing AWS, Microsoft Azure, or Google Cloud depends on workload requirements, existing ecosystem, skills, and contractual terms, not on any provider being universally "best."
What Is Public Cloud? (Quick Answer)
Public cloud is computing infrastructure — servers, storage, networking, and software — that a third-party provider owns, operates, and makes available to multiple customers over the internet on a pay-as-you-go basis. Customers share underlying physical infrastructure with other tenants, but their data and workloads remain logically isolated according to the provider's access controls.
What is the single most important factor when choosing a public cloud provider?
0%Security & compliance
0%Total cost & pricing predictability
0%Reliability & performance
0%Data residency & global availability
Table of Contents
Public Cloud Service Models: IaaS, PaaS, SaaS, and Serverless
Public Cloud Architecture: Regions, Availability Zones, Virtualization, Networking, and Storage
Public Cloud vs Private Cloud vs Hybrid Cloud vs Multi-Cloud
Public Cloud Security: Shared Responsibility and Core Controls
Major Public Cloud Providers: AWS vs Microsoft Azure vs Google Cloud
What Is Public Cloud?
Public cloud is a model of computing in which a third-party provider owns and operates the physical infrastructure — data centers, servers, storage arrays, and network hardware — and makes computing resources available to multiple customers over the internet, typically on a pay-as-you-go basis. The U.S. National Institute of Standards and Technology (NIST) defines cloud computing broadly as on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned with minimal management effort, and it identifies public cloud as one of four deployment models alongside private, community, and hybrid cloud. [1]
"Public" in this context describes who owns and operates the infrastructure and how broadly it is offered, not whether a customer's data is publicly visible. A retailer's customer database on AWS, Azure, or Google Cloud is no more exposed to the public internet than a database sitting in the retailer's own data center; exposure depends on the access controls and network configuration the customer applies, not on the deployment model itself.
Public cloud providers achieve efficiency through resource pooling: the same physical servers, storage systems, and network hardware serve many customers at once, with software-defined boundaries — virtual machines, containers, separate accounts, and encryption keys — keeping one customer's workloads logically isolated from another's. Multitenancy does not mean customers can see each other's data; it means they share the same underlying hardware pool under the provider's isolation controls.
A hypothetical example: a small logistics company that needs extra order-processing capacity only during peak shipping season could rent virtual servers from a public cloud provider for those weeks, pay only for the hours used, and release the capacity afterward, without buying, housing, or maintaining physical servers of its own.
How Does Public Cloud Work?
Public cloud works by abstracting physical hardware into software-defined resources that customers request through an API or web console, rather than through a purchase order to a hardware vendor. Underneath, a provider's global network of data centers houses the physical servers, storage devices, and networking equipment; a layer of virtualization, container orchestration, and serverless runtimes turns that hardware into resources a customer can provision in minutes.
A simple walkthrough of what happens when a customer launches a new resource:
The customer selects a resource, such as a virtual machine, a managed database, or a serverless function, through the provider's console, command-line tool, or API.
The provider's control plane checks the request against the customer's account permissions and available capacity in the selected region.
Automated provisioning systems allocate compute, storage, and network resources from a shared pool and attach them to the customer's virtual network.
An operating system image or container is deployed onto the allocated capacity, and the resource becomes reachable through the customer's configured network and identity controls.
Usage is metered continuously, by the second, the request, or the gigabyte, and reported to a billing system that produces the customer's invoice.
The customer can scale the resource up, down, or to zero, and release it when no longer needed, stopping the metered charges.
This automation is what separates public cloud from traditional hosting: a request that once took days of procurement and physical installation can complete in minutes, because the underlying capacity already exists in the provider's data centers, waiting to be allocated.
Core Characteristics of Public Cloud
NIST's definition of cloud computing rests on five essential characteristics that apply to public cloud deployments specifically. [1]
On-demand self-service — a customer can provision compute, storage, or other resources automatically, without a phone call or ticket to the provider's staff.
Broad network access — resources are reachable over standard internet protocols from a range of client devices, not only from a private, physically connected network.
Resource pooling — the provider's physical and virtual resources serve multiple customers using a multi-tenant model, with resources dynamically assigned according to demand.
