When a team asks which AI gateway to choose, the real question is often larger: can we keep control of our data, run the models we want, and protect our agents without operating a collection of disconnected products?
Our AI infrastructure decision guide starts with those requirements. Select privacy, security depth, model freedom, and operating preferences to narrow the candidate list. It shows the reason an option stays, needs confirmation, or is set aside.
Data and model control come first
Open-weight models can give you the option to run inference locally and pin a version. A self-hosted gateway can govern that traffic. An open-source gateway adds rights to inspect, modify, or fork its code. These are different kinds of control.
For a completely on-prem workflow, check all three layers—and the scanners, judge models, logs, and tools around them. Source availability alone cannot establish that data stays inside your boundary.
Protect the action, and count the latency
Prompt injection and jailbreak filtering are useful baseline controls. An agent can also be manipulated by an apparently legitimate request, a fraudulent claim of authority, or a poisoned tool response. Evaluate the business decision and enforce permissions before the action executes.
Those checks have a cost. Measure time to first token, time to a safe complete answer, task success, and cost per completed task at realistic concurrency. Include security and verification calls. A fast proxy benchmark is not a benchmark of the protected application.
Prefer a manageable system
Our preference is fewer enforcement hops and one accountable operating owner when the required capabilities are available. A separate tool should close a demonstrated gap large enough to justify its integration work, latency, and failure modes. One product can still make many remote calls, so inspect the actual request path.
Compare the edition you will need at scale. Check client and tool compatibility, private availability of advanced features, upgrade work, and the cost of leaving. A free or open-source starting point is valuable only if the complete system remains workable as requirements grow.
Where Cortega fits
Cortega combines model routing, policy checks, endpoint coverage, and model operations, with self-hosted deployment and tooling for local open-weight models. The same questions apply to it: does the chosen configuration cover your clients, enforce your policies, remain within your data boundary, and meet the performance and cost targets?
We publish the guide, so it is not an independent review. Its shortlist includes alternatives, makes uncertainties visible, and can rule out Cortega when a requirement does not fit. Work through your requirements.