What Is the Purpose of an AI Gateway?
An AI gateway is the centralized control plane that sits between applications, agents, and AI models to route, secure, and govern all AI traffic. It plays the role for AI traffic that an API gateway plays for service traffic, adding capabilities built for large language models (LLMs), including model routing, token tracking, and prompt safety. Also known as an LLM gateway, an AI gateway enables an organization to enforce consistent policies about routing, access, spending, and security.
Key Takeaways
- An AI gateway is a centralized control plane that routes, secures, and governs all AI traffic
- Without an AI gateway, enterprises lose visibility and control as AI use multiplies across teams
- An AI gateway gives enterprises one place to control model access, usage, and cost
- An AI gateway is the API gateway for AI traffic, adding model routing, token budgets, and guardrails.
How an AI Gateway Works
As a single entry point for all AI requests across the enterprise, an AI gateway is the endpoint for every model call, tool invocation, and agent action sent by applications and agents. The gateway converts each request into a common format, checks it against policy, selects the appropriate model, and forwards it accordingly, then returns the response along the same path. Any request that violates policy or exceeds budget limits is stopped at the gateway.
Why AI Gateways Are Emerging Now
Enterprise AI has outgrown a single chatbot connected to a single model. Organizations now run many models across their teams and tasks, each with different capability, cost, and performance requirements. Even a single AI agent can chain together requests to multiple models in the course of one task. When AI applications are managed in silos, organizations lack a complete and consistent view of which models are being used by whom, at what cost, and how enterprise data is flowing through these requests. By providing this understanding, an AI gateway makes it possible to improve governance and get token costs under control.
Core Capabilities of an AI Gateway
Unified Model Access and Intelligent Routing
Unified model access lets applications use the same API endpoint to connect with OpenAI, Anthropic, and other providers without the need to maintain a separate SDK for each LLM. New models can plug in with no code changes. As the gateway receives each request, it routes it intelligently to the best-fit model based on cost, latency, task type, and availability, sending simple prompts to smaller models and complex prompts to more capable ones. If a model is unavailable, the gateway falls back to the next capable option while preserving context.
Authentication and Access Control
Before routing a request, the gateway authenticates the caller and applies policies by user or group, syncing with the identity and access management (IAM) system to maintain consistency. Virtual API keys abstract upstream credentials held in a secure vault. Organizations can apply least-privilege scoping to limit each agent and tool to the models it requires.
Cost Management and Budgets
An AI gateway tracks and controls AI spend in real time by setting token budgets per model, team, and user; recording each request’s cost; alerting owners as spending nears a limit; and blocking requests when hard limits are reached. As budget is consumed, requests cascade to the next model in a defined chain. Finance teams can see costs as they accrue rather than waiting for monthly invoices.
Observability and Rate Limiting
Operations and security teams can monitor internal utilization as well as LLM traffic and performance through a single point of control. Organizations can record how AI is used by individual teams, and use rate limiting to ensure fair allocation of shared capacity. Caps on requests per minute (RPM) and tokens per minute (TPM), enforced per key or team, can stop a single noisy caller from draining capacity.
Guardrails and Data Protection
Prompts and responses are screened to block unsafe content and protect sensitive data. Checks can include prompt safety and injection protection, masking of personally identifiable information (PII), and pattern-based filtering against configurable rules. For deeper LLM security, organizations can integrate the gateway with dedicated AI security platforms.
Data Sovereignty
Depending on their compliance needs, organizations can choose to deploy their AI gateway on-premises, in a private cloud, or in an air-gapped environment, and in software or hardware form factors. Organizations with regulatory requirements for data sovereignty can keep AI under their own control within a single-tenant deployment, while others may opt for a cloud-hosted gateway in which the provider retains some control.
AI Gateway vs. API Gateway
Both AI gateways and API gateways act as single entry points that authenticates callers, enforces policy, limits request rates, and logs traffic. In each case, the gateway centralizes control that would otherwise be scattered across individual applications and allows administrators to make policy changes without modifying individual services.
What an AI Gateway Adds
An AI gateway provides LLM-specific functions for model routing, token tracking, and prompt safety. It also attributes a cost to each call for budgeting and financial management, and carries conversational context when a request moves between models.
Where an API Gateway Falls Short for AI
API gateways meter and route by request count, path, and service, while treating each call as roughly equivalent. AI requests vary enormously. A one-line summary and a multi-step financial analysis have very different implications for token cost, latency, and required model capability. An API gateway is unable to provide the level of governance this complex variability requires.
The AI Gateway as the ADC for AI Traffic
Application delivery controllers (ADCs) began as load balancers and grew into the policy and visibility layer for application traffic. AI gateways are following a similar path for AI traffic, as explored in this discussion of how the AI gateway is evolving into an intelligent control plane.
AI Gateway Use Cases
The most common enterprise AI gateway use cases and supporting capabilities include:
| Use Case | What the AI Gateway Provides |
|---|---|
| Multi-model application development | One API across every model |
| Enterprise AI cost control | Real-time tracking and token budgets |
| Governing AI agents at scale | Identity-based access and policy |
| AI coding assistant rollouts | Managed access, usage, and spend |
| Sovereign and regulated AI | On-premises or air-gapped control |
| Customer-facing AI agents | Reliable routing with guardrails |
What to Look for in an AI Gateway
Context-aware Intelligent Routing
Basic AI gateways forward requests by static rules, such as always sending a given team to a given provider. A full-featured enterprise AI gateway makes routing decisions individually based on the model pools, priorities, and fallback order defined by administrators. Fallback behavior during a model failure or exhausted budget is an important consideration for performance in production.