Rapid elasticity — capacity can scale outward or inward quickly, often automatically, to match demand, and can appear unlimited to the customer at any given time.
Measured service — resource usage is monitored, controlled, and reported, giving both provider and customer visibility into consumption, which is the basis for pay-as-you-go billing.
Public Cloud Service Models: IaaS, PaaS, SaaS, and Serverless
NIST's original definition names three cloud service models — Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) — that differ in how much of the technology stack the provider manages versus the customer. [1] Serverless computing, also called Function as a Service (FaaS), has since become an important operating model in its own right; it is not one of NIST's original three service models, but a way of consuming compute where the provider manages the server entirely and the customer supplies only application code that runs in response to events.
In IaaS, the provider supplies virtualized compute, storage, and networking, and the customer manages the operating system, runtime, and application. In PaaS, the provider also manages the operating system and runtime, so the customer focuses only on application code and data. In SaaS, the provider manages the entire stack including the application itself, and the customer only configures and uses it. Serverless computing removes server management from the customer entirely for individual functions, billing by execution rather than by provisioned capacity.
Model | Customer manages | Provider manages | Common use | Example category |
IaaS | OS, runtime, applications, data | Physical hardware, virtualization, network | Custom infrastructure, migrated workloads | Virtual machines, block storage |
PaaS | Applications, data | OS, runtime, middleware, infrastructure | Application development and deployment | Managed app platforms, managed databases |
SaaS | Configuration, data, users | Everything below the application | Ready-to-use business software | Email, CRM, productivity suites |
Serverless / FaaS | Application code, event logic | Servers, scaling, runtime | Event-driven and intermittent workloads | Function execution services |
Public Cloud Architecture: Regions, Availability Zones, Virtualization, Networking, and Storage
A public cloud provider organizes its physical infrastructure into regions, which are geographic areas containing multiple, physically separate data centers, and availability zones, which are one or more discrete data centers within a region, each with independent power, cooling, and networking. Distributing workloads across availability zones lets an application survive the failure of a single data center; distributing across regions adds protection against a wider event and can also reduce latency for users in different geographies.
Virtualization software divides physical servers into multiple virtual machines, each with its own operating system and resources. Containers package an application with its dependencies into a lightweight, portable unit that shares the host operating system's kernel, making them faster to start and easier to move between environments than full virtual machines. Serverless platforms go a step further, running application code without the customer provisioning any server or container at all.
Above the compute layer, virtual networks — called VPCs on some providers and VNets on others — let customers define private IP address ranges, subnets, route tables, and firewalls inside the provider's shared infrastructure. Load balancers distribute traffic across multiple compute instances; content delivery networks cache content at edge locations closer to users. Storage services typically split into object storage for unstructured files, block storage for attaching to virtual machines, and file storage for shared network file systems, alongside managed database services that handle patching, backups, and replication.
Providers do not use identical terminology or topology for any of this: AWS, Microsoft Azure, and Google Cloud each structure their regions, virtual networks, and managed services differently, so architecture designed for one provider does not translate directly to another without adaptation.
Public Cloud vs Private Cloud vs Hybrid Cloud vs Multi-Cloud
NIST's four deployment models — public, private, community, and hybrid cloud — describe who owns the infrastructure and who it serves, while "multi-cloud" is an industry term describing the use of more than one public cloud provider, not a NIST deployment model. [1] Private cloud dedicates infrastructure to a single organization, whether hosted on the organization's own premises or by a third party; hybrid cloud connects private and public environments so workloads and data can move between them; multi-cloud, by contrast, means running workloads across two or more separate public cloud providers, often to avoid dependence on a single vendor or to use each provider's particular strengths.
Deployment model | Ownership / management | Typical cost model | Common use case |
Public cloud | Third-party provider, shared infrastructure | Pay-as-you-go, consumption-based | Variable workloads, rapid scaling, new development |
Private cloud | Single organization, dedicated infrastructure | Capital expenditure or dedicated hosting fees | Strict regulatory or performance requirements |
Hybrid cloud | Combination, connected environments | Mixed, depends on components | Gradual migration, data residency needs, legacy integration |
Multi-cloud | Two or more public providers | Combined pay-as-you-go across providers | Avoiding lock-in, best-of-breed services, redundancy |
Hybrid cloud and multi-cloud solve different problems and are not interchangeable: hybrid cloud is about connecting private and public environments, while multi-cloud is about using multiple public providers side by side, and an organization can do both at once.
