What Is an AI Gateway? Architecture, Controls and Use Cases | KrakenD AI Gateway

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Head of Technology, Ahgora Sistemas
Head of Technology, Ahgora Sistemas
Tech Lead Cloud Infrastructure, IBM
Platform Engineering Lead / Culture Amp
An AI gateway is a centralized control layer that manages traffic between applications and AI services, including large language models (LLMs).
It routes model requests, enforces security policies, controls usage, and provides observability across AI workloads.
For platform engineering teams, the challenge is maintaining consistent controls as applications, models, providers, and traffic volumes increase.
McKinsey’s August 2026 State of AI survey found that approximately 20% of respondents reported that AI operating costs, including token costs, constrained their organization’s AI usage.
An AI gateway helps address these operational challenges by providing a common enforcement point for AI traffic.
An AI gateway is a specialized API traffic management layer that connects applications to AI models while enforcing routing, access, security, and consumption policies.
An LLM gateway is the more specific term for a gateway focused on language models. Both terms often overlap in enterprise AI architecture.
Without centralized controls, applications may independently manage provider credentials, retries, authentication, usage tracking, and error handling.
Platform teams introduce an AI gateway to standardize these responsibilities and reduce duplicated operational logic.
Its primary responsibilities include:
An AI gateway does not train models or replace inference infrastructure. It governs how applications access those services.
An AI gateway operates between client applications and model endpoints. It can extend an existing API gateway or form a separate infrastructure component.
Two logical responsibilities define its architecture: the control plane determines policies, while the data plane processes requests according to those policies.
These responsibilities do not necessarily require physically separate components.
This request lifecycle establishes the relationship between architecture, security, routing, and observability.
Trust boundaries remain essential. Application identity, provider credentials, and tenant permissions must be validated rather than accepted from untrusted request metadata.
LLM routing determines which model or provider receives an inference request. Routing rules may consider permissions, model capabilities, availability, or application requirements.
Provider abstraction allows applications to communicate through a stable interface while infrastructure teams manage underlying endpoints.
However, model features, tokenization, streaming formats, and supported operations can differ. An AI gateway does not automatically eliminate every compatibility issue.
Failover and fallback solve different problems. In KrakenD’s AI Router, configured failover does not automatically handle requests that match no route.
A final route without conditions can handle unmatched requests. Without that route, the gateway returns an error when no routing rule matches.
Failover also cannot guarantee recovery after a response has partially streamed. Different models may produce different outputs for equivalent requests.
Semantic caching matches requests by meaning rather than exact text. Its effectiveness depends on similarity thresholds, response suitability, and tenant isolation.
KrakenD Enterprise implements semantic caching using an Open Neural Network Exchange (ONNX) embedding model and Redis storage. Configurable cache partitioning helps separate responses by user or tenant.
AI gateway security combines traditional API protection with controls adapted to LLM traffic, such as prompt inspection and model-specific access restrictions.
AI governance establishes rules governing who can use AI resources, what operations are permitted, and how usage decisions are recorded.
Key security controls include:
The OWASP Top 10 for LLM Applications 2025 identifies prompt injection, sensitive information disclosure, and unbounded consumption as important security risks.
Guardrails can mitigate selected threats but cannot guarantee complete protection against prompt injection or unsafe model behavior.
In KrakenD Enterprise, Prompt Guard evaluates incoming request bodies using predefined patterns, regular expressions, Common Expression Language (CEL) policies, or external classifiers.
Response validation and masking require appropriate response-processing controls. They should not be confused with the incoming-request checks performed by Prompt Guard.
Provider credentials can be managed through centralized configuration. KrakenD recommends injecting sensitive credentials through environment variables rather than storing them directly in configuration files.