GPU Inference Server to Serverless Migration for Long-Tail API Providers
A client running an image generation inference service on two dedicated A100 servers — paying a fixed monthly cost regardless of actual request volume — migrated to a serverless GPU architecture. Designed for developers and small teams selling AI API services who carry idle GPU cost most of the time. Pay-per-second, autoscale-to-zero, OpenAI-compatible endpoint, and zero-downtime cutover.
Discuss a Similar ProjectWhat We Built
Containerised Inference Service
CUDA-optimised Docker image with model weights baked in, dependency layer caching, and warm-up logic — bringing cold-start time down to under 4 seconds at p99.
Serverless GPU Deployment
Inference workers deployed to a serverless GPU platform with autoscale-to-zero during idle periods and scale-up in under 5 seconds on demand — zero infrastructure management after deployment.
OpenAI-Compatible API Wrapper
FastAPI wrapper implementing the OpenAI API schema. Existing client integrations unchanged — the migration is transparent to downstream users, requiring only an endpoint URL swap.
Nginx Percentage-Based Traffic Migration
Traffic gradually shifted from old servers to new serverless endpoint: 5% → 25% → 100% over 4 days — with instant rollback capability at every stage. Zero downtime, zero client impact.
Real-Time Cost Dashboard
Live per-request cost vs. old fixed server cost with cumulative savings counter, break-even visualisation, and billing projection for the next 30 days based on trailing request volume.
Prometheus & Grafana Monitoring
Request latency (p50/p95/p99), error rate, cold-start frequency, and GPU utilisation tracked in real time — with alerting thresholds for latency spikes and error rate anomalies.
Technologies Used
Key Outcomes
Monthly infrastructure cost reduction at equivalent request volume
Zero-downtime progressive traffic migration from old servers to serverless
p99 cold-start time after containerisation and layer caching
Need Something Similar?
Tell us about your current inference setup, model, and request patterns. We will assess the serverless economics and design the migration plan.