A biopharma research company needed a more scalable cloud foundation as RNA research growth, merger activity, limited AWS expertise, and rising costs made its environment harder to govern and more expensive to run.
Case Study
70–80% reduction in gene-sequencing processing time
20–30% reduction in compute costs
36–48 hour sequencing jobs moved into single-digit hours
Cloud complexity outpaced growth
A biopharma research company’s cloud environment had grown more complex as the organization scaled its RNA research operations and absorbed additional cloud environments through merger activity. The organization was managing limited internal AWS expertise, reduced visibility into governance processes, rising cloud costs, and overlapping infrastructure across accounts, systems, and teams.
At the same time, research workflows were placing new demands on the cloud environment. Scientists relied on large compute resources to run intensive sequencing and analysis jobs, but those systems were often sitting idle between runs. When the jobs did run, they could take days to complete, slowing the pace of research and increasing the cost of discovery.
The company did not simply need more cloud infrastructure. It needed a more scalable cloud operating foundation: one that could improve governance, control costs, and give scientists faster access to the compute power their work required.
Challenge
- The company needed a more scalable cloud operating foundation
- Research workflows were placing new demands on the cloud environment, slowing the pace of research and increasing the cost of discovery
A better model for working in the cloud
Stellix was initially engaged to assess the company’s AWS architecture through a Well-Architected Framework review. That work went beyond a basic cloud assessment. Stellix examined security, architecture, account structure, networking, governance, and operational practices, then delivered a structured roadmap for improvement.
The team identified architectural and operational inefficiencies across the cloud environment, including integration gaps, data synchronization issues, cross-account conflicts, DNS inconsistencies, and EC2 usage that had become a major cost driver. Stellix also helped rationalize the broader AWS footprint created by merger activity, including overlapping accounts, duplicate services, fragmented storage, and large volumes of scientific data that were not actively used.
But the engagement wasn’t limited to cleanup. Stellix worked proactively with the company’s developers, IT team, and scientists to understand how research work was being done. This led to targeted proofs of concept designed to show how cloud-native approaches could improve both cost and scientific throughput.
One major opportunity was research computing. Existing sequencing and analysis workflows were running on large, persistent machines, even though those resources weren’t needed continuously. Stellix helped re-architect the workflow using cloud-native high-performance computing patterns, including parallel processing, AWS Batch, and containerized workloads. Instead of keeping large servers running while they waited for the next job, researchers could submit jobs into a cloud environment that scaled compute resources as needed and shut them down when the work was complete.
Stellix also helped define a more disciplined model for cloud operations, including account optimization, near real-time data integration across environments, standardized instrument-to-cloud integration, and lifecycle-based storage practices for scientific data.
Stellix Role
- Replaced persistent, underused machines with on-demand cloud compute
- Built a roadmap for account rationalization and service consolidation
- Applied lifecycle-based storage practices to roughly 750 TB of scientific data
A cloud built for research
The company gained a cloud foundation better aligned to the way its research teams work. The environment became easier to govern, less wasteful to operate, and more responsive to scientific demand.
For researchers, the change was practical: cloud infrastructure no longer had to be a bottleneck. Compute-intensive jobs could be completed faster without keeping large resources running unnecessarily. For IT and operations teams, the work created clearer governance, better control over cloud usage, and a more structured roadmap for ongoing cloud management.
The engagement also demonstrated the value of Stellix’s proactive cloud model. Rather than waiting for tickets or adding infrastructure on request, Stellix helped the company see where cloud design, cost control, and research acceleration could be solved together.
Outcome
- Improved governance across a complex AWS environment
- Created a more scalable cloud operating model for faster research and disciplined cloud spending
A new foundation for growth
Stellix reduced gene-sequencing processing time by 70–80%, moving jobs that had taken 36–48 hours into single-digit hours. Compute costs were reduced by 20–30% through parallelization, right-sized resources, and cloud compute that scaled only when needed.
In parallel, Stellix helped address the cost and governance burden created by a large AWS footprint, including roughly 750 TB of scientific data, of which only a small portion was actively used. Through AWS Well-Architected reviews, account rationalization, service consolidation, and hot/warm/cold/frozen storage tiering, the work established a roadmap for stronger governance and lower infrastructure waste.
Together, the engagement helped the company build a more scalable cloud operating model: one that supports faster research, more disciplined cloud spending, and a foundation for continued scientific and digital growth.
Results
- Reduced gene-sequencing processing time by 70–80%
- Moved 36–48-hour sequencing jobs into single-digit hours
- Reduced compute costs by 20–30%
Bridging Vision and Reality
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