Neither cloud nor on-premises storage is universally better for large biological datasets. On-premises systems can suit steady workloads and fast local access; cloud storage can suit variable capacity, collaboration, and analysis performed near cloud-hosted data. A hybrid setup can stage new data locally, then transfer selected files to cloud storage. Choose based on how data is used and moved, where computing happens, retention and recovery needs, governance, and the people and budget available to operate the system.
How the options differ for biological data
“Cloud” and “on-premises” describe where storage is operated, not a complete architecture. A lab may keep sequencer output on local storage temporarily, store working datasets on a local system, use cloud object storage for collaboration, or combine these approaches. The important question is where data needs to be at each stage of its lifecycle—and what it takes to get it there.
| Decision factor | On-premises storage | Cloud storage |
|---|---|---|
| Workload and capacity | Can suit predictable, steady use when the institution can operate and maintain capacity for it. | Can expand for temporary analysis peaks without requiring the lab to size all local infrastructure for peak demand. |
| Data access | Can provide fast access to data already local to instruments, users, or compute. | Can support shared access and analysis near cloud-hosted datasets, subject to service configuration and network access. |
| Data movement | May avoid repeated transfers for local workflows, but does not by itself solve off-site collaboration or backup. | Uploads, retrievals, and downloads take time and may incur charges; costs depend on the service and usage. |
| Lifecycle and retention | Capacity must be provisioned and managed for the files the lab retains. | Storage tiers and lifecycle policies can match different access patterns, but archive retrieval may not be immediate. |
| Operations | The institution operates and supports the infrastructure, including maintenance and recovery. | The institution still manages accounts, access, costs, monitoring, and its responsibilities for data oversight. |
These are architectural trade-offs, not guaranteed performance or price results. Actual costs and access depend on the institution’s setup, provider terms, data volumes, and workflow.
When on-premises storage makes sense
Local storage is a strong candidate when data is read or written frequently by nearby instruments or compute, demand is relatively steady, and the institution has the staff and facilities to run the system. NIH STRIDES notes that a local system with high, continuous utilization may have lower total cost over time as hardware is amortized. That observation is not a blanket finding that local storage is cheaper: purchase, operation, capacity planning, backup, and refresh costs all matter.
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Local staging can also protect an instrument workflow from a network outage. AWS’s genomics reference architecture writes sequencer output to local on-premises storage before transferring it to Amazon S3, allowing sequencing to continue during an outage if local capacity is available. This is one provider’s architecture example, not a universal requirement. A lab relying on staging needs enough usable space for expected output and a plan to resume transfers and handle accumulated files after a disruption.
When cloud storage makes sense
Cloud can be useful when capacity needs rise and fall across projects, when collaborators need access to shared datasets, or when analysis can run close to data already hosted in the cloud. NIH STRIDES identifies temporary expansion of computing resources and access to datasets already in the cloud as potential benefits. These advantages depend on the particular service, configuration, network, and workflow.
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Do not compare cloud and local storage using the per-terabyte storage rate alone. Include data transfer, retrieval, and egress costs, as well as the location of compute. NIH STRIDES cautions that large downloads can make egress expensive. If a dataset must repeatedly move between cloud storage and local compute, both transfer time and charges can change the economics. Running analysis near the cloud copy may reduce the need for those downloads, but the lab must account for the compute and other service costs involved.
Why a hybrid workflow can be practical
A hybrid design assigns different jobs to local and cloud systems. For example, a lab might stage instrument output locally, transfer selected files to object storage, and apply lifecycle rules to data that is accessed less often. This can preserve local access for active work while making other datasets available for shared or cloud-based analysis.
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The split should follow the data’s actual use rather than a simple rule that all raw data belongs in one place and all derived data in another. Before moving files to a lower-access tier or removing a local copy, determine who may need them, how often they are accessed, how quickly archived data must be restored, and what recovery copies are required.
How to make the decision
- Describe the workload. Estimate current and expected data volume, write and read throughput, access frequency, and whether demand is steady or concentrated in project peaks.
- Map data movement. Record where instruments write files, where analysis runs, who needs access, and how much data would be uploaded, retrieved, or downloaded. Include network capacity and transfer time, not only transfer fees.
- Set lifecycle and recovery needs. Specify how long raw, intermediate, and derived data must be retained; how quickly less-used data must be restored; and the recovery time and recovery point objectives for important workflows.
- Check governance before migration. Review the applicable data-use terms, institutional policies, access controls, encryption and key-management requirements, audit needs, and any data-location constraints.
- Account for operations. Identify who will manage local infrastructure or cloud accounts, monitor usage and costs, maintain backup and recovery, and respond to incidents. Include the skills and ongoing effort needed for each option.
- Compare the full workload cost. For local systems, account for hardware, utilization, operations, and recovery. For cloud, account for storage tier, compute location, transfer, retrieval, and egress. Use the institution’s real workload and current provider terms; there is no neutral, universal price comparison established here.
- Choose a boundary and test it. Decide which data stays local, which is copied to cloud, and what triggers tier changes or deletion. Validate transfer, access, restoration, and outage procedures with representative data before depending on the design.
Security and NIH controlled-access genomic data
Storage location alone does not establish that a workflow meets its obligations. For NIH controlled-access data, NIH policy guidance says cloud providers or third-party IT systems used to store or analyze the data are expected to meet applicable security best practices, while the institution remains responsible for oversight. NIH’s requirements page was last updated September 25, 2026. Check the current data-use terms and institutional controls before migration; requirements can depend on the dataset and agreement.
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For either architecture, translate governance requirements into operational controls: who is allowed access, how access is managed and audited, how encryption keys are handled, and how backup and recovery are protected. Confirm that the selected service or system and the institution’s procedures meet the applicable requirements rather than assuming one location is inherently compliant.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the available guidance does—and does not—establish
AWS guidance for genomics describes Amazon S3 as an option for file-based datasets and recommends planning for storage cost, transfer performance, and lifecycle management. It advises selecting access tiers and lifecycle policies based on observed access patterns and describes local staging and file interfaces for tools that require them. These recommendations are AWS-specific; compare equivalent services and current terms if considering another provider.
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Neither the NIH STRIDES guidance nor the AWS architecture example establishes a neutral, current, like-for-like cost or performance winner across cloud providers and institution-owned systems. A sound recommendation requires the lab’s data volume, throughput, access pattern, transfer volume, retention period, compute location, staffing, security requirements, and recovery targets.
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