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.
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What’s new Copy linkThe challenge is no longer access. It is operations. Copy link1. Select the right model for the task Copy link2. Validate with your own evals and data Copy link3. Optimize cost and performance Copy link4. Operate at scale with enterprise confidence Copy link5. Continuously improve as models and workloads evolve Copy linkWhat this means for developers Copy linkGet started Copy link85 Impressions