Self-Hosted Reranker — Local Cross-Encoder Inference API-Free

I will deploy a self-hosted cross-encoder reranker for your pipeline — setting up BGE-reranker, ms-marco-MiniLM, or FlashRank on your infrastructure, building a FastAPI inference endpoint, configuring batching for throughput optimisation, and integrating with your retrieval pipeline — eliminating per-API-call reranking costs for high-volume applications. Self-hosted reranking is the economically optimal choice for high-volume retrieval applications — managed API reranking costs accumulate proportionally with query volume, while a self-hosted reranker on a moderate GPU instance provides essentially unlimited reranking capacity for a fixed monthly infrastructure cost.

The deployment covers model selection for your hardware (GPU or CPU-optimised model), FastAPI inference endpoint, batch processing for throughput, Docker deployment, integration with your retrieval pipeline, latency benchmarking, and cost comparison against managed API reranking.