Introduction
Our “Battle of the Single-Cell Platforms” and “Battle of the Workflow Managers” articles have been some of the most popular posts on the Bridge Informatics blog. Inspired by those comparisons, we wanted to tackle another question we hear all the time: should you run your pipelines in the cloud or on a traditional on-premises HPC cluster?
At Bridge Informatics, we work with clients at all stages of their computational journey. Some don’t even know what “the cloud” means for bioinformatics. Others are deep in HPC already but want to know if cloud bursting is worth the investment. Some have pipelines that run, but costs spiral out of control. And some are seasoned experts who simply don’t have enough time to manage infrastructure on top of analysis, so they rely on us to help find the right balance.
No matter where you are, the same question eventually comes up: cloud or on-prem HPC?
If you are just here for the highlights, check out the quick comparison table below.
If you want the details, keep reading for a breakdown of costs, scalability, practicality, and when each option makes the most sense.
Quick comparison
| Option | Best for |
| Cloud HPC | Elastic scaling, bursty workloads, collaboration, pay-as-you-go flexibility |
| On-Prem HPC | Consistent workloads, long-term cost control, data-sensitive environments |
| Hybrid | Teams that want on-prem stability with cloud flexibility when needed |
Here’s a closer look at each option
Cloud HPC: Flexible but watch the costs
The cloud shines for flexibility. Need to scale up to process 10,000 samples overnight? Spin up a fleet of instances and pay only for what you use. This elasticity is especially useful for projects with unpredictable or bursty workloads. Cloud platforms also make collaboration easier, since data and pipelines can be shared across institutions without dealing with VPNs or local firewalls.
But the cloud comes with downsides. Costs can balloon if you leave instances running or if data transfer fees add up. For very consistent, heavy workloads, the cloud can actually be more expensive than on-prem hardware in the long run.
When to choose it: If your workloads are irregular, you need scalability on demand, or you are collaborating across organizations.
On-Prem HPC: Stable and predictable
Traditional HPC clusters are still the backbone of many institutions. The big advantage is predictability: once you’ve bought and set up the hardware, your costs are largely fixed. For steady workloads that run continuously, on-prem can be cheaper than cloud in the long run.
On the other hand, clusters can be inflexible. If you need more capacity, you have to wait for new hardware. Maintenance and system administration are also ongoing costs that cloud providers absorb for you.
When to choose it: If your workload is consistent and you want long-term cost control, or if you are in a setting where sensitive data must stay in-house.
Hybrid: The best of both worlds?
Many groups today take a hybrid approach: stable, consistent workloads run on-prem, while peak demand “bursts” to the cloud. This offers cost efficiency and flexibility, but requires careful planning so workflows move smoothly between environments.
When to choose it: If you already have on-prem hardware but occasionally need extra power, or if you want to gradually test cloud adoption without fully committing.
Conclusion
Just like choosing a workflow manager, deciding between cloud and on-prem HPC depends on your goals, your budget, and your infrastructure.
At Bridge Informatics, we help clients across this entire decision space. Sometimes that means introducing someone to the cloud for the first time. Sometimes it means tuning on-prem clusters for better throughput. And sometimes it means designing hybrid solutions so experts can get the best of both worlds without burning out managing infrastructure. Wherever you run your pipelines, our job is to make sure they are efficient, reproducible, and built for the scale of your science.
Click here to schedule a free introductory call with a member of our team.