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What is an AI Gateway?
An AI gateway is a software layer that sits between your AI applications and the LLM providers they use. It routes requests to the right model, inspects prompts and responses for security threats, tracks token usage and costs, and enforces the policies your organization sets. Every AI request goes through it. Nothing reaches your LLMs without passing its rules.
TL;DR - Key Takeaways
- An AI gateway centralizes traffic control between your applications and LLM providers from one software layer, replacing scattered per-app instrumentation
- Traditional API gateways treat every payload as opaque: they cannot inspect semantic content, detect prompt injection, or enforce AI-specific policies
- The six core capabilities of an AI gateway are: intelligent routing, runtime security, observability, cost control, rate limiting, and authentication
- TrustGate is NeuralTrust's open-source AI gateway, built for enterprise security and governance at scale, with an average inline latency under 100ms
You are running AI in production. Prompts go out, responses come back, and somewhere in that traffic there are data leaks you have not caught, costs you do not understand, and policy violations nobody logged.
An AI gateway puts you back in control. It sits between your apps and your LLMs, inspects every interaction, and enforces the rules you set.
This guide explains what it is, how it works, and what to look for when you need one.
The Problem Nobody Warned You About
You shipped an AI feature. Users love it. The product manager wants five more.
Then someone in security asks: what is actually going in those prompts? Which models are you calling? Is any PII going to OpenAI? Where is the audit log?
Nobody has a good answer.
I see this pattern constantly at enterprises running AI in production. The features ship fast. The control layer comes later. Sometimes never. And as you add more models, more teams, more agents, the gap between what your AI is doing and what you actually know about it gets wider.
An AI gateway closes that gap. Not with extra code scattered across every application. Not with manual reviews. With one central layer that sees everything.
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What Is an AI Gateway?
An AI gateway is a reverse proxy layer built specifically for LLM traffic. It sits between your applications and the model providers you use, whether that is OpenAI, Anthropic, Google, a self-hosted model, or all of the above. Every request goes through it. Every response comes back through it.
Unlike a traditional API gateway, it understands what is in the payload. It reads the prompt. It inspects the response. It knows who made the request, which model handled it, how many tokens it used, and whether the interaction violated any of your policies.
TrustGate is NeuralTrust's open-source AI gateway. It sits between AI applications and LLM providers to enforce security, routing, and observability policies at enterprise scale. It is the technical foundation for what we cover throughout this guide.
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AI Gateway vs API Gateway: The Actual Difference
People ask this a lot. Here is the honest answer: an API gateway handles traffic. An AI gateway understands it.
A standard API gateway manages HTTP traffic: authentication, rate limiting, routing and load balancing. It treats every payload as opaque. It does not know if a request contains a sensitive data query, a prompt injection attempt, or a routine product question.
An AI gateway does the same traffic management work and adds semantic inspection. It reads the content. It can act on what is in the prompt and in the response.
You probably need both. They are not competing products.
| Capability | API Gateway | AI Gateway |
|---|---|---|
| Request routing | ✅ | ✅ |
| Rate limiting | ✅ | ✅ |
| Authentication | ✅ | ✅ |
| Prompt inspection | ❌ | ✅ |
| PII detection and redaction | ❌ | ✅ |
| LLM cost tracking by token | ❌ | ✅ |
| Model fallback routing | ❌ | ✅ |
| Audit log of model interactions | ❌ | ✅ |
| AI policy enforcement | ❌ | ✅ |
Related article: AI Gateways vs. API Gateways: What’s the Difference?
How an AI Gateway Works
The flow takes milliseconds. Here is what happens when a user sends a message to your AI app:
- Request arrives at the gateway from your application
- Authentication check: is this user or service allowed to call this model?
- Security inspection: does the prompt contain injection attempts, PII, or policy violations?
- Routing decision: which model handles this? Based on cost, latency, or current load
- Upstream call: the gateway sends the request (possibly modified) to the LLM provider
- Response inspection: does the model's reply leak sensitive data or violate output policies?
- Logging: the full interaction is recorded: timestamp, tokens used, latency, policy outcomes
- Response delivered back to your application
TrustGate handles this with an average inline latency under 100ms. Your application barely notices.
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The 6 Core Capabilities of an AI Gateway
1. Intelligent Routing
Not every prompt needs your most expensive model. A classification task can run on a smaller, cheaper model. A complex reasoning task might need a larger one. Intelligent routing sends each request to the right model based on rules you define: cost thresholds, latency targets, capability requirements, or fallback logic when a provider goes down.
This is one of the most immediate cost levers available.
Related article: How an AI Gateway Reduces LLM Costs
2. Runtime Security
Your AI application is a new attack surface. Prompt injection is listed as a top threat by OWASP, a crafted input that manipulates the model into doing something it should not do. An AI gateway inspects every prompt against your security policies and blocks or transforms threats before they reach the model.
This works alongside your guardrails layer, not instead of it.
Related article: AI Gateway vs. AI Guardrails: Understanding the Key Differences
3. Observability
You cannot fix what you cannot see. An AI gateway gives you full visibility into every request and response: token counts, latency breakdowns per model and provider, error rates, cost per call, and which teams are generating the most traffic. That is what real LLM observability looks like in production, versus guessing from application logs.
4. Cost Control
LLM costs stack up fast, especially with agents running thousands of calls. An AI gateway tracks usage by team, by application, by model, and by individual user. You can set budget limits, get alerts before costs spike, and route traffic to cheaper providers automatically when thresholds are hit.
