A Developer’s Guide to Managing Models, Cost and Quality in Microsoft Foundry
The hardest part of building AI systems today is no longer getting access to a capable model. It is knowing how to choose, validate, optimize, and operate the right model across the full lifecycle of a real application.
Take a retrieval-augmented generation (RAG)-based customer support copilot or a tool-calling agent that helps employees complete business workflows. In a prototype, it may be enough to pick a strong model, connect a few data sources, and get a useful response. In production, the system needs to retrieve the right context, call the right tools, meet quality and safety thresholds, stay within latency targets, and run at a cost the business can sustain.
Models evolve, costs shift, and production requirements often arrive after the first version is already working. Success depends less on choosing the most powerful model and more on building a disciplined operating approach around the application.
That is where Microsoft...
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