BioTeam has helped organizations answer that question across pharma, biotech, clinical diagnostics, academic medical centers, and federal research for years. Some of those engagements ended in a cloud migration. Some ended with a new on-premises cluster. Most landed somewhere in between.
1. Asking the Same Question Across a Decade with a Federal Research Agency
Case: Federal research agency, research HPC, multiple engagements over more than a decade.
BioTeam has helped this agency revisit the cloud question several times. An early assessment and roadmap weighed on-premises HPC against cloud. A later analysis of alternatives modeled ten-year costs and found that owning data center capacity would cost millions less than renting it. Most recently, a pilot rebuilt a smaller version of the production cluster in a commercial cloud.
The pilot worked. It also ran into friction. Staff could not easily co-locate compute and storage in the locations with the best bandwidth back to campus, and the agency’s external network could not move all of its large datasets off-site. Cloud earned a role as a per-project supplement and a proving ground for new CPU and accelerator architectures. It did not earn the job of replacing the cluster.
Lesson: Organizations make the cloud decision more than once. Each reassessment brings new pricing and new workloads, and the answer can shift with them.
2. Making the Case on Speed, Not Savings with a Global Pharma Client
Case: Global pharmaceutical company, research computing steering committee, 2012.
The steering committee mainly wanted to know whether the cloud would save money. BioTeam found that an apples-to-apples cost comparison was not possible because business units could not see the full operational cost of internal IT services. Meanwhile, one internal HPC cluster took close to a year to move from concept through design, vendor selection, and approval.
BioTeam recommended framing cloud around speed instead: short-lived dev, test, and prototype environments, elastic scaling for variable workflows, and neutral ground for external collaboration.
Lesson: Cost is often the weakest argument for the cloud. Speed and flexibility usually make the stronger case.
3. Recommending Against Cloud Bursting with a Life Sciences Client
Case: Life sciences company, research HPC, 2018 assessment with follow-on support in 2021.
The organization wanted to know whether to build out local infrastructure or move to the cloud. Slow data transfer was already holding back cloud analysis of its nanopore sequencing data. Bursting would have meant shuttling datasets back and forth, keeping software environments in sync on both sides, and building identity management that spanned them.
BioTeam recommended building out on-premises and explicitly advised against a cloud-bursting hybrid. Cloud kept two targeted roles: data exchange with collaborators, and workloads that could run entirely in the cloud. A few years later, BioTeam supported the migration into the organization’s new HPC environment.
Lesson: Cloud bursting is simple on a diagram. In practice, data movement and environment drift can cost more than the extra capacity is worth.
4. Starting with Storage Before Compute with a Large Biotech Client
Case: Large biotechnology company, research storage roadmap within a broader infrastructure modernization program, 2015.
The goal was to expand storage services from purely on-premises to a mix of on-premises and cloud, with tiers for internally generated data. BioTeam’s roadmap sequenced the work: a cloud gateway and added NAS capacity first, then hybrid cloud HPC, and finally a single namespace spanning sites and clouds.
Some research groups expected to move entirely to the cloud eventually. Others would need fast local storage for years. The roadmap had to serve both.
Lesson: Compute moves easily; data does not. Where the data lives usually decides where the compute goes.
5. Building the Foundation Before the Use Cases with a Global Pharma Client
Case: Global pharmaceutical company, scientific infrastructure assessment for a research informatics program, 2017.
The organization wanted a hybrid architecture for elastic computing and collaboration across its research groups. BioTeam identified four core cloud use cases: on-demand HPC, dataset ingest and transfer, analytics software-as-a-service, and data-intensive compute frameworks.
The first recommendation came before any of them. Get network and security right, and establish a secure base in the public cloud, legal agreements included, before moving a single workload.
Lesson: Hybrid works best as one planned architecture on a solid base. A string of one-off cloud projects rarely adds up to that.
6. Balancing Collaboration and Compliance with a Clinical Diagnostics Client
Case: Commercial diagnostics company, informatics pipelines under strict security and compliance requirements, 2016.
This organization was designing a hybrid cloud environment for a globally collaborative research community, under high security and compliance standards. The company brought BioTeam in for the secure, high-speed networking and HPC infrastructure that tied the two environments together. Cloud and on-premises resources had to work as one connected research environment, and none of the controls required for sensitive clinical data could slip.
Lesson: When collaboration and compliance pull in opposite directions, the network design is where you reconcile them.
7. Separating Clinical and Research Needs with a Large Academic Medical Center
Case: Large academic medical center, HPC assessment and roadmap, 2024 to 2025.
Research teams were exploring cloud and hybrid pipelines, but some clinical software, such as treatment planning tools, was not available in the cloud at all. Researchers wanted to know how to build pipelines that could span both. BioTeam’s roadmap set a target state where cloud, on-premises HPC, and cloud-bursting resources each run the workloads suited to them.
Lesson: Research and clinical computing in an academic medical center share infrastructure but not constraints. One platform decision rarely fits both.
8. Investing On-Prem on the Way to Cloud with a High-Volume Clinical Diagnostics Client
Case: High-volume clinical diagnostics laboratory, molecular and digital pathology workflows, 2025.
A previous cloud proof-of-concept for a key clinical pipeline had failed. It added 1.5 hours to turnaround compared with the on-premises pipeline, and it was hard to deploy reliably. The organization still wanted to take advantage of the cloud, but needed an approach that met its operational requirements.
BioTeam recommended moving to a cloud-centric footprint by the end of 2028, with targeted upgrades to on-premises infrastructure in the meantime. That interim investment buys time to properly modernize and revalidate clinical workflows.
Lesson: Investing in on-prem can be part of a cloud strategy. For clinical work, it is often what makes a safe migration possible.
What Ties These Together
These eight engagements span different organizations, scientific domains, and constraints, and several ended up elsewhere rather than in the cloud. A handful of principles held across all of them.
- Workload first, platform second. We start with what the science needs (latency, turnaround, data volume, burstiness, and regulatory context) and let the platform follow.
- Honest answers, even from a cloud partner. BioTeam has been an AWS partner since 2007, and we still recommend on-premises infrastructure when it is a better fit.
- Cost alone rarely settles it. Real cost comparisons are hard to build and easy to dispute. Speed, capability, and risk often matter more.
- Data gravity. The cost of moving large datasets often answers the platform question before compute is even considered.
- Foundations make or break a hybrid. Networking, security, identity, and environment consistency are where hybrid and burst strategies succeed or fail.
- Revisit the decision. Pricing, workloads, and policy change. A good answer in 2018 may not be the right one in 2026.
