Short answer: A biotech should architect cloud infrastructure for genomics, imaging, and big data around five layers: automated instrument-to-cloud ingestion, tiered object storage with lifecycle rules, elastic CPU and GPU compute that scales to zero between runs, orchestrated pipelines scientists can launch without managing servers, and compliance controls (audit trails, access logging, retention) built in from day one. Designing these layers around each data modality keeps costs predictable and results traceable at terabyte scale.
Scientific computing at scale isn’t one workload. A genomics pipeline, a cryo-EM reconstruction, and a high-content imaging screen each stress infrastructure differently. What they share is volume. Data per run is measured in terabytes, and a growing company can reach petabytes before it notices. Architecture choices made in the first year decide whether that growth stays manageable.
What are the core layers of a scientific computing architecture in the cloud?
A scientific computing architecture in the cloud has five core layers: ingestion, storage, compute, orchestration, and governance. Each layer needs to be designed for terabyte-scale data and bursty demand, not for steady business traffic.
Here’s how the layers fit together, in the order data flows through them:
- Ingestion: automated, verified transfer of raw data from sequencers, microscopes, and imaging systems to cloud storage, with checksums to confirm nothing was lost or altered.
- Storage: object storage organized by project and modality, with lifecycle rules that move data from hot to cool to archive tiers as it ages.
- Compute: elastic CPU for most pipelines and GPU capacity for imaging, structural biology, and AI workloads, scaling to zero when idle.
- Orchestration: workflow engines and managed services that let scientists launch standard pipelines without provisioning servers.
- Governance: audit logging, role-based access, encryption, retention policies, and cost tagging across every layer.
How should genomics data be stored and processed in the cloud?
Genomics data should be stored in object storage or a purpose-built omics service, with raw reads moved to archive tiers once processed and only analysis-ready files kept in hot storage. Processing should run through orchestrated, versioned pipelines on elastic compute, so results can be reproduced.
The volumes justify the planning. A 30x human genome requires more than 120 Gb of sequence data per sample, and a single NovaSeq X Plus 25B flow cell can sequence about 64 genomes in one run (Illumina NovaSeq X specifications). Services like AWS HealthOmics are built for this pattern and handle omics-specific storage and workflow execution.
How should imaging and cryo-EM workloads be architected?
Imaging and cryo-EM workloads should be architected around high-throughput storage close to GPU compute, because reconstruction and image analysis read enormous files repeatedly. Raw movies and images should move to cheaper storage tiers once processed, keeping only reconstructions and curated datasets in fast storage.
Cryo-EM is one of the heaviest workloads in the lab. A single cryo-TEM experiment can generate more than 1 to 2 TB of data per day (Histochemistry and Cell Biology, 2023). Architectures that pair a high-performance file system with GPU instances during processing, then tier data down afterward, balance speed with cost.
When should a biotech use GPUs instead of standard compute?
A biotech should use GPUs for workloads that parallelize well across thousands of cores: cryo-EM reconstruction, deep learning image analysis, protein structure prediction, and AI model training or inference. Most standard genomics alignment and variant calling still run cost-effectively on CPU.
GPU capacity is expensive, so the architecture should launch it only when a job needs it and release it immediately after. In one PTP engagement, re-architecting an AlphaFold protein structure workload on AWS made processing 12 times faster and cut costs by 86%. The gain came from matching the compute to the job, not just adding hardware.
How do you keep storage costs under control at petabyte scale?
Storage costs stay under control at petabyte scale through lifecycle policies, clear data ownership, and cost tagging by project. Every dataset should have a defined path: how long it stays in hot storage, when it moves to archive, and when (if ever) it can be deleted under the retention policy.
Without those rules, everything stays indefinitely in the most expensive tier. That’s one reason cloud waste is climbing across industries: organizations estimate 29% of IaaS and PaaS spend is wasted (Flexera 2026 State of the Cloud Report, March 2026). For a biotech storing terabytes a week, tiering is the single biggest cost lever.
How does compliance fit into a big data architecture?
Compliance fits into a big data architecture as a governance layer that spans ingestion, storage, compute, and orchestration. It isn’t a separate system added at the end. Any dataset that could support a clinical or regulatory submission needs audit trails, controlled access, and proof of integrity from the moment it leaves the instrument.