Key Benefits of Public Cloud
Public cloud's advantages come from shifting infrastructure ownership and operational burden to a specialized provider, but each benefit carries conditions that determine whether it materializes in practice.
Faster provisioning — new capacity can be available in minutes rather than the weeks or months a hardware purchase and installation typically requires.
Elastic scaling — capacity can expand to absorb demand spikes and contract afterward, which particularly benefits workloads with variable or seasonal traffic.
Global reach — providers operate data centers across many regions, letting an organization deploy closer to users worldwide without building its own facilities.
Reduced hardware ownership — customers avoid the capital expense and lifecycle management of owning servers, though this shifts cost from capital to operating expenditure rather than eliminating it.
Access to managed and advanced services — managed databases, analytics platforms, and AI/ML services let teams use sophisticated capabilities without building and operating them from scratch.
Faster experimentation — infrastructure as code and self-service provisioning let development teams test ideas and discard them cheaply, supporting faster iteration.
None of these benefits is automatic or guaranteed to reduce total spending; each depends on how well the organization architects, monitors, and governs its cloud usage.
Limitations, Risks, and Trade-Offs of Public Cloud
Public cloud shifts some risks rather than eliminating them, and introduces others that are specific to shared, consumption-based infrastructure.
Unpredictable spend — consumption-based billing means costs can rise with usage in ways that are harder to forecast than a fixed hardware budget.
Data-transfer and egress charges — moving data out of a provider's network, and sometimes between its own regions, can carry per-gigabyte fees that are easy to underestimate.
Misconfiguration risk — publicly accessible storage, overly permissive access roles, and open network ports are among the most common causes of cloud security incidents, and they are customer-side failures rather than provider vulnerabilities.
Skills gaps — architecting, securing, and cost-managing cloud environments requires expertise that many organizations must build or hire for.
Service quotas and regional availability — not every service is available in every region, and providers impose default resource limits that can require advance planning.
Vendor dependence — reliance on a provider's proprietary services can make it costly or slow to move workloads elsewhere later.
Outages — even well-architected providers experience regional service disruptions, and an application's actual resilience depends on how it was designed, not solely on the provider's infrastructure.
These risks argue for deliberate architecture and governance, not against public cloud itself; most are manageable with planning.
Public Cloud Costs: How Pricing Really Works
Public cloud pricing is built around consumption: customers are billed for compute by the second or hour, for storage by the gigabyte per month, for many managed services by the request or by data processed, and for data transfer out of the provider's network by the gigabyte. [8] Pay-as-you-go does not automatically mean cheaper than owning equivalent hardware; it means costs track usage rather than a fixed depreciation schedule, which can be an advantage or a disadvantage depending on how steady that usage is.
A simple way to think about total cloud cost is as the sum of several categories, adjusted for any discounts: compute, plus storage, plus data transfer and networking, plus managed services and databases, plus licensing, plus observability and logging, plus backups, plus support plan fees, plus the operational overhead of managing it all. This equation is a simplification, not a complete accounting of any provider's actual billing structure, but it captures where costs commonly hide.
Providers commonly offer several purchasing options for compute: on-demand pricing with no commitment, reserved or committed-use discounts in exchange for a one- to three-year commitment, and spot or preemptible capacity, which is spare capacity offered at a steep discount that the provider can reclaim on short notice, suited to fault-tolerant or interruptible workloads. [8] Committed-use discounts can reduce steady-state compute costs substantially compared with on-demand rates, but only when actual usage matches the commitment; unused commitment is wasted spend.
A cheap compute instance can still produce an expensive architecture: an efficient virtual machine paired with excessive data transfer between regions, an oversized managed database, or verbose logging retained indefinitely can cost more in aggregate than the sticker price of the compute itself suggests. Total cost of ownership, not the list price of a single resource, is the only realistic basis for comparing cloud costs against on-premises alternatives or against other providers.
