Enterprise AI has a structural problem. Most organizations are over budget, under-governed, and building on architectures that weren't designed for production AI at scale. The gap is the layer between users, agents, systems and models: how traffic is routed, how data is reached, and who is allowed to do what
Learn what an AI gateway can and should do for you
This e-book works through the AI gateway from first principles: why it emerged, what it controls, where routing alone falls short, and how to measure whether it's working once it's live. You'll walk through eight short parts covering LLM gateways, MCP gateways, and the governance, context, and connectivity layers that most gateways overlook today.
Read it in order or jump to the part you need. Either way, our aim is to give data and IT leaders a clear, vendor-honest map of the category before you commit budget to it.
Why enterprise AI is over budget and under-delivering
What is an AI gateway, and why it matters now
What an MCP gateway does that an API gateway can't
What is an LLM gateway, and where it fits in the stack
Why a gateway without context just moves the problem
What to look for in a context layer
Use cases an enterprise AI gateway makes possible
How to measure AI gateway success after deployment
