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ShopScale
Serverless Inference Architecture for 100M+ Users
-45%
Inference Cost
99.99%
Uptime
5min
Deployment Time
Black Friday Crash
The client's legacy recommendation engine crashed during peak traffic, costing $2M in lost sales. They needed a system that could auto-scale from 0 to 10k requests/sec instantly.
Serverless GPU Mesh
We redesigned the inference layer using distributed serving on Kubernetes with auto-scaling policies based on request velocity. Implemented aggressive caching and model quantization to fit higher throughput on cheaper instances.
Input Stream
Context Retrieval
Core
Orchestrator
Action Engine
Response Gen
System Architecture v1.0
Key Outcomes
- Handled 2x peak traffic of previous year with zero downtime.
- Reduced cloud bill by 45% via spot instance orchestration.
- Enabled A/B testing of new models in production.