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Neither cloud object storage nor on-premises storage is the right choice for every large scientific dataset. Compare the full data lifecycle: how quickly and how often data must be accessed, where analysis runs, how much data moves, what governance requires, and who can operate the system. Cloud can scale and put data near cloud-based analysis; local storage can suit sustained workloads close to users and instruments. A hybrid design can combine them, but none of these storage locations alone provides long-term preservation.
What is the practical difference?
Cloud object storage keeps data as objects in a provider-managed service, accessed over a network and often offered in tiers with different access characteristics. On-premises storage is infrastructure operated at the institution, such as a shared storage system or a local cluster’s storage. The comparison is not simply “provider versus hardware”: the institution still configures cloud access, monitors costs, and manages data stewardship, while local infrastructure requires equipment, capacity planning, protection, and ongoing operations.
These approaches can coexist. A research group might keep frequently analyzed data near its compute, place less frequently accessed data in a lower-cost tier, and deposit a preservation copy in an appropriate repository. The right arrangement depends on the workflow rather than dataset size alone.
Compare the choices across the data lifecycle
| Decision area | Cloud object storage | On-premises storage | What to establish |
|---|---|---|---|
| Capacity and growth | Can scale with demand; costs can grow as use expands. | Capacity is limited by installed equipment until the system is expanded. | Current volume, growth rate, peak demand, and expansion lead time. |
| Lifecycle cost | Storage, requests, retrieval, outbound transfer, support, and administration may all contribute. | Equipment, refresh, power, space, networking, staffing, protection, and operations contribute. | Costs over the project’s full retention period, using realistic access and transfer patterns. |
| Access and sharing | May suit distributed collaborators and data analyzed on cloud compute. | May be convenient for local users, instruments, and analysis. | Where users and compute are, and how much data must move between them. |
| Performance | Depends on the network, service, client, workload, and storage tier. | Depends on the system purchased and operated, and its connection to users and compute. | Required throughput, latency, concurrency, file-size mix, and application behavior. |
| Operations | The provider operates underlying infrastructure; the customer still configures access, monitors use and cost, and manages data. | The institution manages infrastructure, software, capacity, protection, and refresh. | Available staff skills, support coverage, and clear operational accountability. |
| Governance and preservation | Must meet applicable data-use, security, institutional, and funder requirements; a storage account is not by itself a preservation program. | Also requires appropriate access controls and security; local control is not by itself preservation. | Data classification, agreements, residency, audit, retention, deletion, repository suitability, and exit planning. |
NIH STRIDES recommends assessing whether tools are cloud-ready, whether workloads vary, whether collaborators are distributed, whether suitable local infrastructure exists, and whether the institution has budget and accountable staff for cloud transition and ongoing service use. Its guidance describes local systems as often having higher upfront cost and lower marginal cost, with fixed capacity, while cloud can scale but may cost more depending on use and pricing. These are conditional trade-offs, not a universal cost ranking. See NIH STRIDES guidance on whether cloud fits a research workload.
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Is cloud storage cheaper for large datasets?
There is no reliable universal price-per-terabyte answer. A cloud estimate that counts only stored capacity misses charges that can depend on requests, retrieval, outbound transfer, support, and administration. A local estimate that counts only the purchase price of disks misses refresh cycles, power, space, networking, backup or other protection, and staff time. Compare complete costs for the period the data must be kept, not just the first month or the initial purchase.
Model more than one realistic usage pattern: the active analysis period, expected collaborator access, occasional reanalysis, and the less-frequent access that may follow. Include the likely volume and direction of transfers. NIH STRIDES warns that egress—data transferred out of a cloud service—can become expensive for large volumes and recommends comparing providers’ egress charges when data may move out. Obtain current, workload-specific quotes; the available guidance does not establish a comparable universal cost or egress rate.
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How access frequency, retrieval, and data movement affect the choice
Frequently accessed data
Data used repeatedly for analysis need a storage path and tier that meet the workflow’s access and performance requirements. If compute runs in the cloud, keeping data near that compute may reduce repeated movement; if analysis is local and continuous, an institution’s well-utilized local system may be practical. Measure the application’s actual needs rather than assuming a storage label guarantees performance.
