Enterprise LLMs differ from consumer AI in that they must integrate with private company data, comply with security and regulatory requirements, and connect to internal systems. Key architectural decisions include using RAG to ground model outputs in proprietary knowledge, choosing between cloud APIs, self-hosted open-weight models, or hybrid deployments, and implementing multi-layer security guardrails covering input filtering, output validation, and workflow monitoring. Production monitoring requires tracking latency, token costs, and output quality, while cost optimization leverages intelligent routing and semantic caching. Multi-provider adoption is growing rapidly, with 76% of enterprise AI use cases now purchased rather than built in-house, and Anthropic, OpenAI, and Google together accounting for ~88% of enterprise LLM API usage.
Table of contents
How RAG connects LLMs to your organization’s knowledgeDeployment models for enterprise LLM infrastructureSecurity controls and guardrails in productionMonitoring LLM behavior and controlling costsEvaluating your enterprise LLM architectureShip Faster with AIFAQs on enterprise LLM implementationSort: