AI Gateways Explained: Controlling AI Cost, Access and Data Leakage
Once more than one team is calling more than one AI model, nobody can say what it costs, who is using what, or what data is leaving. An AI gateway is the control point that fixes that. Here is what it does, when it earns its place, and what to check before you choose one.
An AI gateway sits between your applications and agents and the AI models and tools they call. Every request passes through it, so you can decide who uses which model, cap and attribute token spend, strip sensitive data before it leaves, and see what is happening. It does for AI traffic what an API gateway does for APIs, and increasingly it is the same product.
AI usage in most large organisations grew the way cloud did: bottom up. One team signed up to one model provider, another to a second, keys were pasted into configuration files, and the bills landed on different budgets. That is manageable for a pilot. It stops being manageable when a dozen applications and a growing number of agents are calling models and internal systems every minute, and the board asks a simple question: what are we spending on AI, and what data are we sending out?
What an AI gateway actually does
The feature lists differ by product, but the core is now consistent across the serious platforms. Taking Kong and Microsoft's documentation as two reference points, an AI gateway gives you:
- One endpoint for many models. Applications call the gateway, and the gateway routes to the right provider, balances load across deployments and fails over when a provider is slow or down.
- Token limits and quotas. Limits based on tokens rather than requests, set per application, team or user over a minute, a day or a month, so one workload cannot consume the capacity everyone else needs.
- Cost attribution. Token use and cost recorded per consumer, so AI spend can be charged back and forecast instead of discovered on an invoice.
- Semantic caching. Repeated or near identical prompts served from a cache rather than paying the model again, which cuts cost and response time on repetitive workloads.
- Data and prompt protection. Personal data redacted before it reaches an external provider, and prompts screened for injection attempts and unsafe content.
- Agent and tool control. Existing APIs exposed as tools that agents can call over the Model Context Protocol, with access to each tool controlled and logged.
- Observability. Token use, latency, errors and cost exported to your monitoring stack, so AI traffic is visible alongside everything else.
Sources: Kong AI Gateway documentation; Microsoft, AI gateway capabilities in Azure API Management.
A gateway enforces the rules you decide. It does not decide them. Which teams may use which models, what data may leave the organisation and who approves a new agent are governance questions. Put the gateway in without answering them and you have an expensive proxy. Our guide to AI governance covers the decisions that should come first.
Do you need one?
Not every organisation does yet. A single application calling a single provider can often manage with the provider's own quotas and keys. The case for a gateway gets strong when several of these are true:
- More than one team, or more than one model provider, is in production use.
- You cannot answer "what did we spend on AI last month, by team" without a spreadsheet exercise.
- Regulated or confidential data could reach an external model.
- Agents are starting to call internal systems, not just generate text.
- You have committed or provisioned model capacity that several applications need to share fairly.
- Security wants one place to log, inspect and switch off AI traffic.
Where AI gateways come from
There are three broad routes, and the right one depends mostly on what you already run.
| Approach | Where it fits | What to watch |
|---|---|---|
| API management platform with AI features | You already run, or need, an API platform. Kong, Azure API Management, Apigee and MuleSoft have all added AI controls to their gateways. | Which AI features need which tier or edition, and whether AI requests are metered like API calls. |
| Cloud provider's native gateway | Your AI estate is concentrated on one cloud and its model catalogue. | How well it governs models and agents running outside that cloud. |
| Dedicated AI gateway or open source proxy | A specialist team wants depth on model routing and evaluation quickly. | Enterprise identity, audit, support and how it sits alongside your existing API controls. |
Microsoft is explicit that its AI gateway "extends API Management's existing API gateway; it's not a separate offering". Kong takes the same view, running model traffic, MCP tools and agent to agent traffic through one runtime. For most large organisations that convergence is the useful fact: the AI gateway decision and the API management decision are increasingly one decision. Our comparison of Kong, Apigee, MuleSoft and Azure API Management covers that side.
What catches people out
- Caching across permission boundaries. A semantic cache that serves one user's answer to another can leak information the second user should never see. Scope caches carefully, and keep them away from personalised or sensitive responses.
- Prompt logs become sensitive data. Logging prompts and completions is valuable for audit and debugging, and it creates a new store of exactly the data you were trying to protect. Decide retention and access before you switch it on.
- Latency you did not measure. Every hop and every inspection policy adds time. Measure it on real traffic before committing an interactive application to the path.
- Policy lock in. Each gateway has its own policy language. The more logic you put there, the harder it is to move later, so keep business rules out of the gateway where you can.
- Two sets of numbers. Gateway token counts and provider invoices will not always match. Agree which is the record for chargeback.
How AI gateways are bought, and what to weigh
The metering differs more than the features, and the metric is what drives the bill. Some platforms charge per gateway or control plane, some per request, and some add a charge per model behind the gateway. Kong publishes its self service Konnect Plus pricing, which includes a monthly charge per model proxied through its AI gateway on that plan, while Enterprise is quoted. Kong Konnect pricing. Microsoft makes AI gateway capabilities available across Azure API Management tiers, but private networking and multi region deployment depend on the tier, which is where costs step up. Azure API Management pricing.
Before you compare quotes, check how AI requests are counted, what the cache and the logs will cost to run, which features sit only in enterprise editions, and what happens to the price when agent traffic multiplies the number of calls.
How we help
We design and deliver API and AI gateway platforms, from the question of whether you need one, through architecture, policies and integration, to rollout across teams. We spent years on the vendor side building enterprise software quotes, so we also tell you what a gateway should cost for your traffic and which terms matter in the contract. Where a new platform is needed, Kong is usually where we start, because it governs API, AI model and MCP traffic in one gateway and runs wherever your systems do. Where you already run Azure API Management or another platform that fits, we build on that instead. And if your situation does not need a gateway yet, we will say so.
Working out whether you need an AI gateway?
Tell us how many teams and models are in play, what worries you most about cost or data, and where agents are heading. We will tell you whether a gateway earns its place and what good looks like for your estate. We design and deliver it too.
Prefer email? Reach us directly at hello@c4cgroup.co.uk.
Frequently asked questions
What is the difference between an API gateway and an AI gateway?
An API gateway controls traffic to your APIs: authentication, rate limits, routing and logging. An AI gateway applies the same idea to AI models and agent tools, adding controls that only make sense for AI, such as limits based on tokens, semantic caching, prompt screening and redaction of personal data before it reaches a model provider. Several platforms, including Kong and Azure API Management, now run both on the same gateway.
Does an AI gateway reduce AI costs?
It can, in three ways: semantic caching avoids paying again for repeated prompts, token quotas stop one workload consuming shared capacity, and routing can send simpler requests to cheaper models. Its biggest effect is usually visibility, because spend that is attributed to a team gets managed. The gateway itself has a cost, so model the saving against your actual traffic.
Can an AI gateway stop sensitive data reaching a model?
It can redact personal data and screen prompts before they leave, which reduces the risk considerably. It is not a complete answer: redaction rules miss things, and data can still leave through tools and applications that bypass the gateway. Treat it as one control alongside data classification, access control and clear rules on which providers may receive which data.
Do we need an AI gateway if we only use Microsoft Copilot?
Probably not for Copilot itself. An AI gateway governs the calls your own applications and agents make to models and tools. Copilot inside Microsoft 365 is governed through Microsoft's own administrative controls. A gateway becomes relevant when you start building your own AI applications or agents.
What is an MCP gateway?
An MCP gateway sits in front of Model Context Protocol servers, the connectors that let AI agents call tools and data. It centralises authentication, controls which agent or user may call which tool, and logs every call. Many AI gateways now include this capability, so it is often the same product rather than another one to buy.