In my last post, I announced that bioAF now supports both AWS and Google Cloud. That post was about the implementation. This one is about the decision behind it.
The question I get most often is some version of “Which cloud should we build on?” And the honest answer is that it’s almost never AWS versus GCP on the merits. Both platforms run modern bioinformatics workloads without breaking a sweat. Both offer managed Kubernetes, object storage, databases, GPUs, and enough infrastructure to take you from a five-person startup to a Fortune 100 company. The real question is which one fits your team, and that’s a different question than which one has the better spec sheet.
Start with your constraints, not the feature list
Founders often spend weeks comparing instance types, storage pricing, and benchmark numbers. In my experience those are rarely the deciding factors. A shorter set of practical questions usually settles it: Is your company already invested in one cloud? Are you sitting on startup credits from AWS or Google? Are you collaborating with a university or partner that already has infrastructure on one platform? Do you have engineers who already know how to operate one of them?
If the answer to any of those is yes, you’ve probably already found your cloud. Changing providers later is far easier than asking your team to abandon years of operational knowledge they already have.
The biotech-specific factors most comparisons skip
Generic cloud comparisons apply equally to a fintech or a SaaS company. A few things genuinely differ when your workloads are genomic, and these are worth weighing before the generic stuff.
Managed genomics is where the two clouds have actually diverged. AWS HealthOmics is a HIPAA-eligible, GxP-capable managed service that runs WDL, Nextflow, and CWL pipelines and stores sequence data at petabyte scale, and it’s under heavy active development right now. Google took a different path. It deprecated Cloud Life Sciences and shut it down in July 2025, pointing users to Batch as the successor. Batch is a capable general-purpose job runner, but it isn’t a genomics-aware managed service, so on GCP you’ll generally assemble your own orchestration on Batch plus GKE. Neither approach is wrong. If a managed genomics layer matters to your team, that asymmetry should factor into the decision.
Egress will dominate your bill before instance pricing ever does. Whole genomes, imaging stacks, and cryo-EM datasets are large, and moving them around is where biotech cloud spend tends to go sideways. Data leaving a cloud, crossing regions, or bouncing between a collaborator’s environment and yours costs real money. Plan your storage and transfer architecture around keeping compute next to data, and the per-gigabyte rate differences between providers stop mattering.
Compliance is a posture, not a checkbox. If you’re heading toward clinical work, you’ll care about HIPAA Business Associate Agreements, GxP validation, and 21 CFR Part 11 controls around audit trails and electronic records. Both clouds can meet these requirements. AWS is more commonly encountered in regulated pharma environments, which mostly means more partners and prior art exist for the audits you’ll eventually face, not that GCP can’t get you there.
Google Cloud
Google Cloud tends to appeal to organizations already living in Google’s ecosystem. If your company runs on Google Workspace, sharing the same identity platform, billing account, and org structure with your infrastructure is genuinely convenient. BigQuery remains one of the best managed analytics platforms available, and Vertex AI has matured into a serious ML offering. For genomics, imaging, and other data-intensive research, those services carry real weight. Google also pushed managed services early, which can mean fewer infrastructure components for your team to babysit.
It’s far from perfect, though. The Console can be frustrating, with multiple paths to the same setting and documentation that occasionally points at pages that have moved. IAM is the more common pain point. Google’s permission model is extremely granular, which sounds great until you’re trying to build a practical least-privilege role and find yourself doing trial and error to land on the right combination. Resist the temptation to paper over that with broad admin grants, especially if compliance is in your future. The granularity is worth the effort once you’ve invested in it.
Good fit for: teams already on Google Workspace, organizations doing heavy analytics or ML, and startups that want strong managed services and don’t mind living inside Google’s ecosystem.
AWS
AWS remains the default for a large share of the industry, and the reasons hold up. The ecosystem is enormous, so whatever managed service, consulting partner, Terraform module, or blog post you need, it probably already exists. AWS also simply has more infrastructure: more availability zones per region, the largest CDN edge network, more instance and GPU types, and more total burst capacity. That last point is easy to overlook until you need hundreds of CPUs or specialized GPUs during a crunch and want to be confident the capacity is there.
AWS has historically been viewed as the cloud with the largest global footprint. That gap has narrowed considerably. Google has expanded aggressively and, depending on which 2026 tracker you read, the two are roughly even or GCP is slightly ahead. Where AWS still leads is availability-zone depth and edge presence, which is the thing that actually matters for resilience and latency.
The tradeoff is complexity. AWS gives you more choices nearly everywhere, and sometimes that means six ways to do one thing. Cost optimization takes more effort too, because the same flexibility that lets experienced teams tune spend precisely also lets newer teams overprovision expensively.
Good fit for: organizations with enterprise or pharma customers, teams expecting significant compliance requirements, and companies that want maximum flexibility and a managed genomics service to start from.
What about cost?
People love asking which cloud is cheaper, and the honest answer is that they’re much closer than most assume. Compute, storage, and managed-database pricing are all competitive enough that the difference rarely shows up in the final bill.
The largest cloud bills I’ve seen weren’t caused by picking the wrong provider. They were caused by architecture. Idle resources left running, no storage lifecycle management, unnecessary cross-region data transfers, and overprovisioned clusters dominate cloud spend far more than the gap between two pricing tables. Pick the cloud your team understands, architect it carefully, and you’ll almost certainly spend less than you would chasing a cheaper rate card on an unfamiliar platform.
A note on Azure
I’ve kept this to AWS and GCP because those are the two with supported bioAF installers, but Azure is a legitimate third option, especially if you’re already a Microsoft shop or partnering with large pharma that standardized there. It’s outside the scope of this post, but it’s not outside the scope of a serious evaluation.
My recommendation
If you’re already on AWS or Google Cloud, stay. The operational knowledge your team has is worth more than any marginal advantage the other platform offers.
If you’re starting from scratch, I’d weight it like this. Choose Google Cloud if you’re centered on Google’s ecosystem and doing analytics and ML-heavy research, and if you’re comfortable building your own pipeline orchestration. Choose AWS if you expect enterprise or pharma partnerships, anticipate real compliance requirements, or want a managed genomics service like HealthOmics to build on rather than assembling one yourself. Neither choice will keep you from building a successful biotech company.
Where bioAF fits, and where it doesn’t
I made bioAF cloud-agnostic with built-in support for both of these clouds because I don’t think your cloud provider should dictate how your scientists work. Whether you’re on AWS, Google Cloud, or something else entirely, bioAF gives you the same experiment tracking, pipeline execution, notebooks, automation, audit trail, and computational biology workflows. The infrastructure underneath changes, but the science layer your team touches every day does not.
I want to be straight about the limits of that, though, because the abstraction isn’t magic. bioAF can make your workflows, metadata, and notebooks feel identical across clouds. It can’t change your egress economics, your compliance posture, or whether a managed genomics service exists underneath you. Those are real differences, and they’re exactly the ones worth deciding on deliberately rather than by default.
There is no universally best cloud for biotech. There’s the one that fits your team, your partnerships, and your constraints. If you’re evaluating bioAF, try it on the cloud you already use. If you’re genuinely undecided, deploy it on both and see which experience fits your team better. I’d be interested to hear what you find.
Do you still need help choosing? Or do you need help setting up bioAF in your cloud of choice? We offer services and support for exactly that reason! Reach out to hello@bioaf.co to get started.