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An AI inference gateway is a software layer between an application and one or more AI model providers. The application sends requests to a stable gateway endpoint; the gateway maps the requested model to configured provider targets and can apply routing, access, and operational policies before forwarding each request. Depending on the implementation, it may be a standalone proxy or part of a broader API gateway platform.
Where an AI inference gateway fits
Without a gateway, an application typically connects directly to a model provider. With one, the request path becomes application → gateway → model provider. AWS describes inference architecture in its inference guidance; Kong and LiteLLM document gateway implementations that can route requests to model providers.
The gateway gives the application an intermediary endpoint. That can make it possible to change provider targets or apply shared controls without putting every provider integration and policy in each application. The exact capabilities depend on the gateway and its configuration.
How a gateway routes model requests
Model names map to provider targets
An application can request a model name or alias. The gateway resolves that name to one or more configured upstream targets—such as a model offered by a particular provider—and forwards the request. Kong describes this model-to-provider mapping in its AI Gateway documentation.
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Policies choose among eligible targets
If multiple targets are configured, a gateway may distribute requests round-robin, use priorities or weights, or select based on factors such as connections, latency, usage, or cost. Some implementations also offer semantic routing, which selects a target based on the request’s meaning. LiteLLM documents weighted, rate-limit-aware, least-busy, latency-based, and cost-based strategies in its routing documentation; Kong documents routing and balancer options in its AI Gateway documentation.
These are implementation options, not features every gateway provides. A routing decision also does not establish that the selected model will produce an equally accurate or appropriate answer. Teams need to define eligible targets and selection criteria, then evaluate output quality on their own workloads.
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Retries and failover address upstream problems
A gateway can be configured to retry a request or send it to another target if an upstream is unavailable. Load balancing and failover can help manage availability, but their behavior depends on the gateway’s implementation, policy, and configuration. They are not a guarantee that every request will succeed or that a replacement target will behave identically.
How gateways help govern requests
A shared gateway can enforce controls at the boundary between applications and providers. Depending on the implementation, these can include:
- Caller authentication and credentials: authenticate applications or users and centralize provider credentials rather than distributing provider keys across application code.
- Model access: restrict which consumers may call particular models or provider targets.
- Usage limits: apply request or token limits to manage usage.
- Safety and data handling: filter prompts or responses, or redact personally identifiable information.
- Usage records and observability: record or report request counts, token use, errors, latency, and cost.
Kong documents authentication and model policies in its AI Gateway documentation. Its documented flow uses a consumer’s assigned authentication strategy before attached model policies execute; that ordering is a Kong-specific example, not a universal gateway rule. LiteLLM describes related access, limits, and monitoring capabilities in its documentation.
A gateway centralizes controls, but it does not by itself establish regulatory compliance or remove the need to secure the gateway infrastructure and upstream providers. Teams still need to decide what data may be sent, configure policies appropriately, protect credentials and logs, and verify that controls work as intended.
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What to compare when evaluating a gateway
Gateway products differ in both capability and operating model. Compare the following against your application’s requirements:
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- Provider and API support: which model providers and request formats can the gateway handle?
- Routing behavior: does it support static targets, weighted or health-aware selection, latency- or cost-based routing, or semantic routing?
- Recovery behavior: how are retries and failover configured, and what happens when all eligible targets are unavailable?
- Governance: can you authenticate callers, control model access, set limits, filter content, and redact sensitive data?
- Deployment and data control: where does the gateway run, and who controls provider credentials, request handling, and logs?
- Visibility: can operators inspect usage, token consumption, latency, errors, and costs at a useful level of detail?
- Integration and operations: what changes are needed in applications, and what work is required to configure, monitor, and maintain the gateway?
Official product documentation can establish which features a vendor describes, but it does not provide a neutral benchmark showing that one routing strategy or product is best for every workload. Test candidate configurations against your own requirements and evaluate both operational behavior and model output quality.
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