As AI adoption accelerates, AI gateways are quickly becoming a foundational component of enterprise AI architecture. In many ways, they’re following a familiar evolution (e.g., ADC), and A10 Networks is well-positioned to lead that evolution.
Just as application delivery controllers (ADCs) became the centralized control point for application traffic, optimizing performance, enforcing policies and providing visibility into connections. AI gateways are emerging as the centralized control point for AI traffic. They provide a single point through which organizations can manage AI requests, apply policies and gain visibility into how AI is being used across the organization. The slight difference is…AI is increasingly complex and incredible, which means a modern AI gateway must be intelligent enough to granularly adapt to constant changes to how AI is used, and what AI can do.
The first generation of AI gateways centralized access to AI services, simplified API management, provided visibility into AI use and helped organizations meter requests across different AI providers. Those capabilities remain core to the AI gateway, but enterprise AI has evolved well beyond just connecting users to an LLM.
Today’s AI environments are far more dynamic. Different users and teams require access to different AI resources, such as multiple AI providers, specialized language models, agentic AI, and other AI tools. Every AI request varies in complexity, making some models more appropriate than others. Despite all these changes to how AI is used, and what AI can do, organizations still must find a way to optimize performance, minimize latency, streamline security, and control rapidly growing AI costs.
As such, managing AI has become just as important as deploying it. Now the mic turns to the AI gateway, how will it evolve as well?
Evolving beyond static routing policies, the A10 AI gateway brings intelligence into the routing decision itself. Requests can be evaluated based on factors such as prompt complexity, request type, organizational budgets and identity before being routed to the model best suited for the task. At the same time, organizations can apply identity-based access policies at both the individual and team level, ensuring users have authorized access to the AI resources they need while maintaining centralized visibility and control over use and cost.
As AI continues to expand across applications, users and autonomous AI agents, the AI gateway becomes more than a traffic manager – it’s the intelligent, “evolved ADC” that will take a request, and match it with the right model, for the right user, at the right cost, in a timely manner.
To learn more about A10 AI Gateway, download this solution brief: A10 AI Gateway: The Intelligent Control Plane for Enterprise AI.
An AI gateway manages AI traffic the way an ADC manages application traffic. It authenticates users and agents, routes each request to the model best suited for the task, enforces budgets and rate limits, and gives teams centralized visibility into AI use. Modern AI gateways route intelligently based on prompt complexity, request type, and identity rather than static rules.
An AI gateway authenticates and authorizes the users and agents requesting access to a model, then routes each request. An AI firewall operates after access is granted, inspecting prompts and responses to block threats like prompt injection, data leakage, and model theft. The two work together: the gateway controls access; the firewall inspects content.
Enterprise AI is now multi-model, multi-team, and multi-provider, which creates cost, security, and visibility challenges that direct model integrations cannot solve. An AI gateway centralizes these concerns, so teams manage AI traffic from one control plane. It optimizes performance, enforces identity-based access, controls rapidly growing AI spend, and simplifies securing AI operations at scale.
Traditional AI gateways decide where requests go using static routing policies. The A10 AI Gateway decides where requests belong, evaluating prompt complexity, request type, organizational budgets, and identity before routing to the best-suited model. It applies identity-based access at individual and team levels while maintaining centralized visibility and control over AI use and cost.
No. An API gateway manages traditional API traffic with authentication, rate limiting, and schema validation. An AI gateway operates at the model-call layer, routing requests across multiple AI providers and models, metering token usage, enforcing AI-specific budgets, and adapting to prompt complexity. AI traffic is dynamic and non-deterministic, which requires controls a standard API gateway does not provide.
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。