Analysis scenarios
Different workloads, very different server needs
Single-cell, transcriptomics, metagenomics, and shared lab use don’t care about the same things. Some are short on memory; others need storage and collaboration. Sizing by scenario gets you closer to your real needs than reading machine specs alone.
On this page
01
Single-cell lives or dies on memory and job stability.
02
Transcriptomics is about a smooth environment and easy result management.
03
Metagenomics and team use hinge on storage, maintenance, and collaboration.
Step 01
Single-cell
Single-cell analysis: memory and stability bite first
Single-cell work is sensitive to memory — once datasets get big, a local machine gives up fast. Beyond the parameters themselves, think about how often you’ll re-run jobs, whether results need to persist long-term, and whether you’ll work alongside RStudio Server or Jupyter day to day.
What single-cell users care about most
- Enough RAM to run Cell Ranger and the downstream pipeline
- Easy recovery and restart when a job gets interrupted
- R and Python environments that both work smoothly
- Whether results can be shared across a team and kept long-term
Step 02
Transcriptomics
Transcriptomics: what matters is how smoothly the whole pipeline runs
Transcriptomics projects aren’t always the most demanding step by step, but they tend to involve many tools, many scripts, and plenty of result wrangling. For this kind of work, a consistent environment and comfortable daily operations usually beat a few extra CPU cores.
What transcriptomics workflows need
- Common tools and packages preinstalled and complete
- RStudio Server that’s comfortable for everyday statistics and plotting
- Easy batch saving and organizing of result files
- A setup that holds up across a long project cycle
Step 03
Metagenomics
Metagenomics: compute and storage both need a look
Metagenomics projects rarely end after one run — expect multiple batches of data, lots of intermediate files, and longer job chains. So when sizing, look at storage space and the ongoing management experience along with raw compute.
Common metagenomics concerns
- How much storage large sample sets consume
- Stability during long-running jobs
- Environment compatibility across multiple analysis tools
- Convenient archiving and team sharing of results
Step 04
Lab sharing & medical research
Shared lab use and medical research: long-term reliability comes first
Once a server isn’t just yours but shared across the lab, the questions change. The point is no longer just whether you can run a job — it’s whether everyone can keep working smoothly over the long run. When medical research data is involved, isolation, compliance, and centralized maintenance matter even more.
What these scenarios prioritize
- Stays responsive when several people work at once
- One consistent environment instead of everyone installing their own
- Data isolation, access control, and low-effort long-term maintenance
- Dedicated resources rather than a shared plan
Related Paths
Keep exploring
Product plans
Shared, Pro, or dedicated: which one fits
View product plans
Pre-purchase questions
Get clear on specs, memory, and how you will work
View common questions
Public cloud comparison
How specs, pricing, and ready-to-run environments differ
View the comparison
Why move to the cloud
Queues, contention, and the real cost of self-hosting
See why teams switch
Need Help
Know what you’re running but not what to rent?
Tell us your main analysis type, roughly how much data, and how many people will share — we’ll help you decide whether a shared plan is enough or a bigger setup makes more sense.