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Cloud-based genomics software rarely has one monthly price. Your bill is assembled from workflow compute, temporary run storage, long-term sequence or variant storage, application credits, API operations, and data transfer. The total changes with sample type, read depth, region, retention period, retries and downloads.
For a useful budget, define the assay and sample count first, then model compute, storage and data movement separately. Published 2026 examples illustrate the range: AWS HealthOmics meters private workflows and data stores, Google Cloud Healthcare API combines storage with operations and network charges, and Illumina BaseSpace Sequence Hub uses iCredits for analysis, storage and applications.
Why there is no single cloud-genomics price
Genomics workloads are unusually variable. A 30× human whole-genome analysis can involve hundreds of gigabytes of reads, several alignment and variant-calling stages, large intermediate files and repeated quality-control jobs. An exome, targeted panel, RNA-seq library or 16S run may use a different workflow, different reference data and much less compute. Two teams can therefore process the same number of samples and receive very different bills.
Cloud providers also separate costs that may be bundled in a local bioinformatics server. A typical invoice can include:
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- Compute: virtual CPUs, memory, accelerators or specialized instances while workflow tasks run.
- Workflow and run storage: temporary file systems, containers, reference files and intermediate outputs attached to a run.
- Persistent data storage: FASTQ, BAM, CRAM, VCF, annotation databases and metadata retained after analysis.
- Operations and queries: API requests, ETL jobs, de-identification, indexing or variant-store queries.
- Data transfer: ingress, egress and transfers between regions or services.
- Application licensing or credits: packaged analysis tools may charge per run, sample or credit rather than by the hour.
There is no independent, general-purpose statistic that establishes one average price for “cloud genomics.” Vendor list prices and free allowances are useful planning inputs, not a market-wide average.
How the major pricing models work
AWS HealthOmics: metered workflows and data stores
AWS says HealthOmics has no HealthOmics licensing cost and that customers pay for what they use. Its private workflows are priced from the compute and file-system resources requested by each run. A workflow that requests more CPUs, memory or run storage costs more even if its code does not use every resource continuously. Retries and parallel tasks can increase consumption.
Ready2Run workflows use a fixed charge per run instead of exposing every underlying compute task. That model can make a validated, standard analysis easier to forecast on a per-sample basis. HealthOmics also separates sequence storage, variant and annotation storage, run storage and data transfer into their own pricing dimensions. AWS states that the service supports WDL, Nextflow and CWL and can scale to tens of thousands of tests per day; those are AWS product claims, not an independent performance benchmark.
Google Cloud Healthcare API: storage plus operations
Google Cloud Healthcare API combines several billable dimensions. Depending on the service and workflow, charges can arise from structured storage, blob storage, API requests, ETL, de-identification and network utilization. A pipeline that only archives objects has a different cost profile from one that repeatedly queries structured records or runs transformations.
Google lists Iowa structured storage at $0.000328767 per GiB-hour for usage above 1 GiB through 1,024 GiB. Blob storage above 1 GiB is listed at $0.000027397 per GiB-hour. These are regional list rates expressed per GiB-hour; multiply by the amount stored and the hours retained, then add operations and network charges. They are not a complete price for a genome analysis.
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Illumina BaseSpace Sequence Hub: iCredits
BaseSpace Sequence Hub states that storage, compute and third-party application costs are charged in iCredits. The credit system is sample-oriented, but the number of credits depends on the selected analysis application and its data requirements. Published examples include:
| Analysis | Published credit example |
|---|---|
| 30× human whole genome | 7 iCredits |
| Exome | 2 iCredits |
| Cancer panel | 2 iCredits |
| Transcriptome | 2 iCredits |
| Differential expression | 1 iCredit |
| RNA panel | 2 iCredits |
| 16S metagenomics | 1–2 iCredits |
These examples indicate relative application consumption; they do not establish a universal dollar value for an iCredit. Check the current credit price and the exact application version before converting credits into a budget.
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A defensible answer requires at least coverage, sample count, workflow, region and retention policy. “Whole genome” can mean a 30× human genome, a lower-coverage genome, a non-human genome with a different reference, or a reanalysis of existing aligned reads. Each case invokes different compute and storage.