Infrequently accessed or archival data
A lower-cost cold tier may involve slower retrieval or an extra access or thaw charge. Do not put data there without checking the service’s current retrieval behavior, charges, and the time researchers can tolerate waiting. NIH’s Sequence Read Archive notice described hot storage as immediately accessible and cold storage as potentially slower and subject to platform-dependent access or thaw charges; that description is from a 2020 planning notice, not a statement of current SRA implementation. See the NIH SRA cloud resources notice.
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Transfers between storage and compute
Moving large datasets can add both network time and cost. Map where each stage of the workflow runs—instrument, preprocessing, primary analysis, collaboration, and later reanalysis—and estimate how much data crosses each boundary. NIH STRIDES notes that cloud may be unsuitable for some very high-performance workloads requiring low node-to-node latency. Conversely, for a sustained workload that analyzes local data continuously, local hardware costs may be amortized over high utilization. Neither observation guarantees an outcome for a particular institution or workload; validate throughput, latency, concurrency, and total cost in context. See NIH STRIDES discussion of cloud advantages and limitations.
Cloud storage does not transfer governance responsibility
Cloud suitability depends on the data-use conditions and the institution’s ability to apply required controls. Before selecting a location, establish the data classification, approved users, access controls, audit needs, applicable agreements, residency constraints, retention and deletion rules, and who is responsible for each control. Cloud services may require staff to configure and monitor those controls; an institution’s local system also needs appropriate security and access management.
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For controlled-access genomic and associated phenotypic data covered by the NIH Genomic Data Sharing Policy, NIH’s 2015 notice says an investigator may request permission to use a public or private cloud system. The Data Access Request must request cloud use, name the provider or providers, and describe the intended service use; the system must meet NIH security best practices and institutional IT requirements. This rule is scoped to the policy and data it names, not all research data. Check current NIH requirements and any other applicable rules before use. See NIH NOT-OD-15-086.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Storage is not the same as preservation
A bucket or file system can hold bytes, but it does not automatically provide curation, persistent identification, discoverability, documented access and reuse conditions, or long-term stewardship. NIH says researchers should use repositories specified by relevant policies or funding opportunities; where none is specified, it gives primary consideration to repositories for the discipline or data type, with generalist and institutional repositories as other possibilities. Its repository-selection guidance notes that large datasets may benefit from cloud-based repositories for access, preservation, and sharing. Repository choice should account for persistent identifiers, sustainability, metadata, curation and quality assurance, access and reuse, security and integrity, confidentiality, provenance, formats, and retention. See NIH repository-selection guidance.
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Do not treat continued access to a cloud account or local system as a preservation guarantee. NIH STRIDES cautions that cloud resources are not guaranteed to remain available indefinitely, and says uploading data does not automatically make it FAIR (findable, accessible, interoperable, and reusable). Plan for repository deposit where required or appropriate, integrity checks, redundancy, retention, and an exit or migration path.
A practical way to choose
- Inventory the data. Record current volume, expected growth, file-size mix, required retention, and any especially large peak periods.
- Map access and compute. Identify who uses the data, how frequently they need it, where their compute runs, required performance, and acceptable retrieval delays.
- Estimate movement. List transfers between instruments, local systems, cloud services, collaborators, and repositories; estimate volumes and directions rather than assuming transfer is negligible.
- Check constraints and operations. Confirm data-use and funder rules, institutional security requirements, staff capacity, support coverage, and who will monitor access, use, and costs.
- Compare full lifecycle options. Request current cloud pricing for the expected storage tiers, requests, retrieval, transfer, and support. Compare it with local acquisition, refresh, operations, protection, staffing, and expansion costs over the same period.
- Evaluate a hybrid design where useful. Decide which data need fast access near analysis, which can tolerate delayed retrieval, and which require deposit in a suitable repository. Specify how data move between these locations and who maintains each copy.
- Validate before committing. Check the application’s compatibility and test representative transfers and access patterns where feasible. Confirm how the design handles growth, reanalysis, collaborator access, and eventual migration.
What the NIH SRA example does—and does not—show
NIH’s 2020 request for information reported that the Sequence Read Archive had been copied to Google Cloud Platform and Amazon Web Services in 2019 while remaining accessible from NCBI on-premises storage. It described a proposed hybrid approach and hot/cold placement under consideration. The notice reported nine million SRA records in 2019; that is a dated figure, not a current count. The example shows that multiple access paths and tiers can be considered together, but it does not establish the current SRA architecture or prescribe a design for other research programs.
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