◈ Life sciences

More experiments.
Fewer queues.
Less spent on the same GPUs.

Biotech, pharmaceutical and research organisations run some of the most compute-hungry work there is — sequencing, structure prediction, simulation — and the size of the question is often set by the size of the budget. Compute offers the same GPUs as the leading cloud for 30% less, billed by the second.

Used by biotech start-ups, contract research organisations and Fortune 500 pharmaceutical groups for discovery, lab data and trial operations.

30%
Under the leading cloud on GPU hours
Per-second
Billing on every run
Configured
Residency and separation
Complete
Access record

What this looks like in practice

◆Genomics and sequencing pipelines◆Molecular modelling and structure prediction◆Simulation and screening on GPUs◆Laboratory data and sample tracking◆Trial operations and data capture◆Literature and document analysis with AI inference◆Reporting for oversight bodies◆Genomics and sequencing pipelines◆Molecular modelling and structure prediction◆Simulation and screening on GPUs◆Laboratory data and sample tracking◆Trial operations and data capture◆Literature and document analysis with AI inference◆Reporting for oversight bodies
What holds true here

Four things you stop arguing about

◆

GPUs without rationing

H100, A100 and L40S instances 30% under the leading cloud on-demand for the same GPU, started for the run and stopped after it.

◇

Sensitive data kept apart

Separation between studies, environments and data is a configured property, not a procedure.

◈

Every access accounted for

Who looked, when, and at what — retained and answerable as a lookup.

○

Reproducible states

Pipeline code and configuration kept with a full history, so an earlier analysis can be reproduced exactly.

The shift

The budget stops setting the size of the question

When GPU time is expensive and reserved, scientists run fewer experiments and smaller screens. Lower prices and per-second billing change what is worth trying.

The usual arrangement
  • ✕Screens sized to what the reservation could cover
  • ✕Pipelines waiting for a shared cluster
  • ✕Access questions answered by reading logs by hand
  • ✕Earlier analyses hard to reproduce exactly
  • ✕Discovery, lab and trial systems on separate bills
What replaces it
  • ✓More runs for the same budget
  • ✓Capacity started when the pipeline is ready
  • ✓Access questions answered as lookups
  • ✓Analyses reproducible from recorded state
  • ✓One bill, itemised by programme
What you can count on

Stated plainly, and qualified

30%
Under the leading cloud on GPU hours
H100, A100 and L40S, against the leading cloud on-demand
30%
Under the leading cloud’s model service
Tokens on the general model
Per-second
Billing
Runs priced to the second
Complete
Access history
Queryable, retained
Closer in

Life Sciences, specifically

More screens for the same grant

A discovery programme that can afford more runs explores more of the space. When the same GPU costs 30% less than on the leading cloud and is billed only for the seconds it runs, the same budget goes further. That is as true for a seed-stage biotech as it is for a global pharmaceutical group.

Pipelines in CodeGraph

Analysis code with review and CI, so what produced a result is always recorded.

Lab data on CosmoBase

Sample and assay records in managed databases priced under the leading cloud’s managed databases.

Literature by the token

Document and literature analysis through AI inference, 30% under the leading cloud’s model service for the same model.

Ask us about attestations

Residency, access records and separation are configured. For a specific attestation, ask and we will answer plainly.

Against the leading cloud

Life Sciences on Compute, against the leading cloud

H100 GPU hour
$4.82/hr
Leading cloud $6.88/hr · Same GPU (H100 80 GB)
30% less
A100 GPU hour
$1.92/hr
Leading cloud $2.74/hr · Same GPU (A100 40 GB)
30% less
General & code model — input
$0.105 / million tokens
Leading cloud $0.15 / million tokens · The same open-weight model, input tokens
30% less
Database
$0.0112 / database-hour
Leading cloud $0.016 / database-hour · Managed database, smallest instance
30% less

Compared with the leading cloud's own published on-demand prices, US East. Every comparison.

Questions we are actually asked

Answers without the hedging

Do you hold the attestations our studies require?+

Tell us which attestation you need and we will say plainly whether we hold it today. Residency, separation and access records are configured properties of the platform, and we will show you how they are set.

How much less is GPU time than the leading cloud?+

30% under the leading cloud’s published on-demand price for the same H100, A100 or L40S, shown beside ours. Billing is per second.

Can we reproduce an earlier analysis?+

Pipeline code and configuration are kept with a full history, so an earlier state can be recovered and run again.

Is there a minimum commitment?+

No. There are no reservations or minimum terms. Support plans are monthly.

Other industries

Where else this goes

AI Agents

Run agents, the tools they call and the models behind them on one platform, billed by the token and the second, for less than the same work costs on the leading cloud.

Customer Acquisition

Host the landing pages, attribution pipelines and onboarding flows that bring customers in, and spend less on the infrastructure than you would on the leading cloud.

Marketing

Run campaign sites, personalisation, content pipelines and marketing data on one platform that costs less than the leading cloud for the same machines and models.

Lead Generation

Run the forms, enrichment jobs, scoring models and routing that turn interest into leads, for less than the same infrastructure costs on the leading cloud.

Sales

Run CRMs, sales data, call analysis and AI assistants on one platform, priced under the leading cloud for the same machines, databases and models.

Creator Economy

Host creator platforms, media processing, storefronts and AI tools on infrastructure that bills by the second and costs less than the leading cloud for the same machines.

Start where it matters most

Begin with one workload. Keep what you have running. Decide whether to continue once you have seen it carry something real.

No migration required to begin. Nothing to tear down if you stop.