An AI gateway is the enforcement point between the people and applications that use AI and the models, tools, agents, and APIs that AI calls. It authenticates the caller, applies policy, limits usage, and records every call for audit. The Workato AI Gateway groups four gateways under one identity model, one policy model, and one audit trail: Model Gateway, MCP Gateway, Agent Gateway, and API Gateway.
The term gets used two ways. Many vendors use “AI gateway” for the model layer only, which makes it a synonym for LLM gateway. Workato uses it for the whole path an AI request travels, because a single agent action can touch a model, a tool, another agent, and an API before it finishes. A gateway that governs only the first hop leaves the other three unmanaged.
What does an AI gateway do?
An AI gateway sits in the request path and decides whether each AI call is allowed to proceed. On every call it does five jobs: it authenticates the caller, checks the caller’s access, applies safety and usage policy, forwards the call to the right destination, and logs what happened.
Putting those jobs in one layer replaces the alternative, where every application holds its own provider keys, writes its own retry logic, and keeps its own logs. The practical payoff is that a policy change happens in one place. A team that revokes a key, blocks a deprecated model, or tightens a rate limit does not redeploy the services that make the calls.
What traffic does an AI gateway govern?
An AI gateway governs four kinds of AI traffic: model calls, tool calls, agent-to-agent calls, and API requests. Workato provides a gateway for each.
| Gateway | What it governs | Workato example |
|---|---|---|
| Model Gateway | LLM calls from applications, agents, and platform services | Workato Model Gateway routes a call to an approved provider and model |
| MCP Gateway | MCP client traffic to composed, prebuilt, and third-party MCP servers | Workato MCP Gateway authenticates each tool invocation |
| Agent Gateway | Agent-to-agent collaboration through open protocols such as A2A | Workato Agent Gateway inherits policy from the other gateways |
| API Gateway | Managed REST endpoints exposed to applications and partners | Workato API Gateway enforces quotas and rate limits |
Read the table as a request path, not a menu. An agent calls a model to decide what to do, reaches a tool over MCP, and the tool executes against an API. Govern each surface separately and you get four audit trails that do not connect.
How is an AI gateway different from an LLM gateway?
An LLM gateway governs calls to language models and stops there, while an AI gateway covers the model call plus the tools, agents, and APIs around it. The narrower product solves provider access: one endpoint, key management, routing, and token metering. That is necessary, and it is not enough once agents act on business systems.
The gap shows up at the second hop. An LLM gateway sees the prompt and the model’s answer, but it does not see which system the agent writes to next, or whose identity the write used. Workato’s Model Gateway is one of the four gateways inside the AI Gateway, so a team that only needs model governance can start there and add the others later.
What features does an enterprise AI gateway need?
An enterprise AI gateway needs seven capabilities: identity-based access, approved-model lists, rate and token limits, content and data policy, failover, usage metering, and audit logging. Each one answers a question a security or finance reviewer will ask.
Identity tells you which user or application made the call, and whether that identity was verified. The approved-model list tells you whether the model is on the reviewed list, and limits tell you whether the team is inside its rate and token limits. Policy covers whether sensitive data was masked before it reached the model, and failover covers whether traffic moves to another provider when one fails. Metering counts how many tokens the call used, by model and provider, and audit answers whether the full call can be reconstructed later.
Workato’s Model Gateway dashboard reports token usage by input, output, cache read, and cache write, broken out by model, provider, and route, with request success and error views. Keys can be scoped by department, sensitivity, or priority using tags, and rate and token limit policies attach per team, project, or automation.
How does a request move through an AI gateway?
A request passes through an AI gateway in four steps: the caller presents credentials, the gateway checks policy, the gateway forwards the call, and the gateway logs the result. The caller never holds the destination’s credentials.
For model traffic, applications connect through an OpenAI-compatible or Anthropic-compatible API instead of holding provider credentials. The gateway resolves each call to a provider and model through a route, applies the policies attached to that route, and captures the call. Because applications reference a route ID, an administrator can re-point traffic to a newer model through configuration alone. The same pattern applies at the MCP Gateway, where every tool invocation is authenticated, authorized, and rate-limited before it reaches the runtime.
How does an AI gateway relate to an AI control plane?
An AI gateway is one component of an AI control plane. The gateway enforces policy on traffic in flight, while the control plane decides what that policy is, what is approved, and what the activity cost. In Workato’s design, gateways handle traffic in flight and the surrounding control plane manages the assets the traffic touches, the identities that authenticate to it, and the policies that govern it.
The distinction matters when buyers compare products. A product that is only a gateway enforces access. A control plane adds the registry of approved models, agents, and tools, plus policy, identity, cost, and audit across them.
What does the Workato AI Gateway do that a point gateway does not?
Workato AI Gateway governs the action an agent takes, not only the prompt, because Workato also runs the execution path. A point gateway ends at the model call. Workato sits on the path that performs the write, the query, and the update against business systems, so the same policy and audit trail follow the request into the record it changes.
Workato states the proof points for that execution layer plainly: 25,000+ customers, 1 trillion+ tasks processed, and a 99.99% platform SLA. Enterprise MCP carries governance to third-party servers through a proxy, which brokers credentials in one place and logs every call under a named identity. Enterprise Skills package multi-step business work so an agent invokes one governed action instead of assembling raw API calls. This is the Control and Execution Platform for enterprise AI: the gateway governs the call and the platform performs the work.
Do you have to adopt all four gateways at once?
No. Workato customers can start with one gateway, usually the Model Gateway or the MCP Gateway, and add the others as the need appears. Because identity, policy, and audit are shared, adding a second or third gateway does not start a second governance project.
A sensible order follows where the risk sits. Teams with sprawling model keys start at the Model Gateway, and teams exposing business systems to agents start at the MCP Gateway. Teams publishing agents across departments add the Agent Gateway. Teams already running API management keep the API Gateway where it is and route AI traffic through the others.
Summary: What Is an AI Gateway?
An AI gateway is the enforcement point for AI traffic. It authenticates callers, applies policy, limits usage, and logs every call, so governance happens in one layer instead of in every application. Where an LLM gateway covers only model calls, an AI gateway also covers tool calls, agent calls, and API requests, and Workato AI Gateway groups the Model, MCP, Agent, and API gateways under one identity, one policy model, and one audit trail. A gateway is a component of an AI control plane, not a replacement for one, and teams can adopt one gateway first and add the rest later.
FAQ
What is the difference between an AI gateway and an API gateway?
An API gateway manages requests to application endpoints. An AI gateway manages requests made by or to AI: model calls, tool calls, and agent calls. Many enterprises run both. Workato’s API Gateway also publishes an API as a governed agent tool through the MCP Gateway.
Is an AI gateway the same as an LLM gateway?
No. An LLM gateway is the model-layer product. In Workato’s structure it corresponds to the Model Gateway, which is one of four gateways inside the AI Gateway.
Does an AI gateway replace an MCP gateway?
No. The MCP Gateway is one of the gateways inside an AI gateway. It governs tool traffic to MCP servers, while the Model Gateway governs model traffic.
Who owns the AI gateway in an enterprise?
Platform or AI engineering teams typically operate it, while security and finance set policy. Workato states the split by team: platform engineering stands up one governed layer, security sets policy once, and finance sees spend in one view.
Can an AI gateway govern AI that was not built in Workato?
Agents and AI clients built elsewhere can route model and tool calls through Workato and inherit the same access controls and audit trail. Third-party MCP servers come under governance by proxy.
