Deployment Strategies & Monitoring
Explore various deployment models for LLM applications and set up effective monitoring for performance, cost, and output quality.
Deploying & Monitoring LLMs
Welcome to Lesson 2! In this lesson, we'll explore different ways to get your LLM-powered applications live and how to keep a close eye on their performance once they're running.
Understanding deployment models helps you choose the right infrastructure, while effective monitoring ensures your application stays reliable, cost-effective, and delivers quality outputs.
Choosing Your Deployment Path
When deploying an LLM application, you have several primary strategies. Each has its own trade-offs regarding control, cost, scalability, and data privacy.
- Cloud LLM APIs: Using services from providers like OpenAI, Anthropic, or Google.
- Self-Hosted Models: Deploying open-source or proprietary models on your own infrastructure.
- Edge Deployments: Running smaller models directly on user devices or local hardware.
Let's dive into each one.
All lessons in this course
- LLM Operations (LLMops) Principles
- Deployment Strategies & Monitoring
- Scalable LLM Application Architectures
- Caching & Cost Optimization for LLM Apps