The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Neither cloud object storage nor on-premises storage is the best choice for every large scientific dataset. Choose based on how fast data is growing, where analysis and collaborators are located, how often data will be read or moved, what retrieval delays are acceptable, and who can operate and govern the system. Cloud can scale and put data close to cloud-based analysis; local storage can suit sustained, data-intensive work near existing infrastructure. A hybrid design is also possible. In every case, storage location is only one part of the decision: it does not by itself provide preservation, curation, or repository services.
Start with the data lifecycle, not the storage label
Before comparing systems, map how a dataset moves from creation through analysis, sharing, retention, and eventual deposit or deletion. A large dataset that is read continuously by local compute has different needs from one that is accessed occasionally by geographically distributed collaborators. The same project may have both patterns at different stages.
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- Volume and growth: Record current capacity, expected growth, peak capacity needs, and how quickly local capacity can be expanded.
- Access pattern: Estimate how often data are read, by how many users or jobs, and whether access is routine, bursty, or rare.
- Location of users and compute: Identify where instruments, researchers, collaborators, and analysis systems run. Moving data between storage and compute can add time and cost.
- Performance: Specify required throughput, latency, concurrency, file-size mix, and application behavior. A system’s nominal capacity alone does not establish how it will perform for a particular workflow.
- Retention and governance: Define access restrictions, applicable agreements and policies, retention periods, deletion rules, security controls, and any location requirements.
- People and operations: Identify who will configure access, monitor use and cost, maintain local hardware or cloud accounts, protect copies, and respond to failures.
NIH STRIDES recommends weighing cloud readiness of the tools, variation in workload, collaborator locations, available on-premises infrastructure, transition and service budgets, and staff capacity for provisioning and monitoring. Its cloud-fit guidance is a useful decision checklist, not a guarantee that one model will be cheaper or more suitable for a specific institution.
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How cloud object storage and on-premises storage compare
| Decision area | Cloud object storage | On-premises storage | What to establish |
|---|---|---|---|
| Capacity and growth | Capacity can scale with demand; costs grow with use. | Capacity is limited by installed equipment until the system is expanded. | Current volume, growth rate, peaks, and expansion lead time. |
| Lifecycle cost | Storage, requests, retrieval, outbound transfer, support, and administration may contribute. | Equipment, refresh, power, space, networking, protection, operations, and staff contribute. | Model realistic access, transfers, retention, and operating needs over the project lifetime. |
| Access and sharing | May be convenient for distributed collaborators and data analyzed in cloud environments. | May be convenient for local users, instruments, and compute. | Where users and compute are, and how much data must move between them. |
| Performance | Depends on network, service, client, workload, and selected storage tier. | Depends on the system purchased and how it is operated; local access can be practical for nearby workloads. | Throughput, latency, concurrency, file sizes, and application requirements. |
| Operations | The provider operates underlying infrastructure, but the customer still configures access, monitors cost, and stewards data. | The institution manages hardware, software, capacity, protection, and refresh. | Available skills, accountability, and support coverage. |
| Governance and preservation | Must meet applicable data-use, security, institutional, and funder requirements; a cloud account is not an archive. | Also needs suitable security and access controls; local control is not preservation by itself. | Classification, agreements, access and audit rules, retention, repository suitability, and an exit plan. |
The comparison is conditional, not a claim that cloud is inherently faster, safer, or less expensive. NIH STRIDES notes that cloud can reduce the need for capital expenditure and scale with demand, while also bringing substantial operating costs; local resources often have higher upfront costs and lower marginal costs but fixed capacity. Actual results depend on utilization, workload, and pricing. See NIH STRIDES’ cloud advantages and caveats.
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Compare full lifecycle cost, including data movement
For cloud object storage, the amount of data stored is only one part of the bill. Depending on the service and usage, requests, retrieval from a tier, outbound data transfer, support, and administration can also matter. A project that stores a great deal but rarely retrieves it may have a different cost profile from one that repeatedly reads or exports the same data. NIH STRIDES specifically warns that egress charges can become expensive for large volumes and recommends comparing providers’ charges when data may need to move out.
For on-premises storage, include initial equipment and deployment as well as later refreshes, power, space, networking, backup or other protection, maintenance, and staff time. Existing infrastructure may change the economics: NIH STRIDES describes a continuously used local system analyzing local data as a case where amortized hardware can have lower total cost. That is a conditional example, not a universal cost result.
Ask for current quotes and model more than one realistic scenario: expected storage growth, frequent versus infrequent reads, likely transfer volumes, retention duration, and the cost of moving data if the project or provider changes. No universal cost-per-terabyte or egress figure can settle this comparison across providers and workloads.
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Match access tiers and transfer plans to how data are used
Frequently accessed data generally call for a tier with fast access. Data that are rarely used may be candidates for a lower-cost archival or cold tier if the project can tolerate retrieval delay and any associated retrieval or access charges. Tier names, delays, and fees vary by provider, so confirm them for the exact service rather than assuming that “cold” means the same thing everywhere.
A 2020 NIH notice about the Sequence Read Archive (SRA) distinguished immediately accessible hot storage from cold storage that might not be immediately accessible and could incur platform-dependent thaw or access charges. This is a historical description of the SRA planning context, not a statement of current SRA implementation or a universal description of all cloud tiers. See the NIH SRA cloud resources notice.
