UneeQ Cuts Digital Human Rendering Costs by 55% Using AWS Spot Instance Orchestration

UneeQ built its high-fidelity 3D digital human rendering infrastructure using Amazon EC2 Spot instances, achieving a 30–55% reduction in service costs per digital human. Traditional cloud infrastructure, optimized for 1:N models, struggled to maintain cost efficiency for AI agent workloads that require independent instances per user.

Custom Orchestration Layer 'Group Manager'

To overcome the limitations of native AWS scaling features, UneeQ developed a custom orchestration layer called 'Group Manager'. This system, built on AWS Lambda and Amazon EventBridge, evaluates available Spot capacity and prices every two minutes and performs dynamic instance allocation based on workload density (the number of digital humans processable per node).

• Workload-aware selection: Analyzes the density and cost of various GPU instance types, such as g5 and g6, to prioritize combinations with the lowest 'effective cost per digital human'.
• Multi-region distribution: Uses Amazon Route 53 and ALB to distribute traffic across multiple regions, such as us-east-1 and us-west-2, maximizing availability.
• Automatic fallback strategy: Immediately switches to On-Demand instances when Spot capacity is unavailable, or terminates On-Demand instances when spare capacity becomes available to minimize costs.

Measured Results and Operational Efficiency

With this architecture, UneeQ maintained 99.95% availability while significantly reducing costs without performance degradation compared to a pure On-Demand environment. Additionally, automation based on CloudWatch monitoring reduced operational burden, saving approximately 15 hours of manual management time per month. This case study presents a blueprint for turning cost management into a competitive advantage in GPU-intensive workloads such as game streaming and real-time rendering.