Inocras reduced genome analysis costs by 72% with AWS HealthOmics

Inocras, a genomic analysis company founded in 2020, has transformed how they process whole genome data. They achieved a 72% reduction in analysis costs and 47% improvement in processing speed after migrating to AWS HealthOmics. Operating out of San Diego, Seoul, Daejeon, and Hong Kong, the company specializes in whole genome sequencing (WGS), which reads an individual’s entire genetic code and generates hundreds of gigabytes of raw data per person—far more than targeted methods like targeted panel sequencing (TPS) or whole exome sequencing (WES).

The company’s infrastructure journey reflects the evolving challenges of genomic analysis at scale. “Inocras’s genomic analysis system infrastructure has evolved through three major phases: On-premises, Cloud migration, and Cloud native,” explains Dawoon Jung, Solutions Architect and DevOps at Inocras. Their initial cloud migration provided the scalability needed to meet growing demand, but it replicated their on-premises infrastructure management burden in the cloud. While they gained the ability to scale compute and storage resources, they still needed to provision, manage, and maintain bioinformatics infrastructure regardless of usage.

By moving to AWS HealthOmics, a HIPAA-eligible service that provides fully managed bioinformatics workflows, Inocras achieved both elastic scalability and dramatically reduced infrastructure management overhead—enabling their team to focus on scientific innovation rather than operational tasks.

Cloud migration: initial success and emerging challenges

Inocras initially employed a “Lift & Shift” approach to their cloud migration, deploying AWS ParallelCluster and Amazon FSx for Lustre to operate over 120 compute nodes. This configuration provided the necessary high-performance compute (HPC) capabilities for their genomic workloads. However, the increased accessibility of cloud resources created unexpected challenges. “The volume of incoming orders exceeded our expectations, resulting in demands for computing power and storage that surpassed our internal projections,” Jung notes.

The team found themselves managing increasingly complex infrastructure requirements. They needed to maintain Simple Linux Utility for Resource Management (Slurm) and other workload management tools, pre-provision infrastructure before each pipeline run, and continuously maintain storage for all workloads. The challenge of scaling infrastructure to meet variable demand became a significant operational burden. “We deliberated on finding a system that could manage this efficiently, and the AWS Korea team recommended AWS HealthOmics as the most suitable service for our needs,” Jung states. “Together with the AWS Korea team, we spent over six months evaluating HealthOmics technology and identified several key advantages that aligned with our requirements.”

The cloud native advantage

HealthOmics offers fundamental improvements over their self-managed architecture. HealthOmics’s fully-managed bioinformatics workflows, which provision compute resources on-demand based on individual workflow requirements, eliminate the need for maintaining always on compute resources. “With HealthOmics, infrastructure is implemented according to the computing resources defined in the workflow without building infrastructure in advance,” Jung explains. This represented a significant shift from their previous approach, which required extensive preparation before running any analysis pipeline.

Storage efficiency proved equally transformative. Their FSx for Lustre setup demanded constant storage maintenance regardless of actual usage, creating both cost and management overhead. HealthOmics changed this equation entirely by automatically scaling storage up when needed and removing it upon completion. This dynamic approach to storage management became a key driver of their cost reduction, eliminating the waste associated with maintaining unused capacity.

Quantifiable impact and production success

The migration to HealthOmics delivered measurable improvements across all key metrics:

• A 72% reduction in analysis costs
• 47% faster analysis speed
• 5x improvement in scalability

“This was based on HealthOmics’ storage management capabilities and flexible compute node scalability,” Jung emphasizes. Inocras has successfully deployed two genomic analysis products on the HealthOmics infrastructure. MRDVision™, launched in April 2025, was built from the ground up using HealthOmics workflows. Their flagship product, CancerVision™, presented a more complex migration challenge as it serves customers across multiple global markets, leading to local data residency requirements.

The global footprint of CancerVision made the recent expansion of AWS HealthOmics to the Asia Pacific (Seoul) Region particularly significant for Inocras. “CancerVision is a product in global service in Korea, the United States, and other countries, so the availability of HealthOmics in Korea was crucial,” Jung explains. The Seoul Region launch removed a critical barrier, so Inocras could complete the transition of CancerVision to HealthOmics, while maintaining service across all their markets. “Thanks to the full support of the AWS Korea team, we can now use HealthOmics in Korea,” Jung notes, highlighting how regional infrastructure expansion directly facilitated their product strategy.

Summary

The journey of Inocras from on-premises systems, through traditional cloud infrastructure, to cloud-native products offers valuable insights for the broader bioinformatics community. Their experience demonstrates that cloud accessibility can drive unexpected demand increases, making scalability essential. The elimination of infrastructure management overhead—from Slurm administration to storage provisioning—freed their team to focus on scientific objectives rather than operational tasks.

Their results provide concrete benchmarks for what’s achievable with modern cloud-native bioinformatics infrastructure. A 72% cost reduction fundamentally changes the economics of WGS, making large-scale studies more accessible to research organizations. The 47% speed improvement accelerates time to value, critical for both research and clinical applications. Also, the 5x scalability improvement confirms that as genomic analysis demands continue to grow, the infrastructure can grow with them.

For many organizations, the journey of Inocras illustrates that the question isn’t whether to adopt fully-managed bioinformatics infrastructure, but how quickly and seamlessly they can make the transition. As the cost and complexity of managing traditional HPC infrastructure continue to rise, the managed service approach, provided by AWS HealthOmics, becomes increasingly compelling. Using HealthOmics benefits organizations that want to focus on scientific innovation rather than infrastructure management.

Contact an AWS Representative to learn how we can help accelerate your business initiatives.

Further reading

• AWS HealthOmics for clinical diagnostics
• AWS HealthOmics features page