Usually, yes. We prefer to extend the stack you already trust, which may include your current cloud foundation, container platform, data pipelines, secrets management, observability tooling, IAM, and service management processes, rather than forcing a rip-and-replace platform decision that adds more risk than value. When a new component is necessary, we define where it lives in the operating model, who owns it, how it is monitored, how it is secured, and how it can be replaced later without breaking the rest of the delivery system.

We design around thin interfaces, durable APIs, event-driven handoffs, and clear failure boundaries so AI services can evolve without destabilizing the systems that still run the business. That approach aligns with our guidance on AI integration strategies and integrating AI into legacy systems without blowing up your roadmap. AI ops should reduce coupling, clarify ownership, and create safe rollback paths, not bury model logic inside the most fragile part of your estate.