How to Control and Optimize Public Cloud Costs
FinOps, short for cloud financial operations, is the operational framework and cultural practice that brings engineering, finance, and business teams together to maximize the business value of cloud spending through collaboration and data-driven decisions; it is broader than simply cutting the bill, since spending more on the right service can be the financially correct decision if it drives more business value. [5]
Tagging and cost allocation — labeling resources by team, project, or environment so spending can be attributed and analyzed.
Budgets, alerts, and forecasting — setting spending thresholds and being notified before they are exceeded, rather than discovering overspend after the invoice arrives.
Rightsizing — matching instance and storage sizes to actual utilization instead of provisioning for worst-case demand by default.
Autoscaling and scheduling — scaling capacity to match real-time demand, and shutting down non-production environments outside working hours.
Storage lifecycle policies — automatically moving infrequently accessed data to cheaper storage tiers or deleting it on a schedule.
Committed-use discounts and spot capacity — applying reserved pricing to steady, predictable workloads and spot pricing to interruptible ones.
Anomaly detection and regular review — watching for unexpected spending spikes and revisiting architecture and commitments on a recurring cadence rather than as a one-time project.
The goal of cost optimization is maximizing the value delivered per dollar of cloud spend, not minimizing the bill at any cost to performance, reliability, or delivery speed.
Public Cloud Security: Shared Responsibility and Core Controls
Public cloud security operates under a shared responsibility model: the provider is responsible for security "of" the cloud, meaning the physical facilities, hardware, and the software that runs its core infrastructure and managed services, while the customer is responsible for security "in" the cloud, meaning how they configure identity, network access, data protection, and the software they deploy. [2][3][4] Moving to public cloud does not remove the customer's security responsibilities; it changes which responsibilities remain, and the exact split shifts depending on whether a service is IaaS, PaaS, or SaaS.
Responsibility area | IaaS | PaaS | SaaS |
Physical infrastructure, hardware, hypervisor | Provider | Provider | Provider |
Operating system and patching | Customer | Provider | Provider |
Application software | Customer | Customer | Provider |
Identity and access management | Customer | Customer | Customer |
Data classification and encryption | Customer | Customer | Customer |
Network controls and firewalls | Customer | Shared | Provider |
Regardless of service model, certain controls remain the customer's responsibility everywhere: identity and access management, including least-privilege permissions and multi-factor authentication, since compromised credentials are among the most common paths to a cloud security incident. Encryption in transit and at rest protects data from interception, but encryption alone does not prevent a misconfigured access policy from exposing that same data to anyone with legitimate credentials; encryption solves confidentiality of the data itself, not who is allowed to reach it.
Effective cloud security programs also include network segmentation and firewall rules scoped to least privilege, continuous logging and monitoring to detect unusual activity, regular vulnerability management and patching for anything the customer manages, and tested backup and recovery procedures, since having a backup is not the same as a verified ability to restore from it. Zero Trust principles, which verify every access request rather than trusting anything inside a network perimeter, increasingly guide how these controls are designed together. [6]
Compliance, Privacy, Data Residency, and Data Sovereignty
Compliance certification held by a cloud provider, for a standard such as ISO 27001 or SOC 2, demonstrates that the provider's own infrastructure and processes meet that standard; it does not automatically make a customer's specific workload compliant, since the customer's configuration, data handling, and application logic sit outside what the provider's certification covers. Meeting a regulatory obligation is a shared outcome, split according to the same shared responsibility logic that governs security.
Data residency refers to the physical or geographic location where data is stored; data sovereignty refers to the legal authority a jurisdiction holds over data located or processed within it, which can apply even when a customer did not expect it to; and data privacy concerns how personal data is collected, used, and protected, which is governed by laws that vary by jurisdiction and by the type of data involved. These three concepts are related but distinct, and an organization can satisfy one without automatically satisfying the others.
Practical steps include selecting provider regions that keep data within required jurisdictions, understanding who holds the encryption keys protecting stored data, retaining audit evidence of configuration and access, and defining clear data retention and deletion policies. Organizations handling regulated data should confirm their specific obligations with qualified legal or compliance professionals and with the relevant provider's own compliance documentation, since this guide cannot substitute for jurisdiction-specific legal advice.
