HPC & AI Cost Optimization & Performance Assessment on AWS

Is your parallel computing environment on AWS taking too long to deliver results? Are cloud costs increasing while jobs sit in queues? Are you unsure whether your current scheduler or orchestration approach is optimal for your workloads?

Modality’s expert-led assessment evaluates HPC and AI parallel computing environments on AWS, independent of the job scheduler in use. Whether you are running AWS Batch, AWS ParallelCluster, Slurm, AWS PCS, Altair Accelerator, or custom orchestration, we analyze both performance and cost inefficiencies and deliver actionable recommendations to improve time-to-results and cloud efficiency.

Our team brings 15+ years of hands-on experience operating large-scale, production-grade parallel computing environments on AWS—from thousands of concurrent servers running semiconductor EDA workloads, to large-scale AI inference platforms, and compute-intensive simulations. We understand that for HPC workloads, performance and cost must be optimized together, not traded off against one another.

Three-Session Engagement Process Session 1: Discovery & Environment Analysis (1.5–2 hours)

We review your current environment, performance goals, and cost constraints. This includes understanding your scheduler or orchestration model, queue behavior, scaling approach, and existing monitoring or FinOps visibility. Together, we identify relevant data sources and define access methods that align with your security requirements.

Between Sessions: Data Collection & Analysis

We analyze your environment using methods that fit your security posture—such as read-only dashboard access, collaborative screen-sharing sessions, or structured data review meetings. You retain full control over data access. Our analysis focuses on job throughput, queue behavior, compute utilization, cost allocation, storage I/O efficiency, network latency, data movement, and scheduling effectiveness.

Session 2: Data Review & Deep Dive (1.5–2 hours)

We walk through findings related to performance bottlenecks (queue times, I/O constraints, scaling inefficiencies) and cost drivers (over-provisioning, suboptimal instance selection, inefficient Spot usage). This interactive session aligns technical findings with your business priorities—such as time-to-market pressures, budget targets, or operational constraints.

Session 3: Recommendations & Optimization Roadmap (1.5–2 hours)

We present prioritized recommendations balancing performance improvement and cost efficiency. Each recommendation includes expected impact, estimated cost implications, implementation complexity, and business value. You receive a clear set of quick wins and a practical 90-day optimization roadmap.

Deliverables & Expected Outcomes

Based on prior customer engagements, organizations commonly identify double-digit percentage cost-saving opportunities alongside measurable performance improvements by implementing the recommendations from this assessment. Customers typically experience reduced queue times, faster job completion, improved resource utilization, and more predictable cloud spend—often achieving annual savings that significantly exceed the cost of the assessment.