Architecture Decisions
Choose boring infrastructure. Save your creativity for the product.
The models, APIs, and databases you pick on day one will either accelerate you or haunt you for years. Choose wisely.
What you'll learn
- How to choose between APIs, open-source models, and fine-tuning
- The cost/quality/speed triangle for AI infrastructure
- When to use RAG vs. fine-tuning vs. prompt engineering
- Building for model-agnosticism from day one
API vs. Open Source vs. Fine-Tuned
APIs (Claude, GPT, Gemini): Start here. Fastest time to market. Highest quality for general tasks. You pay per token but you ship in days, not months. The tradeoff: you're dependent on someone else's model, pricing, and uptime.
Open source (Llama, Mistral): Lower per-query cost at scale. Full control. But you own the infrastructure — hosting, scaling, monitoring. Don't go here until you have product-market fit and predictable traffic.
Fine-tuned models: Only when you have domain-specific data that general models can't match. Fine-tuning is expensive, requires clean data, and locks you to a specific model version. It's a phase 2 optimization, never a phase 1 choice.
The Cost/Quality/Speed Triangle
API (Claude/GPT): High quality, high speed, higher cost per query
Open Source (Llama): Good quality, moderate speed, low cost at scale (but high infra cost)
Fine-Tuned: Best quality for your domain, slow to set up, medium ongoing cost
Embeddings + RAG: Good quality with your data, fast queries, lowest cost
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