GuideBeginner

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.

64
lessons
8
modules
English · Spanish
available in
Yes
certificate
Free
access
NIEVA

Outcomes

What you'll be able to do

  • 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
  • Access 100+ models from multiple providers through OpenRouter with cost optimization
  • Deploy LLMs serverless with Modal for auto-scaling without DevOps
  • Compare providers quantitatively with real benchmarks (latency, cost, quality)
  • Build a Unified AI Client that abstracts providers with automatic fallback strategies

Before you start

What you need to bring

It's for you if...

  • 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
  • Developers without credit cards or limited budgets who need free local alternatives

Requirements and materials

  • Basic Python (variables, functions, classes)
  • Basic HTTP knowledge (GET, POST requests)
  • Basic terminal usage (navigation, running commands)
  • No prior LLM or AI experience required

Content

The syllabus, module by module

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