A comprehensive guide to enterprise AI architecture covering the five key layers needed to move AI from pilot to production: infrastructure management, AI engineering lifecycle (MLOps, LLMOps, AgentOps), AI services APIs, governance/control center, and the AI store. The post explains why a single ML layer is insufficient, introduces the AI gateway as a critical missing control plane between apps and LLM providers, and outlines four common failure modes (cost opacity, fragmentation, agentic debt, shadow AI). Governance is framed as a runtime architectural concern rather than a policy document, with Portkey's AI Gateway presented as the solution throughout.
Table of contents
The data and infrastructure layers in productionWhy one ML layer isn't enoughThe missing layer between your apps and your LLMsEmbedding governance into architecture, not around itMapping your architecture from pilot to productionCommon questions about enterprise AI architectureSort: