Managing Multi-Vendor AI Stacks

Most organisations end up using AI from multiple vendors - a large language model from one provider, computer vision from another, an analytics platform from a third, plus internal tools built on open-source frameworks. Managing this complexity is a real operational challenge. Each vendor has different APIs, different data formats, different update cycles, and different approaches to security and privacy. Integration between components requires ongoing engineering effort, and a change by one vendor can break your workflow with another. Effective multi-vendor management requires a clear architecture that defines how components interact, abstraction layers that insulate your applications from vendor-specific details, and operational practices for monitoring, updating, and troubleshooting across the stack. It also requires someone to own the overall picture - understanding how the pieces fit together and making deliberate decisions about where to standardise and where to accept heterogeneity. The alternative is an accidental architecture that grows organically, becomes increasingly fragile, and eventually costs more to maintain than the AI capabilities are worth.