Life sciences workloads are generating data faster than most storage infrastructure can keep up with. Genomic sequencing has dropped from $95 million per genome to under $200, an incredible win for science but a very real infrastructure problem. A single whole-genome sequencing run at 30x coverage generates hundreds of gigabytes. Sequencing at fleet scale can yield over 1 PB per week. Digital pathology is converting glass slides into multi-gigabyte whole-slide images at a pace that's overwhelming traditional storage. Cryo-electron microscopy produces terabytes per session as researchers resolve protein structures at near-atomic resolution. And increasingly, all this data is being curated and fed into AI and machine learning models for drug discovery, clinical decision support, and diagnostics — workloads that demand fast, reliable access to massive training datasets.
Scale that across an organization running thousands of samples, imaging studies, and model training runs, and the numbers get big fast. Petabytes of data that need to move between instruments on-prem and compute in the cloud, without slowing anyone down. Qumulo has been solving this problem for life sciences organizations on AWS — and now AWS has formally recognized that work. Qumulo has achieved the AWS Life Sciences Competency.
What is the AWS Life Sciences Competency?
The AWS Competency program isn't something you just apply for and get. AWS requires technical validation by their own Partner Solutions Architects, real customer success stories, and proof that the solution follows AWS best practices for that specific vertical. It's rigorous, and only a small number of partners hold any given competency.
For life sciences organizations evaluating technology partners on AWS, the competency is a shortcut to trust. It means AWS has reviewed the technology, seen what customers are doing with it, and validated that it works in production for life sciences workloads.
Why life sciences data infrastructure is so hard
Life sciences isn't just a scale problem or a performance problem — it's both, at the same time, with a bunch of constraints layered on top.
The instruments are on-premises, and the analysis is increasingly in the cloud. The pipeline between the two can't be a series of manual copy jobs and overnight syncs. When a sequencer finishes a run, or a pathology scanner completes a batch, or a cryo-EM session wraps up, downstream analysis needs to start right away — whether the compute is in the next room or in an AWS Region on the other side of the country.
Then there's the multi-protocol problem. Instruments write over SMB. Bioinformatics pipelines read over NFS. Data scientists training models want S3 access. Most storage platforms make you pick one, which fragments data across silos that don't talk to each other. Trying to aggregate data across these storage silos slows everything down.
How Qumulo solves this
Qumulo's Cloud Data Fabric was built to solve these exact problems. It provides a single namespace that spans on-prem and AWS. Data generated by sequencers, pathology scanners, and cryo-EM instruments shows up in the cloud without anyone scheduling a transfer — no overnight batch jobs, no waiting around for syncs to complete. When a run finishes, the data is available for analysis immediately.
Qumulo handles NFS, SMB, and S3 natively on the same data. The bioinformatics engineer running a GATK pipeline, the pathologist reviewing whole-slide images, the structural biologist analyzing cryo-EM reconstructions, and the data scientist training a model from an S3-integrated notebook all hit the same data, not copies of it.
And it scales to petabytes without performance falling off a cliff. That is important because data will continue to explode — more sequences, more slides, more structures, more training data — and you can't ask a research organization to pause while you rearchitect your storage.
What's next
Earning the AWS Life Sciences Competency is a milestone, but not the finish line. Qumulo continues to deepen its work with AWS, expand partnerships across the life sciences ecosystem, and invest in the capabilities that help organizations in this space move faster, from instrument to insight.
If your organization is working with life sciences data on AWS and the infrastructure isn't keeping up with the science, or you are struggling to get your data to the cloud effectively and efficiently, we'd love to help.