Identity-based Access Via the Enterprise Directory
Shared API keys treat every caller as the same anonymous caller. An AI gateway should update policies automatically as users join, change teams, or leave, and support individual exceptions without requiring new groups.
Real-time Cost Visibility and Budget Control
Cost reporting should roll up in a way that fits the organization’s budgeting processes. To prevent disruption, budget owners should be alerted before hard limits are reached. Budget controls and rate limits work best when managed through the same set of policies, keeping cost and capacity controls consistent.
Built-in Filtering Rules and AI Security Integration
Teams should be provided with out-of-the-box filtering rules that they can update as policies evolve, as well as the ability to configure new rules. To extend inspection beyond its own filters, the gateway should integrate with a wide range of AI security platforms.
Deployment Flexibility
Sensitive workloads and regulated data call for an isolated, single-tenant deployment that keeps prompts and responses inside the enterprise environment. Infrastructure teams should also be able to choose between software and hardware form factors.
Best Practices for Deploying an AI Gateway
- Route every AI request through a single gateway Any direct model call bypasses routing, budgets, guardrails, and logging
- Tie access policies to identity from day one Connecting the gateway to existing directory systems at rollout ensures that every request will have an accountable owner
- Set token budgets and rate limits per model, team, and user Granular caps keep a single team or runaway agent from draining shared budget and capacity
- Decide which work warrants premium models Define which teams, applications, and task types justify top-tier reasoning models, and let routing send all others to lower-cost options
- Enable guardrails for prompt safety and PII protection at the gateway Gateway-level guardrails ensure that rules are applied uniformly to agents and applications, and spare development teams from having to build their own filtering
- Monitor usage, cost, and performance continuously across all models Regular review shows where routing rules, budgets, or model choices need adjustment as usage patterns shift
- Decide where the gateway will run before evaluating vendors Data residency, regulatory, and sovereignty requirements should be a primary screen for solution selection
How the A10 AI Gateway Delivers Intelligent AI Control
The A10 AI Gateway brings enterprise AI governance and cost control into a single control plane across all of an organization’s AI agents, applications, and LLMs.
Complexity-aware Routing at Low Latency
The A10 AI Gateway classifies each prompt by complexity and routes it automatically to the best-fit model pool. Classification and routing add low latency to a typical request.
Identity-based Access and Virtual API Keys
Policies apply by users and groups, in sync with existing directory systems. Virtual API keys carry their own budgets and rate limits, and provider credentials stay in a secured vault.
Real-time Cost Tracking and Token Budgets
The gateway tracks each request’s cost in dollars and enforces token budgets and rate limits at the model and team level. Budget owners receive alerts as spending approaches a limit.
Guardrails and TrojAI Security Integration
Built-in pattern-based filtering screens requests and responses. TrojAI by A10 Networks extends this protection with runtime defense against prompt injection, jailbreaking, and data leakage. The gateway is an integrated element of A10’s approach to AI security.
Sovereign Deployment, On-premises or Air-gapped
The A10 AI Gateway is available as standalone software or with integrated hardware. It runs on-premises, in private clouds, or in air-gapped environments as a single-tenant deployment.
FAQs
An AI gateway evaluates each request on task complexity, task type, cost, latency, and model availability, then sends it to the best-fit model within the pools and priorities defined by administrators. If the chosen model is unavailable or over budget, the gateway falls back to the next capable model while keeping conversation context intact.
Yes. Multi-provider support is a defining feature of an AI gateway. Applications connect to a single API endpoint that can forward requests to LLM providers such as OpenAI, Anthropic, and Google. Teams can switch providers or add models through gateway configuration changes without changing application code, and upstream credentials remain centralized.
Yes. An AI gateway attaches a specific cost to each request and tracks a running total by user, team, application, task, and model. This cost-per-task view is especially valuable for agentic workflows, where a single task can trigger multiple model and tool calls, and it enables enforceable control through budgets, rate limits, and lower-cost routing.
Yes. An AI gateway deploys as software or integrated hardware, on-premises, in a private cloud, or air-gapped, depending on data residency needs. Deployment starts by connecting it to the corporate directory and upstream model providers, then pointing applications and agents at the gateway endpoint. Set budgets, rate limits, and guardrail rules before routing production traffic.
No. An AI gateway and an AI firewall serve different roles. The gateway governs AI operations, establishing who is making each request, what that identity may access, which model handles it, and what it costs. An AI firewall inspects prompts and responses for threats such as prompt injection, jailbreaks, and sensitive data leakage. The gateway supplies the identity context that ties firewall findings to specific users and agents.
Yes. AI gateway and LLM gateway refer to the same thing: a centralized control plane that routes, secures, and governs traffic between applications, agents, and large language models. The term LLM gateway emphasizes the language-model focus, while AI gateway is the broader label.
Yes, for any enterprise running more than one model or AI agent. Without a central control point, teams lose visibility into which models are used, by whom, and at what cost, and governance gets harder as AI use spreads. An AI gateway restores that control from a single plane.
Yes, but only slightly. The gateway classifies each prompt and selects a model before forwarding it, which adds low latency to a typical request. For most workloads, the routing, governance, and cost benefits outweigh that small overhead.
