Cost optimization for 1B+ encoder classifications using Inference Endpoints, Infinity, and k6

AI Impact Summary

At 1B+ classifications per day, cost and latency become the primary constraints. The post documents a practical framework to measure cost and latency across hardware choices (via Inference Endpoints), deployment tooling (Hugging Face Hub Library), and serving stacks (Infinity) for encoder-style models, plus a repeatable load-test methodology with k6. It emphasizes batching, VU management, and horizontal scaling with replica GPUs to minimize cost per inference while meeting latency targets.