LLM Access Strategies Guide

Lessons

Modules

🎓

Bootcamp access

Lo que aprenderás

✓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

¿Para quién es?

•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

Requisitos

•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

1Módulo 1: Decision Framework para Acceso a LLMs8 lessons

2Módulo 2: OpenAI API8 lessons

3Módulo 3: LM Studio (Local GUI)8 lessons

4Módulo 4: Ollama (Local CLI)8 lessons

5Módulo 5: OpenRouter (Agregador Multi-Provider)8 lessons

6Módulo 6: Modal (Serverless Deployment)8 lessons

7Módulo 7: Comparación Técnica y Decision Matrix8 lessons

8Módulo 8: Unified AI Client (Proyecto Final)8 lessons

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