Stop Using One LLM For Everything (Model Selection Explained)

Most teams pick a model in development and never revisit it. Then they scale. And the bill scales with them. In this video I'm giving you the decision framework I use to choose the right LLM for any production agent — not based on benchmarks, but based on the four constraints that actually matter: data privacy, latency budget, task complexity, and cost at volume. We're in Layer 2 of the LLMOps stack — Models and Inference. This sits directly between the Orchestration layer we built in Videos 3–6 and the Knowledge layer coming next. Get this layer wrong and it doesn't matter how clean your LangGraph graph is or how well your CrewAI crew coordinates — you're either overpaying, too slow, or non-compliant. 📌 What's covered: → Why the model layer is the most expensive decision most teams never make deliberately → The four dimensions that actually matter in production — not MMLU, not HumanEval → Real cost numbers: GPT-4o vs GPT-4o-mini vs Llama on Groq vs Gemini Flash → A five-question decision framework with concrete examples for each question → What breaks when you switch models — and how to catch it before production → The model factory pattern — one environment variable to switch providers across your entire agent stack #llmops #AIEngineering #LLM #GPT4 #Groq #Gemini #Python #MachineLearning #AIAgents #SoftwareEngineering