Fastly introduced AI Runtime Control on September 20, presenting a gateway for managing requests sent to AI model providers.
The company’s announcement describes bring-your-own-provider-key operation, virtual keys for applications or users, spending controls and request visibility. It also includes ordered provider failover and an optional AI firewall.
A shared gateway adds an enforcement point
When several applications call model providers directly, it can be difficult to apply consistent limits or understand which application produced a bill. A gateway offers one place to apply those controls.
That arrangement also becomes part of the application’s dependency chain. Teams need to understand how limits behave, what happens when a provider fails and whether a fallback changes the model producing the answer.
A successful fallback is not necessarily an equivalent result. Models can differ in capability, output format and data-handling terms.
Logging needs its own review
Visibility into requests can help investigate cost and failures. Request and response logs can also contain sensitive prompts or generated material.
A deployment should specify what is recorded, who can access it and how long it remains available. Those decisions should be made alongside the routing configuration.
The presence of a firewall feature does not establish that prompt injection or other model-related risks are solved. Its scope and behavior need testing against the application’s actual threat scenarios.
Our AI workflow cost guide explains how tools, retries and review contribute to a completed job’s cost. OSBBD has not independently tested Fastly’s product.
