- September 3, 2026: ChatGPT, Claude, and Grok experienced outages within hours of one another. The causes were different and, in some cases, unclear.
- July 19, 2026: A cloud infrastructure issue affected ChatGPT and Codex, forcing OpenAI to redirect traffic away from the affected region.
- March 9-10, 2026: Azure OpenAI experienced availability issues affecting GPT-5.2 across seven regions, with customers seeing failed requests and errors.
Outages like these remind us of what can go wrong when AI becomes part of a production workflow. That’s one reason enterprises use an AI gateway: to manage how applications respond when the provider your application depends on goes down, slows down, or starts returning errors.
And availability is only one part of the problem. A recent MIT study found that 95% of generative AI pilots produce no measurable return, while an IBM study says that 68% of enterprises worry that AI initiatives will fail without better integration. So, even if your pilot is successful, integrating it into existing systems, managing access and data, controlling costs, handling failures, and getting the AI into the workflow where people use it are what determine whether it can work in production.
This guide explains how an AI gateway brings several of these controls into one place, when you should consider one, what it can help you achieve, and where its limits show up. We’ll also look at what you can do when a gateway isn’t enough.
Let’s dig in.
What is an AI gateway?
An AI gateway is a control point between an application and the AI models it calls, whether that’s OpenAI, Anthropic, xAI, Groq, or any others that you are using.
It works as a door that every AI request must walk through. Instead of your application calling each provider’s API directly, with its own authentication format, rate limits, and response shape, it calls the gateway. The gateway decides which model will handle the request, enforces who’s allowed to ask for what, logs what happened, and hands back a response in a consistent format.
As enterprises adopt more AI applications, their engineering teams need to figure out which provider to use, manage API keys for each provider, where to route requests, how to handle failures, and set rules for safe prompts for each application.
An AI gateway brings all these controls in one place, so your team can share the same setup across applications and providers.
Why AI Gateways Exist
A decade ago, enterprises faced a similar problem with backend services. They were running more services, and every application needed the same authentication, rate limiting, and routing logic. API gateways gave them one shared layer to manage these controls.
AI creates similar problems at a different layer. Enterprises now use multiple models from multiple providers, each with different APIs, limits, pricing, and failure modes. Managing those differences separately across every application creates more work for your engineering team and more places for governance to break down.
Regulated industries, like a bank or a hospital, can’t afford to wait for an outage to happen before figuring out how their AI applications should respond. If a provider goes down, they need a clear way to route requests elsewhere, retry them, or fail safely. A gateway helps them manage these decisions from one place.
AI Gateway vs. Traditional API Gateway
So if traditional API gateways and AI gateways do the same job, do you need them both? Let’s take a look.
The most common differentiation you’ll hear is that AI gateways add token tracking. That’s true, but it covers only one part of it.
A traditional API gateway is built around a contract: an endpoint takes a defined input, returns a defined output, and behaves the same way every time barring a bug. This predictability is what makes standard gateway features work in the first place:
- Caching works because identical requests produce identical responses.
- Rate limiting works because you can count requests per second and know roughly what load that represents.
- Schema validation works because the shape of a valid request is known in advance.
But these do not hold for an LLM call because two identical prompts can produce different completions. The real unit of cost is the token, not the request, and a single request can burn anywhere from a handful of tokens to tens of thousands. Latency also runs in seconds, sometimes tens of seconds for longer generations, which changes how timeouts and retries get designed.
Security threats shift too: a traditional gateway worries about malformed requests and SQL injection, while an AI gateway inspects the actual content of a prompt for injection attempts and the actual content of a response for leaked data or policy violations.
So, an AI gateway doesn’t replace an API gateway. Both work on different levels and each solves different problems.
That’s why many enterprises end up using both: the API gateway for everything else, and the AI gateway for AI model traffic.
Core Components of an AI Gateway
An AI gateway brings several controls into one layer. These work like a control room for your AI traffic, helping you manage multiple features and models from a single point.
Unified API and Provider Abstraction
The gateway gives applications one API to work with, even when the models behind it come from different providers. Your application can send requests through the same interface whether you’re using GPT, Claude, or Llama. The gateway handles the provider-specific API format, authentication, and response format behind the scenes.
