Managed Slurm and the validated open-source scientific stack — CADD, cryo-EM, multi-omics, and AI, on one queue — running in your own AWS account. Your data stays in your VPC; your AWS & NVIDIA credits pay the compute.
3 design-partner slots · BYOC plans from $99/mo · free 30-day pilot
Right now they lose weeks to undifferentiated heavy lifting — building containers, tuning GPUs, nursing spot interruptions, wiring Slurm. Clusterra hands all of it to a validated, managed cluster in your own AWS, so your scientists and engineers spend their time on the science and product only they can build. Two questions usually follow — answered below.
Batch, PCS, and ParallelCluster are kits — someone still has to design the cluster, build the containers, wire Slurm, and tune the GPUs. That’s a dedicated HPC/DevOps headcount. We deliver it built, validated, and running on day one.
Spot GPUs at ~60% off — with checkpoint-resume so long jobs survive reclaim.
Most biotechs hold $100K+ AWS Activate and $100K+ NVIDIA Inception credits. Because Clusterra runs in your account, every run draws them down directly — so for months, world-class compute costs you effectively nothing.
Clusterra comes out of AWS HPC — built by people who helped shape AWS’s own HPC services. When we say the raw parts aren’t the instrument, it’s from the inside. Built on open-source Slurm — no lock-in.
Your HPC team for a fraction of one hire — not three (HPC + AI/ML infra + DevOps).
The same managed Slurm cluster runs all three. Start with the pain blocking you now — each has a dedicated page with benchmarks, templates, and case studies.
Docking ranks compounds. FEP ranks them right — without the cluster.
For the lone comp chemist at a Series A/B small-molecule biotech. The open-source CADD stack — OpenFold3, Uni-Dock, Boltz-2, GROMACS, OpenFE — on one queue, no partition routing, no HPC hire.
The cluster cryoSPARC and RELION-5 actually need.
For structural-biology teams running RELION-5 / AreTomo / Warp·M / IsoNet. Multi-TB tilt-series stay in your VPC. No academic-cluster waitlist, no $90/TB egress.
nf-core, AlphaDIA, MAPPs — at cohort scale.
For teams running Nextflow/nf-core, mass-spec, and single-cell pipelines. Cohorts run in parallel, not serial — autoscaling on spot, back to zero when idle.
A single sbatch chains structure → dock → co-fold → MD across spot GPUs in your account — 73 minutes, $1.16, every step costed and reproducible. One pass of a longer optimization loop, not the finish line. (Each branch has its own end-to-end demo.)
sbatch, unedited wall-clock.
The open-source scientific stack your team already cites
A managed Slurm cluster in your AWS, your domain stack operated end-to-end, and every run's history accreting as your program's computational book-of-record.
Results, parameters, and per-job cost stamps accumulate in your own EFS — a cost-stamped record that lives in your account, not ours, and compounds into a system no competitor can pull out of your VPC.
Classical HPC and AI-for-bio on one queue — structure prediction, docking, MD, RBFE, cryo-EM refinement, genomics pipelines, plus model training and inference. Karpenter picks the instance family and GPU. You never write a partition name.
Pre-IND structures, tilt-series, and genomic data are program IP. They stay in your IAM perimeter — not on our infra, not routed through our control plane. You see every dollar on your AWS bill, and your Savings Plans apply.
Submit from the web console if you'd rather not touch Slurm. SSH in and sbatch if you live in the terminal. Wire the REST API into your orchestrator if you automate everything.
Browse the template catalog, fill the form, hit submit. Live logs, per-job cost stamp, run history. For scientists who'd rather not write sbatch.
sbatch fep-campaign --network edges.csvSlurm-native. SSH in and submit the way you would on any HPC system — same sbatch, squeue, sacct, scancel. For people who already live in the terminal.
POST /v1/jobs/submitWire Clusterra into your in-house pipeline, Airflow DAG, or Slack bot. JSON in, JSON out. For platform teams that want Clusterra as the compute backend.
Spot pricing, checkpoint-restart on, us-east-1. Reproducible benchmarks on public datasets — fork the repo, verify the numbers.
Batch, PCS, and ParallelCluster are those parts pre-sorted but still un-assembled — we know, we helped build them. We deliver the cluster already built, validated, and running — in your own account.
Batch, PCS, and ParallelCluster are kits: someone still has to design the cluster, build the containers, wire up Slurm, and tune the GPUs before a single job runs correctly. That’s a dedicated headcount’s worth of HPC/DevOps work. We hand it to you already built and validated — a managed Slurm cluster running right the day we turn it on.
Spot GPU is ~60–70% cheaper than on-demand, but on raw AWS a long-running job dies the instant the instance is reclaimed — so most teams never use it. We catch the 2-minute reclaim warning, drain the job cleanly, and resume it. The cheap compute becomes usable for the long jobs that actually need it.
On raw AWS you get one monthly bill and no record of how a result was produced. We stamp every job with its dollar cost as it runs — spend visible per experiment, per user, per project — and capture the exact pinned container digest and script behind each run, so any result can be reproduced or re-run months later without rebuilding the environment.
All of that — the day-one cluster, survivable spot, costed reproducible runs, and your own AWS/NVIDIA credits paying the compute — for less than a tenth of the hire it would take to build it yourself:
Every plan is the full platform. Plans differ only by how much management credit is included.
For one scientist getting started. Run real campaigns on a proper GPU cluster without committing to anything bigger.
For a team running every week. Pay $499, get $999 of management credit — about 2× the value.
For running hard, with zero math. One flat price covers everything you run — it never changes.
Against any of those, a $99–$1,499/mo management fee is a rounding error — and not a license, a CRO, or a headcount you'd otherwise carry.
For running your cluster — the managed software, the validated scientific stack, spot survival, and provenance. That is the only thing you pay us. The compute itself (GPUs, CPUs, storage) is billed separately and directly by AWS in your own account, at AWS prices — we never touch or mark up that bill.
One scientist, light or occasional runs → Solo ($99/mo, includes $99 of management credit). Running most weeks → Team, the best value ($499/mo, includes $999 of credit — ~2×). Running hard and want one predictable bill → Org ($1,499/mo flat, unlimited, no metering).
“Management credit” is prepaid Clusterra fee — not compute, and not your AWS credits. Solo and Team meter it at one uniform rate, $0.40/GPU-hr + $0.04/vCPU-hr; once the credit is used up you keep paying that same rate, nothing changes. Hours vary by instance; AWS bills the compute separately and directly, at AWS prices.
Yes — this is a real advantage of BYOC. Because compute runs in your own account, your AWS Activate and NVIDIA Inception credits pay for it directly. A SaaS platform that bills you cannot apply those credits — they'd expire unused. Many teams run for months on credits alone; the management fee is the only thing you pay us.
A free 30-day pilot: we stand up your cluster in your AWS, wire your workflows, and get a real campaign running with you — no charge.
Yes — no lock-in. Cancel anytime and changes take effect on your next cycle. Because everything runs in your own AWS account, your data and clusters stay with you.
Or book a free pilot — we get a real campaign running, no charge.
30 minutes to scope. Free 30-day pilot with your first campaign running in your AWS account — CADD, cryo-EM, or multi-omics, no charge. Then BYOC plans from $99/mo. Three design-partner slots — closes when filled.
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