Operating GenAI at scale brings back many of the challenges organizations faced in earlier waves of data science, but with higher speed and broader adoption. The difference is that more teams are now involved (product, engineering, security, legal, operations), and small changes can have large downstream effects.

The goal of this step is to clarify which operational disciplines you need, how they differ, and what that means for platform choices and architecture.

Use enterprise-grade platforms where possible

Cloud providers and data clouds offer GenAI and ML operations capabilities out of the box. Unless you have strict on-premise constraints, a pragmatic approach is to rely on these services instead of building everything yourself.

For RAG in particular, the major cloud providers cover most of the required building blocks (query handling, embeddings, retrieval, storage, and generation) with managed services:

This reduces operational burden because key building blocks are already managed:

• identity and access control
• environments and deployment workflows
• logging and observability
• baseline security controls and compliance options

Software engineering practices still apply

GenAI products are still software products. That means lifecycle, test management, and operational processes remain critical.

At the operational level, many of the fundamentals stay the same (DevOps discipline, infrastructure monitoring, elasticity, data quality and drift monitoring, and privacy and security concerns):

In practice, teams need the same discipline they apply to any production service:

• define release versions and rollback paths
• test changes before rollout
• track incidents and fix recurring failure modes
• keep ownership clear so operational issues do not become “someone else’s problem”