This means adding or testing a new provider doesn’t require every application to learn another SDK.
Routing and Failover
A gateway handles routing based on cost, latency, model capability, or availability. It also handles failover when a provider can’t serve the request by retrying or sending the request to another provider.
There is one important limitation, though: the backup needs to fail independently. If two providers rely on the same underlying cloud infrastructure, routing between them won’t protect you from a shared outage.
Governance and Access Control
IT and security teams need a central place to enforce access rules. A gateway provides role-based access control (RBAC) that determines which users, teams, or applications can use specific models and AI capabilities.
This also creates a record of how these rules are applied. This helps you to see which application made the request and which policy governed it, instead of only knowing that an application called a model.
Caching
Caching can reduce the number of requests sent to a model, which in turn helps you reduce token usage and control costs. With exact-match caching, the gateway returns a stored response when it sees the same request again.
Semantic caching can recognize two prompts that have the same meaning even when the wording is different. This can reduce model calls when applications repeatedly ask similar questions.
Observability and Logging
Logging tells you what passed through the gateway. A useful setup records the request, response, model, latency, and token usage.
Observability goes further by helping teams understand what happened across the request. You can see where a call went, how long it took, what it cost, and whether it failed.
Key Benefits of an AI Gateway
A well-designed AI gateway gives the organization more control over how AI is used, managed, and scaled.
- Manage AI providers from one place.
Large enterprises run over 700 distinct AI-powered applications, as per Salesforce’s State of IT research. A gateway helps you handle this complexity by giving your team a common way to connect to providers and manage their traffic. Each new AI feature can use the same setup instead of adding another custom integration.
- Make AI costs easier to predict.
Semantic caching reduces the number of calls made to a model and helps lower costs. In one production implementation, semantic caching reduced LLM API costs by 73% by increasing the cache hit rate from 18% to 67%.
- Test and switch models without rebuilding applications.
When a new model is launched, your team can test it through the gateway and change the model behind an existing application. This turns a larger engineering project into a platform-level configuration change.
- Give security teams one place to review AI traffic.
Every application that connects directly to a model creates a review. A gateway brings that traffic through a central layer, giving compliance and security teams one place to apply and review policies.
What an AI Gateway Doesn’t Solve
The gateway gives you visibility into what happened, but that doesn’t mean you’re all set. There are a few things an AI gateway doesn’t solve, such as:
The Last Mile Problem
A gateway can log that a model returned a loan-risk summary in 400 milliseconds within policy. But it doesn’t know what steps were taken after that information was passed on.
- Maybe an underwriter read it and made the right call.
- Someone might have copy-pasted it into the wrong file.
- The decision got made before anyone noticed it.
The gap between the AI working correctly and the AI helping is what we call the last mile problem. It’s also one of the reasons why many technically successful systems still fail to deliver value in production and why most pilots don’t get adopted in everyday workflows.
The Accountability Gap
In February 2024, a Canadian tribunal made Air Canada pay a customer $812 after its support chatbot invented a bereavement discount that didn’t exist. Air Canada’s defense team said that the chatbot was “a separate legal entity, responsible for its own actions.” The tribunal said that it didn’t matter if the wrong information came from a chatbot or a static web page; the company still owns it.
This is a classic example of the accountability gap where the system produced an answer, but the organization still had to own the consequences when that answer was wrong. A gateway would have logged that chatbot’s answer perfectly, but it still wouldn’t have caught that the answer was wrong.
This doesn’t mean you shouldn’t opt for an AI gateway. It just gives you a clearer picture of what a gateway can and cannot handle.
We’ve seen this firsthand in Department of Defense systems, federal agencies, and regulated enterprises, where a wrong answer can have real consequences. In these environments, a gateway was only one part of the solution. Policy enforcement had to reach into the workflow, and observability had to trace an output through every system it touched, not just the system that made the call.
That’s the thinking behind the seven layers of the AI Controls Framework we built into UNIFI. The last mile problem and the accountability gap are different failures that an AI gateway can’t solve.