Include the path from storage to compute in the design. If analysis runs outside the storage environment, repeated reads or large exports can add network time and transfer expense. Conversely, placing data near cloud-based analysis or distributed collaborators may reduce some movement. Measure the workflow that matters: network conditions, client software, job concurrency, and data layout all affect actual performance.
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Check workload and tool compatibility
Object storage is not automatically a drop-in replacement for a local file system. Confirm that analysis tools, pipelines, and instruments can use the chosen storage interface, or determine what reconfiguration is needed. Also check whether the workflow depends on shared-file behavior or low node-to-node latency. NIH STRIDES cautions that cloud may not suit some very high-performance workloads with that latency requirement, while emphasizing that fit depends on the use case.
On-premises systems can offer a practical local path when compute, instruments, and data are co-located, but performance and reliability still depend on the system selected and the institution’s ability to operate it. Compare the end-to-end workload rather than relying on a storage medium or location as a proxy for performance.
Apply governance rules to the specific dataset
Cloud storage does not transfer responsibility for research data governance to a provider. Check data-use terms, institutional requirements, funder conditions, security expectations, and any applicable residency, access, audit, and deletion rules before selecting a service. On-premises storage also needs appropriate controls and accountable operators.
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- Subscription-Free Personal Cloud – Store, back up, and manage all your videos, music, and photos and access them anytime without paying any monthly fees.
- Storage Purpose-Built for Data Security – A NAS designed to keep your data safe, the LS200 features a closed system to reduce vulnerabilities from 3rd party apps and SSL encryption for secure file transfers.
- Back Up Multiple Computers & Devices – NAS Navigator management utility and PC backup software included. NAS Navigator 2 for macOS 15 and earlier. You can set up automated backups of data on your computers.
NIH GDS controlled-access data
For controlled-access genomic and associated phenotypic data covered by the NIH Genomic Data Sharing Policy, NIH’s 2015 notice says investigators may request permission to use public or private cloud systems. The Data Access Request must request cloud use, identify the provider or providers, and describe the intended use. The notice also says the system must meet NIH security best practices and institutional IT requirements, with institutional officials and approved personnel remaining responsible for protecting the data. These instructions are scoped to the data and policy named in the notice; verify current NIH requirements and other applicable rules for a particular project. See NIH NOT-OD-15-086.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a repository for preservation and sharing
A bucket or file system, whether local or cloud-based, does not by itself provide curation, a persistent identifier, discoverability, access policy, or long-term stewardship. NIH says, “Data uploaded into the cloud are not automatically FAIR.” Storage design should therefore be paired with a decision about where the dataset will be deposited and how it will be documented, protected, retained, and made accessible as appropriate.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →NIH advises researchers to use repositories required by relevant policy or funding opportunities. If none is prescribed, it gives priority to repositories specific to the discipline or data type, with generalist and institutional repositories as other options. Repository criteria include persistent identifiers, sustainability, metadata, curation and quality assurance, access and reuse guidance, security and integrity, confidentiality, common formats, provenance, and documented retention. NIH notes that large datasets may benefit from cloud-based repositories for access, preservation, and sharing; the repository’s stewardship practices—not cloud hosting alone—are the relevant distinction. See NIH repository-selection guidance.
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That guidance identifies PubMed Central supplementary material as an option for small datasets up to 2 GB when no appropriate discipline- or data-type-specific repository is available. This is a limit for that particular option, not a general definition of a large dataset.
When a hybrid design makes sense
A hybrid arrangement can keep active data near the compute that uses it while placing less-accessed copies or other lifecycle stages elsewhere. For example, a team might use local storage for an instrument or latency-sensitive analysis workflow and cloud capacity for distributed access or a repository service. A hybrid plan also creates operational work: teams need clear rules for which copy is authoritative, how copies stay consistent, who pays transfer costs, and what happens when a copy is deleted or retained.
The SRA is a dated example of the idea, not a blueprint to copy. NIH’s 2020 notice said the SRA had been copied to Google Cloud Platform and Amazon Web Services in 2019 while remaining accessible from NCBI on-premises storage. The notice reported nine million SRA records in 2019 and discussed a proposed hot/cold hybrid model and plans under consideration. Those statements describe the notice’s historical context; they do not establish current deployment. See the NIH notice.
Quick Recap
A practical way to make the decision
- Inventory the dataset: Record present and projected volume, file characteristics, access frequency, user locations, retention, and governance classification.
- Map the workload: Identify where instruments and compute run, how much data is read or transferred, required throughput and latency, and whether tools support the intended storage interface.
- Define service expectations: Decide which data must be immediately available, what retrieval delay is acceptable for infrequently accessed data, and how sharing or recovery should work.
- Build comparable lifecycle estimates: Request current cloud quotes for storage, requests, retrieval, outbound transfer, and support; estimate local equipment, expansion, refresh, power, space, protection, networking, and staffing.
- Validate governance and preservation: Confirm institutional and funder requirements, assign accountable operators, select an appropriate repository where required or suitable, and document retention and exit plans.
- Compare cloud, local, and hybrid scenarios: Use the same workload and retention assumptions for each, then choose the design the institution can operate and fund while meeting the project’s access, performance, and stewardship needs.
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