Real-Time Embedding Service — Low-Latency Production API

I will build a production real-time embedding service — providing a low-latency REST API for on-demand text embedding, with model serving via a local open-source model for latency-critical applications, request batching for throughput optimisation, response caching for frequent inputs, and monitoring tracking latency, throughput, and model performance. Applications requiring real-time embeddings for search or retrieval need an embedding service with latency characteristics that external API calls cannot guarantee — a locally deployed open-source embedding model served via a dedicated API delivers sub-10ms embedding latency compared to 100-500ms for external API calls, which is the difference between a responsive and a sluggish search experience.

The service covers model selection and deployment (sentence transformers on your hardware or cloud GPU), FastAPI endpoint with batch support, semantic response cache, latency monitoring (p50, p95, p99), throughput benchmarking, and Docker deployment configuration.