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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 can map the requested model to a provider target, apply routing and access policies, and forward the request. Depending on the implementation, it may be a standalone proxy or part of a broader API gateway platform.
Where the gateway sits in an AI request
Without a gateway, an application typically connects directly to a model provider. With one, the request takes an intermediary path: the application calls the gateway, the gateway applies its configured rules, and an eligible provider receives the request. AWS describes inference as part of an application architecture, while Kong and LiteLLM document gateway implementations that route model requests to providers.
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That shared boundary can simplify how an organization connects applications to multiple models. It can also centralize settings that would otherwise be implemented separately in each application. The gateway does not make the models themselves interchangeable: supported providers, API formats, model behavior, and the policies a team configures all matter.
How an AI gateway routes requests
A gateway can associate an application-facing model name or alias with one or more upstream provider targets. When a request arrives, its routing policy selects a target from the configured options. Kong documents model-to-provider routing and multiple balancing algorithms; LiteLLM documents strategies that include weighted, rate-limit-aware, least-busy, latency-based, and cost-based routing.
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Common routing strategies
- Fixed or priority routing: Send requests to a designated target, or prefer targets in a specified order.
- Round-robin, weighted, or least-busy routing: Distribute requests across eligible targets using an even sequence, configured proportions, or current load.
- Latency-, rate-limit-, or cost-aware routing: Prefer targets according to observed response time, provider limits, or configured cost criteria, where the implementation supports those signals.
- Semantic routing: Select a target based on the request’s meaning or task rather than only operational signals. This requires a configured approach to classifying or matching requests.
These are implementation options, not features that every gateway provides. A team should establish which targets are eligible and what conditions trigger selection, retries, or failover. Kong and LiteLLM document examples of these capabilities in their respective products: Kong documentation and LiteLLM documentation.
Retries and failover
If an upstream is unavailable or returns an error, a gateway may retry the request or send it to another target. The exact behavior depends on configuration and implementation: which errors qualify, whether another provider can accept the same request, and how many attempts are allowed all affect the result. Retries can improve resilience, but they can also add delay or create additional upstream work; teams need to set and test these policies rather than assume failover is automatic.
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Routing does not measure answer quality
A lower-cost or lower-latency route is not proof that the response will be equally accurate or suitable. Product documentation describes available routing mechanisms, not a universally best target for every task. Teams should evaluate their own workloads and define quality requirements alongside cost, speed, and availability criteria.
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How gateways govern model use
Because requests pass through a shared boundary, a gateway can enforce common controls across applications. Depending on the product and configuration, these may include:
- Authenticating callers and managing access to provider credentials.
- Restricting consumers to approved models or providers.
- Applying request or token limits and other usage policies.
- Filtering prompts or responses, or removing personally identifiable information.
- Recording usage and operational events for review.
Kong’s documentation provides a product-specific example of policy order: consumer authentication uses an assigned authentication strategy before attached model policies execute. Other gateways may order or implement controls differently, so check the behavior of the chosen platform.
Credentials, data, and compliance
Centralizing credentials can reduce the need to distribute provider keys across applications, but the gateway itself becomes a security-sensitive component. Its deployment, access controls, secrets, logs, and connections to upstream providers still need protection. A gateway’s presence does not by itself establish regulatory compliance, guarantee that sensitive data is excluded from provider processing, or replace a review of provider terms and data handling.
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What teams can observe
Gateway observability may report request counts, token use, errors, latency, and cost. These signals can help teams see how applications use models and diagnose operational issues. They are only as useful as the gateway’s instrumentation, retention settings, and reporting, and they should be interpreted with the provider’s own usage and billing information where relevant.
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Compare gateways against the systems and controls your applications actually need. Useful questions include:
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- Which model providers and API formats does it support?
- Can routing be fixed, weighted, health-aware, latency- or cost-aware, or semantic? Can you define eligible targets and fallback behavior?
- How are retries and failover triggered, limited, and reported?
- What authentication, model-access, and rate-limit controls are available?
- Can it filter prompts and responses or redact sensitive data, and where do those controls run?
- Where can the gateway be deployed, and how are provider credentials and request logs controlled?
- What visibility does it provide into usage, tokens, errors, latency, and cost?
- What integration and operational work is required to keep policies, provider connections, and routing rules current?
Official documentation can establish that a product describes particular features; it does not provide a neutral benchmark of comparative performance. Evaluate candidate gateways with representative requests and policies before relying on them for production traffic.
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