How a Fortune 500 manufacturer reduced multi-cloud GPU spend by $1M annually

Outcomes

23% reduction in annual cloud spend Reduced GPU infrastructure costs by more than $1 million per year.

40% improvement in delivery velocity Accelerated engineering throughput with better workload placement and more predictable infrastructure.

GPU utilization increased from 18% to 62% Improved effective resource utilization by 3.4x.

Client profile

Fortune 500 industrial technology company operating a large-scale AI platform across AWS and Azure.

Because of confidentiality obligations, the client name is not disclosed.

The challenge

The client was operating a multi-cloud GPU platform with annual spend of approximately $4 million across AWS and Azure. Despite the scale of the environment, average GPU utilization was only 18%, meaning most provisioned GPU capacity was underused for large portions of the day.

At the same time, infrastructure costs were increasing by 36% year over year. Engineering teams could launch GPU-backed workloads, but there was limited visibility into actual consumption by team, environment, or project. Finance could see total spend rising, but not which workloads were driving it or whether the spend was justified by business value.

Several structural issues were contributing to the problem:

• •Premium GPU instances were being used for workloads that did not require that level of compute
• •Training environments were over-provisioned and left running outside active usage windows
• •Fault-tolerant jobs were running on on-demand capacity instead of lower-cost interruptible instances
• •There was no consistent model for cost allocation, budget accountability, or anomaly detection across the platform
• •GPU scheduling policies were designed around availability, not cost efficiency or workload priority

The client needed to reduce spend without disrupting delivery, compromising compliance, or slowing down model development.

The engagement: AI FinOps & Cost Optimization

We delivered the engagement in two phases: Assessment and Optimization Implementation.

Phase 1: Assessment

We began with a two-week infrastructure and workload assessment across both cloud environments. The goal was to establish a complete view of GPU usage, utilization patterns, workload criticality, and cost drivers.

This assessment included:

• •GPU instance inventory across AWS and Azure
• •Workload classification across training, inference, batch, and development environments
• •Utilization analysis at the GPU, cluster, team, and project level
• •Review of autoscaling behavior and idle capacity
• •Cost allocation gap analysis
• •Compliance review to ensure changes would align with SOC 2 and ISO 27001 controls

This phase gave the client a clear baseline and identified the highest-value optimization opportunities.

Phase 2: Optimization Implementation

With the assessment complete, we executed a phased optimization program focused on infrastructure efficiency, workload placement, and governance.

GPU right-sizing by workload class

We profiled workloads to determine where premium GPU capacity was unnecessary. Inference jobs and lower-intensity workloads that had been deployed on A100-class infrastructure were migrated to smaller, more cost-efficient GPU instances where performance requirements allowed.

This reduced cost substantially on those workloads without affecting service objectives.

Spot and preemptible capacity for resilient training jobs

We redesigned training pipelines to support checkpoint-and-resume, then moved interruption-tolerant jobs onto spot and preemptible capacity. This allowed the client to capture major cost savings on non-critical and restartable training workloads while preserving reliability through automated recovery.

Intelligent workload scheduling and cluster scale-down

We introduced scheduling policies that aligned infrastructure usage with workload urgency and time-of-day demand. Non-urgent jobs were shifted into lower-demand windows, and GPU clusters were configured to scale down aggressively during off-peak periods. Batch processing was consolidated into more efficient execution windows instead of being spread across always-on capacity.

FinOps governance and cost accountability

We established a cost governance framework across both cloud providers, including:

• •Per-team and per-project allocation tags
• •Budget thresholds and alerting
• •Monthly cloud cost review cadences
• •Standardized reporting for engineering and finance stakeholders

This gave teams visibility into their own consumption and created accountability for GPU usage decisions.

Real-time cost and utilization observability

We deployed dashboards that exposed GPU utilization, spend trends, idle capacity, and cost anomalies in near real time. This made it possible to identify waste early, detect unexpected spend spikes, and monitor optimization progress continuously rather than through monthly billing reviews alone.

Results

Within three months, the client achieved measurable improvements in both cost efficiency and operational performance.

23% overall cost reduction Annual platform spend was reduced by more than $1 million from a $4 million baseline.

GPU utilization increased from 18% to 62% The platform moved from chronic over-provisioning to materially higher asset efficiency.

40% improvement in delivery velocity Engineering teams were able to deliver faster because workloads were placed more appropriately, infrastructure was more predictable, and capacity planning improved.

Why this worked

The results did not come from a single tactical fix. They came from combining platform assessment, implementation discipline, and financial governance.

The biggest drivers were:

• •Matching workloads to the right GPU class instead of defaulting to premium instances
• •Moving resilient training jobs to lower-cost interruptible capacity
• •Eliminating idle GPU hours through better scheduling and scale-down policies
• •Creating shared cost visibility across engineering, platform, and finance teams
• •Embedding FinOps controls into the operating model rather than treating cloud cost as a retrospective reporting exercise

Business impact

Before the engagement, the client's GPU platform was scaling in cost faster than its teams could explain or control. After the AI FinOps & Cost Optimization engagement, the platform became more efficient, more governable, and easier to scale responsibly.

Instead of simply reducing infrastructure spend, the client improved the economics of AI delivery: more useful work per GPU, better forecasting, stronger cost accountability, and a platform foundation that could support continued growth without repeating the same spend pattern.