Reliability, Performance, Backup, and Disaster Recovery
A service-level agreement (SLA) is a contractual commitment to a level of availability, typically expressed as a percentage of uptime over a billing period, backed by service credits if the provider falls short. It is not a guarantee of zero downtime, and it does not by itself make an application resilient. High availability for a customer's own application depends on how that application is architected across the provider's availability zones and regions, not solely on the provider's infrastructure.
Redundancy, running duplicate resources so a single failure does not cause an outage, is different from backup, a separate, recoverable copy of data taken at a point in time; redundancy protects against infrastructure failure, while backup protects against data loss, corruption, or deletion, including mistakes made by the customer itself. Recovery Point Objective (RPO) describes how much data loss is acceptable, measured in time since the last backup, while Recovery Time Objective (RTO) describes how quickly a system must be restored after a disruption; both should be defined deliberately rather than left to a provider's default settings.
Multi-zone architectures protect against the failure of a single data center; multi-region architectures add protection against a broader regional event, at greater cost and complexity. Disaster recovery plans are only as reliable as their last successful test; an untested recovery procedure is a hypothesis, not a plan.
Common Public Cloud Use Cases
Websites, web applications, and APIs — variable traffic patterns benefit from elastic scaling and managed load balancing.
Development and testing environments — environments can be created and destroyed on demand rather than held permanently.
Data analytics and data lakes — object storage and managed analytics services handle large, growing datasets without upfront hardware sizing.
Backup and disaster recovery — off-site, geographically separated storage without maintaining a second physical facility.
AI and machine learning workloads — access to specialized hardware such as GPUs and managed model-training and inference platforms without owning that hardware.
SaaS products — providers building their own software-as-a-service offerings on top of public cloud infrastructure rather than operating data centers themselves.
Content delivery and media processing — global edge locations reduce latency for video and static content delivery.
Enterprise application modernization — moving legacy on-premises applications onto managed platforms to reduce operational overhead over time.
When Public Cloud Is—and Isn't—the Right Fit
Public cloud tends to fit organizations with variable or fast-growing demand, a need for rapid provisioning across the world, a preference for a managed-service and operating-expense model over owning hardware, and workloads under active development where experimentation matters.
Closer analysis is warranted for specialized legacy hardware dependencies, workloads with hard real-time or extremely latency-sensitive local requirements, unusual data-sovereignty constraints that limit where data may legally sit, highly stable and fully utilized existing infrastructure with already-favorable economics, specialized licensing tied to on-premises deployment, and environments that must remain disconnected or air-gapped from the internet.
Not every workload benefits equally from migration, and a single organization can reasonably run some systems in public cloud while keeping others on-premises or in a private cloud; the right answer depends on the specific workload's requirements, not on a blanket policy.
Major Public Cloud Providers: AWS vs Microsoft Azure vs Google Cloud
Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are the three largest public cloud providers by scope of service and global infrastructure footprint, and all three offer overlapping core capabilities, including compute, storage, networking, managed databases, container orchestration, serverless computing, and AI/ML platforms, while differing in service breadth, ecosystem integration, and commercial terms.
Criterion | AWS: notable considerations | Microsoft Azure: notable considerations | Google Cloud: notable considerations |
Compute and containers | Broadest range of instance types and managed Kubernetes options; long-established EC2 platform | Deep integration with Windows Server, SQL Server, and Active Directory environments | Strong managed Kubernetes heritage and container-first tooling |
Serverless | Long-established Lambda platform with broad event-source integrations | Azure Functions integrated with the wider Azure ecosystem | Cloud Run and Cloud Functions with strong container-to-serverless portability |
Data, analytics, and AI | Extensive first-party analytics and machine learning service catalog | Strong integration with Microsoft's productivity and data ecosystem | Notable strength in data analytics and machine learning research heritage |
Ecosystem fit | Broad third-party marketplace and long enterprise adoption history | Often preferred by organizations standardized on Microsoft identity, productivity, and licensing | Often considered by organizations prioritizing open-source alignment and data science tooling |
Cost-management tooling | Cost Explorer and native Savings Plans | Cost Management + Billing integrated with Azure subscriptions | Recommender and committed-use discount tooling integrated with billing |
None of these considerations makes one provider universally superior; an organization heavily invested in Microsoft identity, productivity software, Windows Server, or SQL Server licensing may weight Azure integration more heavily during evaluation, while one built around open-source tooling, containers, or existing AWS-specific expertise may weight those platforms differently. Where capabilities overlap heavily, the deciding factors are usually ecosystem fit, existing skills, and commercial terms rather than any single missing feature.
