AI Autopilot for Bioinformatics on AWS
Fovus benchmarks every pipeline step — from alignment to variant calling — and auto-assigns the right AWS instance, storage, and Spot strategy, whether you run Nextflow or a custom workflow.
The challenges
Genomics pipelines chain together wildly different computational profiles in a single run — memory-bound alignment steps like STAR_ALIGN, I/O-saturating format conversion, and embarrassingly parallel variant callers like DEEPVARIANT all sharing the same static resource definition. Lightweight steps sit on oversized instances while demanding ones stall, restart, or fail outright, and a configuration that worked on last month’s cohort stops fitting as sample counts and read depths grow. Sequencing costs have dropped from roughly $50,000 per genome in 2010 to under $300 today, and data volume has grown to match — genomic research now generates on the order of 2–40 exabytes annually, per NHGRI. Most bioinformatics teams end up choosing between an overprovisioned cluster running at partial utilization, or a fragile pipeline that breaks every time input sizes shift.
How we help
Fovus benchmarks every step of your pipeline individually against representative inputs — profiling bottlenecks and resource needs — free of charge, whether you run Nextflow, Snakemake, WDL, or a custom pipeline.
Based on that benchmark data, Fovus assigns each step its own EC2 instance type, storage configuration, and Spot/On-Demand strategy — automatically, at runtime, instead of one config for every step. If your first-choice instance fills up, workloads automatically spill to the next-best ranked strategy instead of stalling in a queue.
For Nextflow users, the nf-fovus plugin requires no changes to your pipeline or containers — Fovus generates the configuration and handles AWS orchestration, with no AWS Batch or AWS ParallelCluster environment to configure by hand.
Fovus Memguard snapshots step state at intervals and on Spot interruption, so runs auto-resume from the last checkpoint with Spot-to-Spot failover — making Spot practical even for long-running steps.
A POSIX-compatible distributed file system built on local SSD transparently caches Amazon S3 and is sized per-step based on measured I/O demand — built for I/O-intensive steps like format conversion.
Fovus deploys directly inside your own AWS account — data, compute, and pipeline execution never leave your environment, supporting HIPAA compliance and data-sovereignty requirements. Optimization scope, Regions, and spend stay inside the boundaries you define.
The numbers behind the Autopilot
Real benchmark data from nf-core/rnaseq and nf-core/sarek on AWS
70%+
Cost avoided vs. one static resource configuration applied across every pipeline step.
3–7x
Higher dollar efficiency vs. single-config cloud deployments, benchmarked on Spot.
$0.70
Per-sample cost for an 8-sample nf-core/rnaseq run on Spot.
$6.15
Cost for a full 30× germline WGS run with nf-core/sarek on Spot.
Business Impact
Genomics teams running pipelines on Fovus are seeing real results: ~$0.70 per sample for RNA-seq and $6.15 for a full 30× germline WGS run, both on Spot with memory checkpointing — 70–85% lower cost than a single static configuration, with Spot runs matching on-demand wall time almost exactly so there’s no speed tradeoff for the savings.
Your next pipeline run could be next.
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