The AI Gateway
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. This series works through the AI gateway from the ground up: why it emerged, what it controls, where routing alone falls short, and how to measure whether it's working.
Eight parts, written for enterprise IT leaders and data architects.
- Why enterprise AI stalls
- What an AI gateway actually controls
- MCP gateway vs API gateway vs LLM gateway
- Why routing without context isn't enough
- How to evaluate and measure your gateway
The series
Why Enterprise AI Is Over Budget and Under-Delivering
93% of enterprises are already over their AI budgets. Three structural gaps account for most of the waste: routing, data access, and governance.
What Is an AI Gateway, and Why It Matters Now
An AI gateway is the centralized control layer that sits between your AI applications and the models, tools, and data they use. Here's what it controls and why it's a distinct category from what came before.
What an MCP Gateway Does That an API Gateway Can't
An API gateway routes HTTP requests. An MCP gateway routes intent, enforces tool policies, and governs what your agents are allowed to do. The distinction has real architectural consequences.
What Is an LLM Gateway, and Where It Fits in the Stack
The LLM gateway is the model-routing layer inside a broader AI gateway. It handles model selection, cost optimization, and failover but it doesn't solve the data problem.
Why a Gateway Without Context Just Moves the Problem Coming soon
A routing gateway that controls model traffic without governing data access just moves the failure point downstream. Here's what the context layer is and why it isn't optional.
What to Look for in a Context Layer Coming soon
Not all context layers are equal. What to look for when evaluating the data-access layer of your AI gateway, and what separates real data governance from a proxy.
Use Cases an Enterprise AI Gateway Makes Possible Coming soon
The use cases that a properly architected AI gateway empowers, from natural language queries over enterprise data to AI-powered BI without SQL.
How to Measure AI Gateway Success After Deployment Coming soon
ROI tracking, audit logging, cost-per-outcome metrics: how to know your AI gateway is working and what success looks like in production.
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