How to Choose the Right Public Cloud Provider
A structured evaluation should document actual workload requirements before comparing providers: compute and storage needs, expected traffic patterns, latency sensitivity, and any specialized hardware such as GPUs. A practical selection framework covers:
Workload requirements and expected scale.
Geographic availability and latency to your users.
Compliance, certification, and data residency requirements.
Security model and how it fits your identity and access management approach.
Existing technology ecosystem and team skills.
Availability of the managed services and AI/data tooling your roadmap depends on.
Migration tooling and support for your current technology stack.
Pricing model, predictable commitments, and expected data-transfer costs.
Support plan options and contractual or commercial terms.
Tolerance for vendor lock-in and a realistic exit or portability strategy.
Proof-of-concept testing with a representative workload, real data volumes, real traffic patterns, and the organization's own application code, reveals far more than comparing marketing feature lists, since headline compute prices alone are inadequate for choosing a provider: data-transfer costs, managed-service pricing, and support terms often matter more in practice than the advertised price of a virtual machine.
Public Cloud Migration and Adoption Roadmap
Define the business outcomes the migration is meant to achieve.
Inventory existing workloads, dependencies, and data.
Classify data sensitivity and establish security and compliance requirements.
Build a cloud landing zone with baseline identity, networking, and governance.
Estimate total cost of ownership for the target architecture.
Select a migration strategy for each workload, commonly summarized as rehost, replatform, refactor, repurchase, retain, or retire.
Run a pilot migration on a low-risk workload.
Migrate remaining workloads in planned waves.
Validate performance, security, and cost against expectations.
Establish FinOps practices and cost governance.
Optimize continuously rather than treating migration as a one-time project.
Refactoring an application to be cloud-native is not automatically the superior strategy for every workload; a straightforward rehost can be the right choice when time, budget, or application stability constraints make deeper redesign impractical, and organizations often mix strategies across their portfolio.
Public Cloud Trends and What to Watch Next
Several developments are shaping public cloud in 2026 and are worth separating from speculation about where they will lead. Established developments include heavy investment by all three major providers in AI infrastructure and managed AI platforms, including specialized accelerator hardware; growing interest in confidential computing, which encrypts data even while it is being processed in memory; the continued maturity of Kubernetes and platform engineering practices for standardizing how internal teams consume cloud infrastructure; and the expansion of FinOps practice beyond cost-cutting into broader technology value management. [5]
Sovereign cloud offerings, regional infrastructure and controls designed to meet a specific jurisdiction's data-residency and legal requirements, are expanding as more governments and regulated industries require it. Predictions about how quickly AI workloads will reshape overall cloud spending, or how far edge computing will push processing away from centralized regions, remain genuinely uncertain and should be treated as open questions rather than settled facts.
FAQ
What is a public cloud in simple terms?
Public cloud is computing infrastructure that a company like AWS, Microsoft, or Google owns and runs, then rents out to many customers over the internet. You pay for what you use instead of buying your own servers, and your data stays logically separated from other customers' data even though you share the same underlying hardware.
What is an example of a public cloud?
Amazon Web Services, Microsoft Azure, and Google Cloud are examples of public cloud platforms. A hypothetical example would be an online store renting virtual servers and a managed database from one of these providers to run its website, rather than owning physical servers.
Is AWS a public cloud?
Yes. Amazon Web Services is a public cloud platform: Amazon owns and operates the infrastructure and makes compute, storage, and other services available to any customer on a pay-as-you-go basis.
Is Microsoft Azure a public cloud?
Yes. Microsoft Azure is Microsoft's public cloud platform, offering infrastructure, platform, and software services over the internet on a consumption-based pricing model, alongside options for hybrid deployments that connect to a customer's own infrastructure.
Is Google Cloud a public cloud?
