Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources — networks, servers, storage, applications and services — that can be rapidly provisioned and released with minimal management effort. This definition follows the influential model published by the US National Institute of Standards and Technology (NIST Special Publication 800-145), which remains the standard reference for what does and does not count as cloud.
For research, the appeal is straightforward: scalable compute and storage without owning hardware, accessible from anywhere, and paid for as used. But cloud also introduces reproducibility, cost and governance considerations that researchers must plan for deliberately.
The five essential characteristics
The NIST model defines five characteristics that distinguish genuine cloud computing from ordinary remote servers. On-demand self-service lets users provision resources automatically. Broad network access makes them available over standard networks. Resource pooling serves multiple tenants from shared infrastructure. Rapid elasticity allows capacity to scale up or down quickly to match demand. Measured service meters usage transparently, enabling pay-as-you-go billing. A platform missing these — a single rented server, say — is hosting, not cloud.
Service models: IaaS, PaaS and SaaS
Cloud services are commonly grouped into three service models that differ in how much the provider manages versus the user. Choosing the right level shapes control, effort and reproducibility.
| Model | What the provider manages | What you manage | Research example |
|---|---|---|---|
| IaaS (Infrastructure as a Service) | Physical hardware, virtualisation, networking | Operating system, runtime, application, data | Virtual machines for a custom analysis pipeline |
| PaaS (Platform as a Service) | Hardware plus OS, runtime and middleware | Application code and data | Managed notebook or database service |
| SaaS (Software as a Service) | Entire stack including the application | Configuration and your data | A hosted survey or reference-management tool |
A useful rule of thumb: as you move from IaaS to SaaS, you trade control and configurability for convenience and reduced operational burden. Reproducible research workflows often favour IaaS or PaaS, where the computational environment can be captured and versioned.
Cloud’s role in research computing and data
Cloud computing has reshaped research computing by lowering the barrier to large-scale analysis. A team can spin up a cluster for a week-long genomics run and release it afterwards, paying only for what they used. Cloud storage hosts large datasets close to the compute that processes them, and managed services reduce the systems-administration overhead that once consumed researcher time. Many funders and institutions now run or subscribe to cloud-based data infrastructure for exactly these reasons.
Cloud also supports reproducibility when used well. Infrastructure-as-code, container images and environment specifications let others recreate the exact computational setup behind a result. This complements broader good practice in capturing and describing computational methods, and aligns with the goals of standardised description and discovery promoted across the CASRAI dictionary.
Cost and governance considerations
Elasticity cuts both ways. Pay-as-you-go can be economical for bursty workloads but expensive for sustained ones, and unmonitored resources can accrue surprising costs. Data egress charges — fees to move data out of a provider — can dominate budgets for data-heavy projects. Governance questions also matter: where data physically resides affects legal and ethical obligations, particularly for sensitive or personal data, and vendor lock-in can make migration costly. Researchers should plan data management, budgeting and exit strategies before committing, and should record provider, region and configuration alongside other metadata so collaborators and reviewers understand the environment. Guidance on documenting outputs is available in our resources for authors.
Frequently asked questions
What is the difference between cloud computing and a remote server?
Cloud computing meets the five NIST characteristics: on-demand self-service, broad network access, resource pooling, rapid elasticity and measured service. A single rented remote server lacks elasticity and self-service provisioning, so it is hosting rather than cloud computing in the formal sense.
Which service model should a research project use?
It depends on the control you need. IaaS gives maximum control over the environment and suits custom, reproducible pipelines. PaaS reduces operational burden for application-focused work. SaaS is simplest when a ready-made tool already meets the need and the environment need not be captured.
Does cloud computing help reproducibility?
It can. Capturing environments as infrastructure-as-code or container images lets others recreate the exact setup behind a result. But reproducibility is not automatic — it requires deliberately versioning and sharing those specifications alongside data and code.
What are the main governance risks?
Key risks include unexpected costs (especially data egress), data residency and sovereignty constraints for sensitive data, and vendor lock-in. Address them with budgeting, a data-management plan, clear records of region and configuration, and a documented exit strategy.
