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# A Developer’s Guide to Managing Models, Cost and Quality in Microsoft Foundry

**[Microsoft Azure](https://daily.dev/sources/azure)** · 9 min read · 0 upvotes · 0 comments

## Summary

Building production AI systems requires more than model access — it demands a disciplined operating lifecycle. This guide covers five stages for managing AI models in Microsoft Foundry: selecting models by workload fit (not leaderboard rank), validating with custom evals and real data, optimizing cost via routing/batching/caching, operating at scale with governance and observability, and continuously improving as models and requirements evolve. Fireworks AI on Foundry is now generally available, offering open model inference through a single Azure endpoint with enterprise SLAs. The platform processed over 176 billion tokens across 17 S&P 500 enterprises during preview.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://devblogs.microsoft.com/foundry/build-2026-foundry-models>

## Similar posts on daily.dev

- [Introducing Fireworks AI on Microsoft Foundry: Bringing high performance, low latency open model inference to Azure](https://daily.dev/posts/introducing-fireworks-ai-on-microsoft-foundry-bringing-high-performance-low-latency-open-model-inf-9rc1c4zrv) · Microsoft Azure · 3 upvotes · 0 comments

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