Connecting AI models to different apps usually means writing custom code for each one.For instance, if you want to use a model in a Slack bot or in a dashboard, you'd typically need to write separate integration code for each app.Let's learn how to simplify this via MCPs.We’ll use @LightningAI's LitServe, a popular open-source serving engine for AI models built on FastAPI.It integrates MCP via a dedicated /mcp endpoint.This means that any AI model, RAG, or agent can be deployed as an MCP server, accessible ...

Performance Comparison - LitServe is reported to be 2x faster than FastAPI [2] Key Features - LitServe offers full control over inference processes [2] - The platform supports serving various model types, including LLMs, vision, audio, and multimodal models [2] - LitServe enables the composition of agents, RAG (Retrieval-Augmented Generation), and pipelines within a single file [2]