QyrosCloud implemented a comprehensive FinOps optimization strategy for a leading therapeutics and biotechnology company, reducing AWS HPC compute costs by 40% and saving approximately $720,000 annually. The engagement optimized drug discovery pipeline workloads through a hybrid Spot/Reserved compute strategy, AWS ParallelCluster tuning, and Batch processing improvements.
What was at stake.
The company runs large-scale batch workloads to support drug discovery pipelines, including:
- molecular simulations
- protein interaction modeling
- high-throughput data processing
- GPU-accelerated computational workloads
As workloads scaled, cloud costs grew rapidly.
“A leading therapeutics and biotechnology company partnered with QyrosCloud to reduce AWS compute costs by 40%, saving the organization approximately $720,000 annually.”
How we solved it.
QyrosCloud implemented a comprehensive FinOps optimization strategy focused on cost reduction, performance improvement, and operational efficiency.
The team redesigned the compute strategy to replace on-demand instances with a combination of:
- Spot Instances for fault-tolerant batch workloads
- Reserved Instances / Savings Plans for baseline compute demand
This hybrid approach significantly reduced compute costs while maintaining reliability.
Key improvements
- prioritized Spot capacity for GPU workloads
- implemented fallback mechanisms to ensure job completion
- aligned Reserved capacity with predictable workloads
Using AWS ParallelCluster, QyrosCloud optimized the HPC environment to better support distributed workloads.
Enhancements included:
- optimized cluster scaling policies
- improved job scheduling efficiency
- automated provisioning of compute nodes
- better utilization of GPU resources
This ensured compute resources were allocated dynamically based on workload demand.
QyrosCloud reconfigured AWS Batch environments to improve job orchestration.
Improvements included:
- optimized compute environments for cost efficiency
- improved job queue prioritization
- dynamic scaling of compute resources
- better alignment between job requirements and instance types
To reduce job runtime and improve efficiency, QyrosCloud implemented cluster placement groups.
Benefits included:
- low-latency communication between instances
- improved network throughput
- faster execution of distributed workloads
This optimization significantly reduced the time required to complete batch jobs, directly lowering compute costs.
QyrosCloud introduced a FinOps framework to continuously optimize cloud spend.
Capabilities included:
- identification of idle and underutilized resources
- rightsizing of instance types
- cost allocation and tagging strategy
- continuous monitoring of AWS spend
This enabled the organization to maintain cost efficiency over time.
“This engagement required us to balance speed with compliance rigor. We deployed infrastructure-as-code from day one, automated evidence collection across the environment, and delivered a production-ready architecture that passed security review on the first attempt.”
The results speak for themselves.
The engagement delivered significant cost savings and performance improvements.
A leading therapeutics and biotechnology company specializing in protein modulation and drug discovery relied on large-scale high-performance computing (HPC) workloads to run batch simulations and data processing pipelines.
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