Yes. Google Cloud is Google's public cloud platform, providing compute, storage, data analytics, and machine learning services to customers worldwide, with consumption-based pricing similar to AWS and Azure.
What is the difference between public cloud and private cloud?
Public cloud infrastructure is owned by a third-party provider and shared among multiple customers, while private cloud infrastructure is dedicated to a single organization, whether hosted on that organization's own premises or by a third party on its behalf. Private cloud generally offers more direct control at a higher fixed cost; public cloud offers elasticity and lower upfront investment.
What is the difference between public cloud and hybrid cloud?
Public cloud refers to shared, provider-owned infrastructure used on its own. Hybrid cloud refers to an architecture that connects private infrastructure with public cloud, letting workloads and data move between the two. An organization can use public cloud as one component of a broader hybrid cloud strategy.
Is public cloud secure?
Public cloud can be highly secure, but security outcomes depend on configuration, identity controls, and monitoring, not on the deployment model alone. Under the shared responsibility model, the provider secures the underlying infrastructure, while the customer secures identity, network configuration, and data within its own environment.
Is public cloud cheaper than on-premises infrastructure?
It depends on the workload. Public cloud often lowers upfront capital costs and can reduce total cost for variable or unpredictable workloads, but steady, highly utilized on-premises infrastructure can sometimes be more cost-effective over its useful life. A realistic comparison requires total cost of ownership, not just sticker prices.
What are the main disadvantages of public cloud?
Common disadvantages include unpredictable billing if usage isn't monitored, data-transfer and egress charges, the risk of misconfiguration exposing resources, a learning curve for teams new to cloud architecture, and the potential for vendor lock-in around proprietary services.
What are IaaS, PaaS, and SaaS?
IaaS (Infrastructure as a Service) provides virtualized compute, storage, and networking, with the customer managing everything above that. PaaS (Platform as a Service) also manages the operating system and runtime, so customers focus on their application. SaaS (Software as a Service) delivers a complete, ready-to-use application that the provider manages entirely.
What is the shared responsibility model?
The shared responsibility model divides security duties between the cloud provider and the customer. The provider secures the underlying cloud infrastructure, while the customer is responsible for security in the cloud: configuring identity, access, network settings, and protecting the data and applications placed there.
What are public-cloud egress fees?
Egress fees are charges for transferring data out of a provider's network, such as to the internet or to another cloud provider. They are often overlooked when estimating cloud costs and can become significant for data-heavy applications, so architectures that minimize unnecessary cross-network data movement tend to cost less.
Can public cloud meet compliance requirements?
Public cloud providers maintain certifications against many recognized standards and offer configuration options that support common regulatory requirements, but a provider's certification does not automatically make a customer's specific workload compliant. Meeting compliance obligations also requires proper configuration, data handling, and documentation on the customer's side.
How do I choose a public-cloud provider?
Start by documenting workload requirements, compliance needs, existing technology ecosystem, and team skills, then compare providers on service fit, pricing structure, data-transfer costs, and support terms rather than on headline compute prices alone. Running a proof of concept with a representative workload is usually more informative than comparing feature lists.
Key Takeaways
Public cloud means provider-owned, shared infrastructure available over the internet, not that your data is publicly accessible.
IaaS, PaaS, SaaS, and serverless represent different points on a spectrum of how much operational responsibility the provider takes on.
Security follows a shared responsibility model: the provider secures the infrastructure, and the customer secures what they configure and deploy within it.
Total cost of ownership, not the list price of a single resource, is the only realistic basis for cost comparisons.
Hybrid cloud connects private and public environments; multi-cloud means using more than one public provider, and the two solve different problems.
AWS, Microsoft Azure, and Google Cloud overlap heavily in core capability; the right choice depends on workload fit, ecosystem, skills, and terms, not on any universal ranking.
A migration plan works best when it mixes strategies, such as rehost, replatform, refactor, repurchase, retain, or retire, across a portfolio rather than applying one approach to everything.
Actionable Next Steps
Document your current workloads, their dependencies, and their compliance requirements.
Estimate total cost of ownership for a public cloud alternative, not just compute list prices.
Define your security and identity requirements before selecting a provider.
Shortlist providers based on workload fit, existing ecosystem, and required managed services.