Common AI Gateway Deployment Patterns
Once you know what an AI gateway can handle, the next question is where to put it. There are a few common ways to deploy one, and each comes with different tradeoffs around control, latency, and scale.
Centralized
The centralized pattern deploys one gateway instance that every application routes through, on-premises or in a private cloud. It’s the easiest to govern, since every policy stays in one place, but it adds a network hop to every call and can bottleneck if it isn’t scaled for peak load.
Sidecar
The sidecar pattern deploys gateway logic alongside each application, often as a container in the same pod. Latency drops with no extra hop to a shared service, but keeping policy consistent across dozens of independently upgraded sidecars gets harder as application count grows.
Embedded SDK
The embedded SDK pattern skips a network service entirely and builds gateway logic straight into application code as a library. It’s the fastest option and the hardest to govern centrally: updating a policy means redeploying every application that imported it, not pushing one config change.
Hybrid
Organizations usually prefer a hybrid: a centralized gateway for anything touching sensitive data or requiring audit-grade logging, with lighter embedded logic for latency-sensitive internal tools where the governance bar is lower.
How to Choose an AI Gateway
If every vendor pitch you have come across says ‘we do it all’, you are not alone. Features such as multi-provider support, semantic caching, and RBAC that used to differentiate a gateway are now bare basics.
That’s why they all sound identical, and that’s where your confusion begins.
To make the choice easier, start with what each team needs the gateway for.
- Platform Engineering: The goal is to reduce custom integration work and make provider changes easier to manage. Can the team add a new provider through configuration, or does every new model require code changes?
- Compliance and Risk: Can the audit trail show who accessed a model, which policy was applied, and what happened to the request? Can access connect to the identity system the organization already uses?
- Business Unit Leaders: Does the AI feature work inside the tools their teams already use, or does it remain stuck in a demo or separate application?
All three perspectives are important while evaluating a gateway for enterprise use. If it works for engineering but creates gaps for compliance or passes security review but never reaches the workflow the business needs, the implementation can stall later.
Conclusion
Before you choose an AI gateway, understand what it relies on.
For every provider you plan to use, identify the cloud infrastructure underneath it. Then map the critical workflows that depend on those providers. If two providers share the same underlying infrastructure, treat them as one failure domain when you design your fallback strategy.
Do the same with your controls. Understand which policies live in the gateway, which ones live in the application, and which ones still depend on the systems around it. This map shows you much more about your resilience than a feature checklist ever will.
If you’re looking to bring those controls together, see how AISquared brings AI traffic, governance, and control into one layer.
Frequently Asked Questions
What is the difference between an AI gateway and an LLM gateway?
An LLM gateway typically refers to a gateway focused on large language models. An AI gateway is a broader term that can cover LLMs alongside other AI model types (imaging and embedding) and capabilities.
Do I need an AI gateway for a single-model application?
No. With one provider and low volume, the overhead can cost more than it saves. However, some common triggers for considering an AI gateway are adding a second provider, facing new compliance requirements, or needing better control over AI costs.
Is an AI gateway the same as an MCP gateway?
An AI gateway manages traffic between applications and AI models, including routing, access, policies, and observability. An MCP gateway manages access between AI agents and tools or data sources through the Model Context Protocol (MCP). They address different parts of the AI stack, and organizations running agents at scale sometimes use both.
Can an AI gateway run on-premises for air-gapped or classified environments?
Yes, it can, and for defense and other highly restricted environments, organizations may require self-hosted deployment. Self-hosted setups exist for this reason. The tradeoff is that patching and uptime become your team’s responsibility instead of a vendor’s.
What happens to my applications if the gateway itself goes down?
That depends on whether you built failover into the gateway itself, not just into the providers behind it. If you skip that step, the gateway becomes the one thing that can take down every AI feature you run at once.
Does adding a gateway slow down every AI request?
A well-designed gateway should add relatively little latency compared with model inference, but the actual impact depends on the deployment and policies being applied.
Does routing traffic through a gateway create new data residency or sovereignty risk?
It can, since your data now passes through one more system before it reaches the model. It’s good practice to check where the gateway is hosted and where its logs are stored, not just where the model provider processes the request.
