LLM Gateway
An LLM gateway is a central proxy layer that routes, logs, secures, caches and meters all model and agent calls in a company — analogous to an API gateway.
An LLM gateway is a central proxy layer that routes, logs, secures, caches, and meters all model and agent calls within an enterprise context, similar to an API gateway.
Explanation
Portkey, Kong AI Gateway, Cloudflare AI Gateway and Google Vertex AI Gateway emerged as a 2026 standard layer.
Marketing Relevance
Without a gateway, cost attribution, security and vendor switching are barely manageable.
Example
All marketing and sales agents run through one gateway with unified auth, caching and logging.
Common Pitfalls
A gateway can itself become a single point of failure — HA and multi-region are mandatory.
Origin & History
LLM Gateways emerged from 2024 onwards due to the necessity of structuring the proliferation of LLM applications within enterprises. Major providers such as Google Cloud (Vertex AI Gateway) and Microsoft Azure (Azure AI Content Safety), alongside specialized startups like Guardrails.ai and Portkey.ai, offered comprehensive solutions from 2025 to ensure governance and cost control over disparate LLM workloads.
Marketing Use Cases
Ensure compliance: Automatically filter and mask sensitive customer data before sending it to external LLM models.
Optimize costs: Intelligently route requests to the most cost-effective LLM model available for a given task.
Increase performance: Cache frequent requests to minimize response times for generative AI applications in customer interactions.
Prevent shadow IT: Establish a central control plane for all AI model usage within the company to enforce security policies.
Frequently Asked Questions
What concrete value does an LLM Gateway offer our agency?
An LLM Gateway centralizes the management and security of all AI model usage, minimizes data protection risks through automatic filtering of sensitive data, and simultaneously optimizes operating costs through efficient routing and caching of requests.
Do we need to rewrite our existing AI applications to use an LLM Gateway?
In most cases, this is not necessary. LLM Gateways are designed to act as a proxy in front of your existing applications, facilitating integration through standard API interfaces. Adaptation is minimal, as they mediate between the application and the LLM.
How does an LLM Gateway impact the latency of our AI-powered services?
By caching frequent requests and intelligently routing to more performant or geographically closer models, an LLM Gateway can actually reduce the latency of your services. It also enables better management of peak loads and increased resilience.
Related Services
Go deeper: Agentic AI Hub · Governance & compliance