Run a proof of concept with a representative workload before committing.
Establish cost visibility, budgets, and alerts from day one of any migration.
Design your governance model, including tagging standards and access policies.
Build a portability and exit strategy before you need one.
Glossary
Public cloud: Computing infrastructure owned by a third-party provider and made available to multiple customers over the internet.
Private cloud: Computing infrastructure dedicated to a single organization, whether hosted on-premises or by a third party.
Hybrid cloud: An architecture connecting private and public cloud environments so workloads and data can move between them.
Multi-cloud: The use of more than one public cloud provider by the same organization.
Cloud service provider: A company that owns and operates cloud infrastructure and offers it to customers.
IaaS: Infrastructure as a Service; the provider supplies virtualized compute, storage, and networking, and the customer manages the rest.
PaaS: Platform as a Service; the provider also manages the operating system and runtime, beyond IaaS.
SaaS: Software as a Service; a complete, ready-to-use application managed entirely by the provider.
Serverless: A computing model where the provider manages servers entirely and the customer supplies only application code.
FaaS: Function as a Service; a serverless model where code runs in response to individual events.
Virtual machine: A software-based emulation of a physical computer, running its own operating system.
Container: A lightweight, portable unit that packages an application and its dependencies, sharing the host OS kernel.
Kubernetes: An open-source system for automating deployment, scaling, and management of containerized applications.
Multitenancy: An architecture where multiple customers share the same underlying infrastructure with logical isolation.
Region: A geographic area containing multiple, physically separate data centers operated by a cloud provider.
Availability zone: One or more discrete data centers within a region, each with independent power and networking.
VPC / VNet: A private, software-defined network a customer configures within a provider's shared infrastructure.
Elasticity: The ability to automatically scale resources up or down quickly to match changing demand.
Scalability: The capacity of a system to handle growth, whether by adding more resources or larger ones.
Autoscaling: A feature that automatically adjusts the number of active resources based on real-time demand.
IAM: Identity and Access Management; controls that determine who or what can access which cloud resources.
MFA: Multi-Factor Authentication; requiring more than one form of verification before granting access.
Shared responsibility model: The division of security duties between a cloud provider and its customers.
SLA: Service-Level Agreement; a contractual commitment to a specified level of service, such as uptime.
RTO: Recovery Time Objective; the target time to restore a system after a disruption.
RPO: Recovery Point Objective; the maximum acceptable amount of data loss, measured in time.
Data egress: Data transferred out of a cloud provider's network, often subject to per-gigabyte charges.
TCO: Total Cost of Ownership; the full cost of a system over its lifetime, not just its list price.
FinOps: An operational framework and cultural practice that maximizes the business value of cloud spending.
Vendor lock-in: Difficulty or cost involved in switching away from a provider's proprietary services.
Sources & References
Mell, P. and Grance, T. "The NIST Definition of Cloud Computing." NIST Special Publication 800-145, National Institute of Standards and Technology, September 2011. nvlpubs.nist.gov/nistpubs/legacy/sp/nistspecialpublication800-145.pdf
Amazon Web Services. "Shared Responsibility Model." AWS Cloud Security, n.d. aws.amazon.com/compliance/shared-responsibility-model
Microsoft. "Shared responsibility in the cloud." Microsoft Learn, n.d. learn.microsoft.com/en-us/azure/security/fundamentals/shared-responsibility
Google Cloud. "Shared responsibility and shared fate." Google Cloud Architecture Framework, n.d. cloud.google.com/architecture/framework/security/shared-responsibility-shared-fate
FinOps Foundation. "What is FinOps?" n.d. finops.org/introduction/what-is-finops
Cybersecurity and Infrastructure Security Agency, United States Digital Service, and Federal Risk and Authorization Management Program. "Cloud Security Technical Reference Architecture." n.d. cisa.gov/resources-tools/resources/cloud-security-technical-reference-architecture-tra
Google. "FAQ (FAQPage, Question, Answer) structured data." Google Search Central Documentation, n.d. (deprecation notice added May 7, 2026). developers.google.com/search/docs/appearance/structured-data/faqpage
Amazon Web Services. "Amazon EC2 Pricing." AWS, n.d. aws.amazon.com/ec2/pricing