Related article: AI Gateway: Centralized AI Management at Scale
5. Rate Limiting
Not just for protecting your provider quotas. Rate limiting in an AI gateway also prevents abuse: a user sending thousands of requests in a loop, an agent that gets stuck and keeps calling the same endpoint, a misconfigured pipeline hammering your most expensive model at 3am. You define the limits. The gateway enforces them without changes to application code.
6. Authentication and Access Control
Who gets to use which model? Your data science team might need access to your highest-capability model. Your customer-facing chatbot should run on a more constrained, cost-optimized setup. An AI gateway enforces those access controls at the request level, not at the application level, so the rules stay consistent even as applications multiply.
Why Enterprises Are Moving Fast on This
IBM's 2025 Cost of a Data Breach Report found that 13% of organizations had experienced a breach involving an AI model or application. Of those organizations, 97% lacked proper AI access controls. That is not a future risk. That is happening right now.
The NIST AI Risk Management Framework identifies monitoring, access control, and incident response as core requirements for responsible AI deployment. An AI gateway is how you operationalize those requirements in production without rebuilding every application.
From the regulatory side: the EU AI Act, broadly applicable from August 2026, requires high-risk AI systems to maintain interaction logs, enforce access controls, and support human oversight. A gateway gives your compliance team documented evidence that those controls exist and are being enforced consistently.
Related article: How AI gateways support data sovereignty and compliance.
Major AI Gateways: How They Compare
Several tools now occupy this space. Here is how the major options compare at a high level:
| Gateway | Open Source | Self-Hosted | Enterprise Security | Agent Support | Data Sovereignty |
|---|---|---|---|---|---|
| TrustGate | ✅ | ✅ | ✅ | ✅ | ✅ |
| Portkey | ❌ | ❌ | Limited | Limited | Limited |
| Kong AI Gateway | Partial | ✅ | Partial | Limited | Partial |
| Cloudflare AI Gateway | ❌ | ❌ | Limited | ❌ | Limited |
| Vercel AI Gateway | ❌ | ❌ | Limited | ❌ | ❌ |
Related articles:
NeuralTrust TrustGate
TrustGate is our open-source AI gateway.
It uses a split-plane architecture: the control plane manages security policies, and the data plane handles LLM traffic. That separation means your data stays in your environment, under your control, without routing through a third-party cloud.
Key features:
- Only one control plane
- Designed for homegrown agents, developer IDEs and third-party agents
- +200 MCP servers available
- 3 in 1: AI Gateway, MCP Gateway & A2A Gateway
- Performance: latency under 100ms, +20k request per second per node, 99% detection, 100% data sovereignty
Also, Gartner named NeuralTrust a Representative Vendor in both AI Gateways and Guardian Agents in the 2025 Gartner Market Guide for AI Gateways.
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The Full AI Gateway Series
This article is the hub for our AI Gateway guide. Each article covers one topic in depth:
- AI Gateway vs MCP Gateway: Key Differences Explained
- AI Gateway Security: How to Protect LLM Traffic in Production
- AI Gateway Architecture: How It Works Under the Hood
- How an AI Gateway Solves LLM Observability
- How an AI Gateway Reduces LLM Costs
- How an AI Gateway Solves AI Governance for Enterprise
- How to Choose an AI Gateway: Enterprise Buyer's Guide (2026)
- AI Gateway for Agentic AI: Securing Multi-Agent Workflows
- How to Deploy a Self-Hosted AI Gateway (Step-by-Step)
FAQs about AI Gateways
1. What is an AI gateway?
An AI gateway is a reverse proxy layer built for LLM traffic. It sits between AI applications and model providers to handle routing, security inspection, observability, cost tracking, and policy enforcement for every request and response.
2. What is the difference between an AI gateway and an API gateway?
An API gateway handles HTTP traffic but treats payloads as opaque. An AI gateway understands semantic content: it can inspect prompts, detect and redact PII, enforce AI-specific policies, and track token-level costs. Most production AI stacks need both. Full comparison.
3. What does an AI gateway do?
It manages all traffic between your applications and LLM providers. Core functions: intelligent routing (sending requests to the right model), runtime security (prompt injection detection, PII redaction), observability (request tracing, cost attribution), rate limiting, and authentication and access control.
4. Do I need an AI gateway?
If you are running LLMs in production with real users or real data, yes. The moment you have more than one model, more than one team using AI, or any sensitive data in your stack, you need a central control layer. An AI gateway is that layer.
5. What is the best AI gateway for enterprise?
Key criteria: open-source availability, self-hosting support, security depth, observability coverage, and compliance support. See our enterprise buyer's guide. NeuralTrust TrustGate is built specifically for enterprise security and governance, with a split-plane architecture that keeps data in your environment.
About the Author
Roger Howroyd is Head of Global SEO and AI at NeuralTrust, where he leads the company's search strategy across SEO, AEO, GEO, and LLM optimization. He specializes in AI-powered search, content strategy, and SEM. Connect on LinkedIn.
NeuralTrust is an AI agent security platform recognized in the Gartner Hype Cycle for Application Security 2026, the Gartner Market Guide for AI Gateways, and the KuppingerCole Leadership Compass for Generative AI Defense. ISO 27001 certified. Headquartered in Barcelona.
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