Rust

High-Performance AI API

Build a blazing-fast AI inference API with Rust. Learn async programming, model optimization, and production deployment.

⏱️ 3h 30min

📦 8 modules

🎯 Intermediate

What You'll Build

Build a high-performance REST API in Rust for AI model inference that handles thousands of requests per second.

You'll implement:

Async endpoints using Axum framework
Connection pooling with reqwest::Client
Redis caching with redis-rs
Prometheus metrics via prometheus crate
Structured logging with tracing

Learn to write production-grade Rust code with tokio async runtime and achieve sub-10ms response times.

Learning Objectives

Build async REST APIs with Axum or Actix-web
Integrate AI models using reqwest and serde
Implement efficient connection pooling
Optimize inference with zero-copy operations
Add Redis caching and rate limiting
Deploy with Docker and monitoring

Prerequisites

Basic Rust programming knowledge
Understanding of async/await and tokio
Familiarity with REST APIs and HTTP
Basic knowledge of JSON serialization

Course Modules

Rust API Framework Setup

Set up Axum framework and create your first routes.

Topics:

Initialize project with cargo new
Add axum and tokio dependencies
Create Router and define endpoints
Implement Json<T> request/response handling

Async Foundations

Master async programming with tokio runtime.

Learn:

Use #[tokio::main] for async entry point
Work with async fn and .await
Spawn concurrent tasks with tokio::spawn
Handle Arc<Mutex<T>> for shared state

AI Model Integration

Integrate with AI inference services, handle model requests, parse responses, and implement error handling.

Connection Pooling

Implement efficient HTTP client pooling.

Build:

Create a shared reqwest::Client instance
Configure pool_max_idle_per_host
Set timeouts with timeout() and connect_timeout()
Use Arc<Client> for thread-safe sharing

Performance Optimization

Profile your API, identify bottlenecks, optimize memory usage, and implement zero-copy operations where possible.

Caching & Rate Limiting

Add Redis caching for repeated requests, implement rate limiting per user, and handle cache invalidation.

Monitoring & Metrics

Integrate Prometheus metrics, add structured logging, implement health checks, and set up alerting.

Production Deployment

Dockerize your application, configure for production, implement graceful shutdown, and deploy with best practices.

Technologies

Rust Axum Tokio OpenAI Redis Docker