01
The pipeline only runs on one machine
Software, dependencies, and versions are tangled together. Move to another machine — or hand the project to someone else — and the debugging starts over.
Life sciences research
The data is here. The analysis shouldn’t wait.
Compute environments ready for genomics, transcriptomics, single-cell, and protein research. Spend less time installing software and more time looking at results.
Keep your existing pipelinesRun jobs in batchesCloud, on-prem, or hybrid
The blockers
What slows you down is rarely just compute.
02
More samples, longer queues
CPU, memory, and storage run short all at once. Nobody can say when the jobs will actually finish.
03
More files, harder to find results
Raw data, scripts, and results live in different places. Months later, matching versions is guesswork.
04
Researchers spend their days fixing environments
Installing packages, clearing disk, fixing permissions, chasing errors. That’s where the time goes.
How we solve them
Get environments and data in order first, then allocate resources per job.
Pin down the environment
Standardize tools, versions, and dependencies so the same pipeline reproduces in every team member’s hands.
New team members don’t relearn the same lessons.
Match resources to the job
Give big jobs more CPU and memory; small jobs don’t wait behind them.
Add capacity when the peak hits.
A place for every file
Raw data, intermediate outputs, and final results each have a home.
No more guessing filenames to find a result.
Errors leave a trail
Job status and run logs stay on record, so you can tell whether the problem is code, data, or the machine.
Debugging stops depending on one person’s memory.
Deployment options
There is no single right answer. Where your data lives, how long jobs run, and what hardware you already have all shape the choice.
01
Cloud
A project is starting now, sample volume swings, or you need a burst of capacity fast.
Best for new projects, batch analysis, and collaboration across sites.
02
On-premise
Data can’t leave the building, jobs run year-round, and you already have servers and storage.
Best for sensitive data and steady workloads.
03
Hybrid
Keep everyday analysis local. When batch jobs pile up, borrow cloud capacity to absorb the peak.
Best for teams with existing hardware and spiky workloads.
How it comes together
Skip the spec sheets. Validating with the job you actually run gives you answers you can trust.
- 01
Map the pipeline
List the software, data volume, and target turnaround.
- 02
Review your resources
Take stock of existing servers, storage, and networking.
- 03
Run a test job
Validate the environment and configuration on a representative pipeline.
- 04
Roll out gradually
Once it runs clean, bring in the rest of your jobs and team.
Tell us about your workload
Tell us your data volume, the tools you rely on, and your target turnaround — we’ll size the requirement first.
No need to commit to cloud or on-prem yet
Existing software and hardware welcome
FAQ
Can we keep our current software and scripts?
Yes. We sort out tools, versions, and dependencies first, then decide between a straight migration and a packaged environment.
Does sensitive data have to go to the cloud?
No. Data can stay on-prem, and compute can be split per job.
What about the servers we already have?
We start with CPU, GPU, memory, storage, and network. Whatever still works gets folded in.
How much compute do we need?
A single test run of one real pipeline tells you more than any spec sheet.