Cloud infrastructure is being redesigned around inference. In the past, cloud architectures were built around web requests and databases, with machine learning as a sidecar. Now, models are becoming the core service: customer support routing, document processing, personalization, and analytics are increasingly model-driven. This changes how you think about capacity planning (burst traffic for inference), cost management (tokens and GPU time), latency budgets (streaming responses), and reliability (fallback paths when a model is unavailable). “Inference-native” architecture means designing your system so that model calls are observable, cacheable, and controllable like any other critical dependency.

The best practices are emerging quickly. Teams use layered caching (prompt + response caching where safe), batching to increase GPU utilization, and routing strategies that choose the cheapest model that meets the quality requirement. They also adopt “model gateways” that centralize authentication, logging, policy enforcement, and evaluation. Instead of every team calling models directly, the gateway applies consistent rules: redaction, rate limits, content filters, and cost budgets. This also simplifies upgrades: you can switch models behind the gateway without rewriting every application. Observability becomes non-negotiable—teams track latency distributions, error rates, token usage, and quality metrics tied to business outcomes.

Over time, cloud + AI will look less like a single vendor stack and more like a modular mesh: multiple model providers, specialized embedding stores, vector search, and evaluation services stitched together with policy. The real differentiator will be “how quickly can you ship reliable improvements?” That depends on automated evals, curated datasets, and deployment pipelines that support A/B tests for model changes. Companies that win will treat models like living dependencies: versioned, tested, monitored, and governed. It’s not enough to call a model—you need an operational system around it that makes cost, risk, and quality visible every day.