That means checksums at ingestion, immutable or versioned storage for regulated data, logged access, pipeline versioning so every result can be reproduced, and documented retention. Under 21 CFR Part 11, the infrastructure has to prove that a record hasn’t been altered outside of authorized, logged processes.
How does the architecture change as a biotech moves from preclinical to commercial?
The architecture’s layers stay the same from preclinical to commercial, but scale, compliance scope, and operational maturity grow. Early-stage companies need a fast, compliant foundation. Clinical-stage companies add validation and stricter governance. Commercial-stage companies add multi-site operations, disaster recovery, and standardization across teams.
Building the layers correctly early is what makes that growth smooth. A preclinical environment with a sensible account structure, lifecycle rules, and audit logging can grow into a clinical and commercial environment. One built ad hoc usually has to be rebuilt, often at the worst possible time.
Scientific Computing Architecture by Data Modality
| Modality | Typical data scale | Compute profile | Storage strategy |
|---|---|---|---|
| Genomics (sequencing) | >120 Gb per 30x genome; ~8 to 10.5 Tb per 25B flow cell run | CPU-heavy pipelines, bursty | Archive raw reads after processing; keep analysis-ready files hot |
| Cryo-EM | 1 to 2+ TB per day per experiment | GPU-heavy reconstruction | High-performance file system during processing, then tier down |
| High-content imaging | Large image sets per screen | GPU for deep learning analysis | Object storage with lifecycle rules by screen or project |
| AI and structure prediction | Model weights plus large reference databases | GPU, short intense bursts | Cache reference data near compute; release GPUs after jobs |
What do biotechs get wrong when architecting for scientific big data?
The most common mistake is designing storage for today’s data volume instead of next year’s. A layout that works at 50 TB becomes unmanageable at 2 PB, and reorganizing petabytes later is slow and expensive. A close second is running every job on the same instance type, which overpays for simple jobs and underpowers heavy ones.
Biotechs also skip data ownership. When no one owns a dataset’s lifecycle, nothing gets archived or deleted, and storage costs grow with no ceiling. A third mistake is letting individual scientists build one-off pipelines on hand-configured servers. That approach works for a single experiment but breaks reproducibility, and reproducibility is exactly what regulators and collaborators ask about.
The data behind scientific computing at scale
Sequencing output: a single NovaSeq X Plus 25B flow cell produces about 8 to 10.5 Tb per run at 2 × 150 bp, enough for about 64 human genomes at 30x coverage (Illumina).
Microscopy output: some cryo-EM facilities generate between 500 TB and 2 PB of data a year (Histochemistry and Cell Biology, 2023).
Cloud waste: 29% of IaaS and PaaS spend is estimated to be wasted, the first increase in five years (Flexera 2026 State of the Cloud Report, March 2026).
FAQ
How much cloud storage does a genomics-focused biotech need?
It depends on sequencing volume and retention policy, but a company sequencing regularly can reach hundreds of terabytes within a year or two. Planning should be based on genomes sequenced per month, file types retained, and how long raw data must be kept for regulatory or scientific reasons.
Should scientists run their own cloud pipelines?
Scientists should be able to launch standard pipelines themselves, but through orchestrated, versioned workflows the IT or cloud team maintains. That combination keeps scientists fast and self-sufficient while keeping results reproducible and costs visible.
Can the same cloud architecture support both research and GxP work?
Yes, with separation. A common approach isolates validated GxP environments in their own accounts, with stricter change control and access, while research environments stay more flexible. Shared governance and tagging keep both visible and manageable.
What’s the fastest way to lower an existing scientific cloud bill?
Start with storage lifecycle rules and idle compute. Moving processed raw data to archive tiers and making sure compute scales to zero between runs usually delivers the fastest savings, before any deeper re-architecture.
Related reading
- Why Life Sciences Needs a Purpose-Built IT Partner (Not Just Another MSP)
- Why Are Life Sciences Cloud Workloads So Different From Everyone Else’s?
- What Does an AWS Partnership Actually Deliver for a Biotech’s Cloud Infrastructure?
- What IT Infrastructure Does a Biotech Need Before Entering the Clinic?
- Should a Scaling Life Sciences Company Use Multi-Vendor or Single-Cloud Infrastructure?
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