Glossary · 4 minute read
What Is an AI Gateway? The Control Plane for LLM Traffic
An AI gateway is a proxy layer that sits between applications and model providers, exposing one interface while handling authentication, routing across models and providers, fallbacks, caching, rate limits, cost metering, logging, and policy enforcement for every call. It centralizes controls that would otherwise be duplicated in each application and makes model changes configuration rather than code.
Once more than one application calls language models, the same problems appear everywhere: each app manages its own keys, picks its own model, handles provider outages differently, and nobody can say what AI costs by feature. An AI gateway solves this by putting one control plane in front of every model call. This explainer covers what a gateway does, how it differs from an API gateway, when to add one, and how to choose between building and buying, drawing on FISTA Solutions' AI enablement practice. The build guide is in how to build an llm gateway and the routing logic in what is an llm router.
What is an AI gateway?
An AI gateway is a proxy service that presents applications with a unified interface to language and other AI models while enforcing organizational controls on every call. Applications authenticate to the gateway; the gateway authenticates to providers, selects the model, applies policies, caches where allowed, records usage and cost, and returns the response. Providers, models, and keys become configuration managed in one place. The economics it meters are in llm token cost explained.
What does an AI gateway do?
| Function | What it provides |
|---|---|
| Unified API | One interface across providers and models |
| Authentication and authorization | Caller identity, per-feature permissions, provider key custody |
| Routing | Model selection by task, cost, latency, or policy |
| Fallbacks | Automatic failover when a provider errors or degrades |
| Caching | Exact and semantic caching of repeated requests |
| Rate limits and budgets | Per caller, per feature, per period |
| Cost metering | Tokens and cost attributed to callers and features |
| Logging and tracing | Every call recorded for observability and audit |
| Policy enforcement | Redaction, content filters, allowed-model lists |
Cost attribution built on gateway data is in how to build an ai cost dashboard.
How does an AI gateway differ from an API gateway?
A conventional API gateway handles HTTP concerns: authentication, rate limiting, routing by path. An AI gateway understands the semantics of model calls: tokens as the unit of cost, prompts as content that may need redaction, models as interchangeable resources with different capabilities and prices, and responses that can be cached by meaning. Many organizations run both, with the AI gateway behind the API gateway. Caching mechanics are in what is prompt caching.
When should you add an AI gateway?
When several applications or teams call models; when more than one provider is used or planned; when cost must be attributed and budgeted; when security or compliance requires central logging, redaction, and allowed-model control; and when provider outages must fail over automatically. A single small application does not need one, but most organizations reach the threshold quickly. Fallback design is in what is a fallback model.
How does a gateway support security and compliance?
Provider keys never reach application code; callers are identified per feature; sensitive content can be redacted before leaving the environment; allowed-model lists enforce data residency and vendor policy; and every call is logged for audit. The gateway is also where prompt injection screening and output policy checks can run consistently. Security patterns are in the AI agent security architecture whitepaper and leakage controls in ai data leakage prevention.
Should you build or buy an AI gateway?
Managed and open-source gateways cover standard routing, caching, metering, and logging and are the right default when data residency and policies are conventional. Building a thin gateway is justified when policies are unusual, traffic must stay entirely within your environment, or integration with internal identity, billing, and observability is deep. Many teams adopt an open-source gateway and extend it. Platform selection is in the AI procurement for CIOs whitepaper.
What are the risks of a gateway?
It becomes a single point of failure and must be highly available; it adds latency, so it must be lean; its logs hold sensitive content and need retention and access controls; and centralizing policy can bottleneck teams if change control is slow. Design for availability, low overhead, redacted logging, and self-service configuration. Availability patterns are in the LLM production readiness whitepaper.
What does an AI gateway look like in practice?
An enterprise with a dozen AI features routes everything through one gateway. Support features use a mid-tier model with fallback to a second provider; document extraction uses a stronger model with batch pricing; internal tools are capped by monthly budget. Prompts are screened for personal data before leaving the environment, every call is attributed to a feature and cost center, and a provider outage one afternoon fails over automatically without any application change. Observability practice is in the AI observability whitepaper.
How FISTA Solutions delivers AI gateways
FISTA Solutions deploys or builds gateways as the first piece of an AI platform, configures routing, fallbacks, caching, budgets, and redaction to client policy, integrates identity, cost reporting, and observability, and operates them to availability targets. The AI enablement practice delivers the platform, AI agents run through it, and forward deployed engineers embed with client platform teams. The record behind the approach is 150+ projects with 99.9% uptime.
To put one control plane in front of every model call, message FISTA on WhatsApp, or read how to build an llm gateway for the implementation steps.
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01What is an AI gateway in simple terms?
A single front door for all the AI model calls an organization makes. Applications send requests to the gateway, which decides which model to use, enforces limits and policies, records what happened and what it cost, and returns the response. Changing models or providers happens at the gateway, not in every app.
02How is an AI gateway different from an API gateway?
An API gateway handles generic HTTP concerns such as auth and rate limits. An AI gateway also understands tokens, prompts, model capabilities, and cost, so it can route by task, cache semantically, redact sensitive content, meter spend per feature, and fall back when a provider degrades.
03When should you add an AI gateway?
When multiple applications or teams call models, when you use or plan to use more than one provider, when cost attribution and budgets matter, when security and compliance require central logging and redaction, or when provider outages need automatic fallback. Single small apps rarely need one.
04Should you build or buy an AI gateway?
Buy or adopt open-source when standard routing, caching, and metering suffice and data residency allows it. Build a thin gateway when policies are unusual, data must stay in your environment, or integration with internal identity and billing is deep. Many teams start with open-source and extend it.
05What does a gateway log?
Request and response metadata, model and version used, tokens and cost, latency, caller identity and feature, policy decisions such as redactions or blocks, and errors and fallbacks. Content logging is configurable and should follow retention and privacy rules.
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