More insight from every compute dollar.
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100x
faster time-to-insight
Each process runs on the execution strategy and parallelism that gets the workload to insight fastest.
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3x
more runs on the same budget
Benchmark-picked strategies and Spot capacity can cut compute cost 70% or more versus static configurations.
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1 day
a week back for R&D
Before Fovus, customer teams lost 10 to 30% of their time to infrastructure work. That time now goes back to science and engineering.
There is no one best HPC configuration.
There is only the best configuration for this workload, right now. Yet most HPC still runs on static configurations chosen manually or inherited from a previous job. The gap between what you’re running and what your workload actually needs is the configuration tax: slower results, higher cloud spend, and experiments that never get run.
Every workload behaves differently
A molecular dynamics simulation does not behave like structural analysis. Even within one pipeline, some steps are compute-bound while others are limited by memory or I/O. One static configuration cannot be best for all of them.
The search space is too large to test manually
Hundreds of instance types, CPUs, GPUs, storage options, parallelism settings, regions, pricing models, and shifting capacity create too many combinations for any team to evaluate by hand for every workload.
The best choice keeps changing
New CPUs, GPUs, instance families, pricing, and capacity arrive constantly. A strategy that was best last quarter can quietly fall behind unless the workload is measured again.
Fovus measures before it decides.
Every workload benchmarked. Every run optimized.
Benchmark Intelligence learns how your own workloads respond to compute, memory, storage, parallelism, and pricing, then uses that evidence to decide how each process should run. Measured, not guessed.
01 Benchmark
Submit your workloads and pipelines from the web UI, CLI, Nextflow, or AI agents via MCP, and set your goal: fastest, lowest cost, or a balance. Fovus profiles each process automatically to find what limits it, whether that’s compute, memory, or I/O, and how well it scales with more cores. Generic defaults can’t tell you that. Benchmarking is built in, at no extra charge.
02 Decide and run
Your benchmark data maps each process’s options, from hardware and parallelism to storage and pricing, by runtime and cost.
Fovus picks the best strategy for each process, down to the GPU type, against live AWS pricing and availability, weighted toward your goal.
License-bound workloads scale with license availability, so the licenses you pay for stay in use.
License-free workloads fan out as parallel tasks across AWS regions and zones, for over 100x faster time-to-insight.
Spot capacity runs with Memguard memory checkpointing, so interrupted jobs resume where they left off instead of starting over.
03 Keep improving
As AWS changes, Fovus re-benchmarks your workloads and updates their strategies without changing your workflow. One customer’s virtual screening moved from ARM to next-gen x86 this way. Nobody touched a config.
1/5
1/5
At first benchmark, Fovus picks ARM
Six months later, it switches itself to next-gen x86
From workload to insight, automatically.
Submit from the web UI, CLI and API, Nextflow, or AI agents via MCP. Fovus benchmarks the workload, chooses the best execution strategy for each process against live AWS pricing and availability, then runs the whole job for you, including data, licenses, infrastructure, and Memguard checkpointing on Spot. Scientists and engineers never pick instance types, AMIs, VPCs, or schedulers per job.
It runs natively on AWS, either Fovus-managed or inside your own AWS account (BYOC), under the guardrails you set.
Proven on production workloads
Life sciences and engineering teams run production workloads on Fovus. Here’s what changed when compute began adapting to the work.
Manufacturing
“Fovus empowered us to easily migrate 100% of our HPC workloads to the cloud. Fovus intelligently optimizes our HPC strategies and maximizes our license utilization, improving our digital innovation productivity by +30%.”
Best isn’t a claim. It’s a measurement.
So we publish the numbers: runtime and cost from real pipelines and simulations, each linked to its study, including results co-published with AWS.
Who it’s for
Different teams care about different outcomes. Fovus gives scientists speed, engineering teams throughput, and platform teams control. None of them has to become a cloud infrastructure expert.
Computational scientists
Screening, simulation, and structure prediction at full scale, without learning the cloud. Same budget, more shots on goal.
4x
faster DMTA cycles at a biotech startup: five in 6 days, not 24
Platform and IT leaders
Run HPC in your own AWS account. Your team sets the guardrails. Fovus makes every decision inside them.
100%
of Komatsu’s production CAE workloads moved to the cloud
Simulation engineers
CFD, structural, and multibody runs on hardware benchmarked for each job, with licenses queued and balanced automatically.
4.5x
faster structural analysis at Komatsu
Bioinformatics leaders
Faster, lower-cost runs on the Nextflow pipelines you already run, with one config file swap. Take on more samples at the same cost.
~$0.70
per sample for nf-core/rnaseq, co-published with AWS
Why teams trust Fovus with production compute
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You set the rules
Set the goal, the budget, and the guardrails. Fovus optimizes only within the limits you set.
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Runs in your AWS account
Deploy inside your own account (BYOC), under your security controls and AWS pricing agreements, with no data leaving your account. Or let Fovus manage it for you.
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Every decision has a reason
Each strategy is chosen from measured performance and cost on your own workloads, not generic defaults.
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Validated by AWS
AWS HPC Competency and AWS Qualified Software. SOC 2 Type II compliant.
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Buy through AWS Marketplace
Use your existing AWS billing. Eligible purchases can count toward your AWS commitment.
