Large language models are democratizing AI capabilities. In a few API calls, a team can summarize documents, generate content, or build a conversational interface. But enterprise architecture demands more than a proof of concept.

The integration iceberg

The visible part of LLM integration is the API call. Below the surface are concerns that determine whether the solution survives production:

• Data privacy and residency
• Prompt versioning and observability
• Caching, cost control, and rate limiting
• Error handling and fallback strategies
• Evaluation and continuous improvement

A layered approach

A pragmatic architecture introduces an orchestration layer between the enterprise application and the LLM provider. This layer handles retries, caching, prompt templates, and telemetry. It also makes it easier to swap providers or route traffic based on use case.

Start with internal tools

The safest place to start is internal tooling: developer assistants, content review helpers, or support summarization. These use cases deliver value while exposing the team to operational concerns in a controlled way.

From experiments to platform capability

Over time, the orchestration layer becomes a platform capability. Other teams consume it through internal APIs, and governance patterns emerge around model selection, prompt review, and data handling.

Enterprises that succeed with LLMs will be the ones that treat them as infrastructure, not magic.