BaseSpace publishes 7 iCredits for a 30× human whole-genome example. That is a useful planning unit inside BaseSpace, but it is not a cross-cloud dollar price. In a metered environment such as a private HealthOmics workflow, estimate the requested resources and runtime for alignment, duplicate marking, recalibration, variant calling, quality control and any retries. Then add run-file-system usage and the cost of retaining FASTQ, BAM or CRAM, VCF and intermediate files.
For a cohort, multiply the per-sample workload only after checking whether the workflow shares references, scatters tasks across samples or creates joint-calling stages. Joint genotyping, cohort quality control and annotation can add a second compute phase whose cost does not scale exactly with the number of genomes.
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How storage changes the bill
Raw and processed genomics files
FASTQ files preserve raw reads and are usually the largest durable objects. BAM files can also be large because they retain aligned reads; CRAM may reduce size when a matching reference is available, but reference management becomes part of the operational plan. VCF and gVCF files are smaller than reads but may be queried frequently or retained for many years. Intermediate sort, index, recalibration and temporary files can briefly multiply the footprint during a run.
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Active versus archive tiers
Many platforms price frequently accessed data differently from archival data. AWS HealthOmics lists separate sequence-storage tiers, while Illumina BioInsight Platform Core lists standard storage at 22.50 BIC per TB per month and archive storage at 2.00 BIC per TB per month. The archive figure lowers the storage rate but is appropriate only when access is infrequent and retrieval rules meet your workflow.
Moving a project to archive can reduce monthly storage charges while adding restore time or retrieval charges. Model the expected number of restores, the amount restored and the time you can wait before choosing an archive tier.
Published allowances and trials
AWS HealthOmics new-customer offer
AWS lists a new-customer free tier for the first two months that includes 275 omics.m.xlarge instance hours and 49,000 GB-hours of run storage. The same offer includes 1,500 gigabase-months each of active and archive sequence storage and 200 gigabyte-months of variant-store storage. These are one-time introductory allowances for eligible new customers, not a recurring monthly entitlement. Confirm eligibility, region and current terms before relying on them in a production budget.
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BaseSpace trial and account storage
A BaseSpace trial provides 250 iCredits that expire after 30 days. BaseSpace accounts include a 1 TB storage allowance; Professional and Enterprise accounts are invoiced monthly for storage above 1 TB. Treat the trial as a short evaluation window: credits that are not used before expiration cannot be assumed to carry forward.
A step-by-step method to build a realistic estimate
- Define the assay and scale. Write down whether the workload is whole genome, exome, RNA-seq, a targeted panel, transcriptome, differential expression or metagenomics. Record sample count, coverage and whether samples will be processed independently or jointly.
- Choose the workflow and output set. List the exact stages and outputs: FASTQ, BAM or CRAM, VCF or gVCF, QC reports, indexes, annotations and intermediate files. Include reference genomes and container images if they are stored in the project.
- Estimate requested resources. For each task, capture CPUs, memory, temporary disk, runtime, concurrency and expected retries. In a resource-metered service, requested capacity and run duration drive cost; in a fixed per-run service, confirm what the run price includes.
- Separate transient and durable storage. Calculate peak run storage for temporary files, then calculate retained storage by file type and month. Apply active and archive rates only after deciding how often each object will be accessed.
- Add API and query activity. Count ingestion requests, ETL, de-identification, indexing and variant or metadata queries. Google Cloud Healthcare API estimates are incomplete if these operations are omitted.
- Model data movement. Record uploads, downloads, cross-region copies and transfers to on-premises systems. Repeatedly downloading a cohort for local analysis can cost more than a one-time upload.
- Apply commercial terms last. Check the current provider calculator for region, free tier, committed-use discount or enterprise agreement. Do not apply a promotional allowance to every future month.
- Validate with a pilot. Run a representative subset, capture actual task runtimes and storage growth, then update the estimate before launching the full cohort. A pilot is an estimate refinement, not a guaranteed price.
Per-sample, per-run or per-hour: which model fits?
| Pricing style | How the bill is formed | Best budgeting use |
|---|---|---|
| Per-sample or per-run credits | A published number of credits is consumed by an application or run. | Routine assays with a stable, validated pipeline and predictable sample volume. |
| Metered compute and run storage | Requested resources, runtime, temporary storage and retries are charged separately. | Custom workflows, variable coverage and research pipelines that need resource control. |
| Storage and operation metering | Stored GiB-hours, API requests, transformations and network use are billed. | Repositories, clinical data services and workloads with frequent queries or ETL. |
A per-run price is not automatically cheaper. It may include a narrow application scope while a custom workflow gives you control to reduce resources or delete intermediates. Conversely, an hourly model can become unpredictable when jobs retry, wait for capacity or retain large temporary disks.
Costs that are easiest to miss
- Intermediate retention: failed or superseded runs can leave large temporary objects behind.
- Retries: a task that fails late may consume compute and storage again when rerun.
- Concurrency: processing many samples at once shortens elapsed time but increases simultaneous resource use.
- Reference duplication: copying genome references into multiple regions or projects creates additional storage.
- Cross-region workflows: moving reads, results or backups between regions can add network charges and compliance complexity.
- Frequent downloads: exporting BAM or CRAM files repeatedly to local storage can create recurring egress costs.
- Query-heavy services: structured genomic records may incur operation charges even when stored volume is modest.
- Credit expiration or overage: trial credits can expire, while storage above an account allowance can be invoiced monthly.
Ways to control cloud-genomics spending
Set lifecycle rules
Define when temporary files are deleted, when active data moves to archive and when projects are closed. Apply the policy by file class so that raw reads and final clinical results are not deleted with disposable intermediates.
Right-size tasks and parallelism
Start with measured runtimes from a representative pilot. Request enough memory and disk to avoid failures, but do not reserve the largest instance for every task. Limit concurrency when storage or egress, rather than compute, is the bottleneck.
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Choose a region that satisfies data-residency requirements and keep reads, references and workflow outputs in that region where possible. A cheaper compute region can become more expensive after repeated cross-region transfers or compliance controls.
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Track spend by project and sample
Use separate projects, tags or cost-allocation labels for studies, then divide total cost into compute, temporary storage, durable storage, operations and transfer. This reveals whether a pipeline is expensive because of runtime or because old data is never retired.
Compliance and procurement considerations
Price is only one selection criterion for genomic data. Compare supported workflow languages and applications, audit logging, identity controls, encryption, regional availability, retention guarantees and the contract terms required for protected health information. A platform that meets a clinical governance requirement may cost more than a generic object store but reduce validation and operational work.
Ask vendors how they define a billable run, whether failed runs consume credits, how archive retrieval is charged, what support is included and whether prices differ by region. Request a written quote for enterprise commitments rather than assuming public list prices apply to negotiated contracts.
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Use one row per assay or cohort and record:
- Sample type, coverage and sample count.
- Workflow name, version and analysis applications.
- Estimated compute hours by task and requested resources.
- Peak temporary storage and retained FASTQ, BAM/CRAM, VCF and intermediate volumes.
- Active and archive retention in months.
- Expected API, ETL, de-identification and query activity.
- Uploads, downloads and cross-region transfers.
- Applicable free-tier allowance, credit balance, discount or contract rate.
Calculate compute, run storage, durable storage, operations and transfer as separate subtotals. Then add a contingency for retries and unexpected reanalysis. Keeping the categories separate makes it possible to change a retention policy without rebuilding the entire estimate.
What a credible quote should include
Before committing to a platform, require a quote or calculator output that identifies region, currency, billing unit, included allowances, minimum storage duration, archive retrieval terms, data-egress rates and support or enterprise fees. Ask for an example invoice for your expected sample mix, not just a headline per-run number.
Finally, distinguish a vendor’s product claim from an independently measured result. AWS’s statements about predictable cost per sample and scale describe the intended HealthOmics model; Illumina’s iCredit examples describe its applications; Google Cloud’s rates describe specific Iowa storage tiers. None is a universal price for every cloud-genomics workflow.
Bottom line
Budget cloud-based genomics software as a metered data-and-compute service, not as a single subscription. Start with assay, coverage and sample count; price the workflow resources; add active and archive storage; include API operations and every expected transfer; then apply current regional rates, free tiers and contract discounts. The published examples provide useful anchors—7 iCredits for a 30× human whole genome in BaseSpace, AWS’s two-month introductory allowances, Google Cloud’s Iowa GiB-hour storage rates and Illumina BioInsight’s standard and archive BIC rates—but your retention policy and workflow design will determine the actual bill.
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