Basic
LLM Access Strategies Guide
Learn to choose the optimal LLM access strategy for any project. Cloud APIs, local models, multi-provider aggregators, or serverless deployment. using a structured decision framework with quantitative trade-offs.
Lessons
13
Modules
2
Type
Free
Learn a structured 5-dimension evaluation framework (cost, quality, privacy, speed, simplicity).
Learn how to work with modern access options:
- OpenAI API (cloud),
- LM Studio (local GUI),
- Ollama (local CLI + Docker),
- OpenRouter (multi-provider aggregator),
- Modal (serverless deployment).
What you'll learn
Apply a 5-dimension decision framework (cost, quality, privacy, speed, simplicity) to choose the optimal LLM provider
Integrate OpenAI API for production cloud applications with robust error handling
Run LLMs locally with LM Studio (GUI) at zero cost with full privacy
Deploy local LLMs in production with Ollama CLI and Docker containers
Who this course is for
AI Engineers who need to decide how to access LLMs for real projects with informed trade-offs
Backend developers adding AI capabilities who want flexibility across providers
Startups seeking cost optimization and vendor diversification across LLM providers
Prerequisites
Basic Python (variables, functions, classes)
Basic HTTP knowledge (GET, POST requests)
Basic terminal usage (navigation, running commands)
No prior LLM or AI experience required
Course content
Explore all the modules and capsules included in this course
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