Concept explainer
What is an AI Gateway?
A single control point that sits between your applications and every AI model they use — routing traffic, enforcing security, tracking cost and keeping production AI reliable at scale.
A control tower for your AI traffic
Modern applications rarely rely on a single AI model. A team might use one provider for chat, another for embeddings, an open-source model for summarisation and a self-hosted model for anything sensitive. Each has its own SDK, keys, pricing and quirks.
An AI gateway is a middleware layer that hides all of that behind one interface. Every call from your apps and agents goes through the gateway, which decides which model should answer, checks that the request is safe, enforces limits, records what happened and returns the result.
Think of it as the equivalent of an API gateway, but purpose-built for the messy, non-deterministic world of large language models — where the "payload" is a natural-language prompt, the "cost" is measured in tokens, and the response can stream back over seconds rather than milliseconds.
Why enterprises need one now
Most organisations start with AI as a set of small experiments: a developer drops an API key into a side project, a team builds a chatbot, another spins up an agent. It works — until it doesn't.
Without a central control point, three problems appear at the same time:
- Shadow AI. Nobody knows which teams are calling which models, with which keys, on which data.
- Unpredictable cost. Token bills arrive at the end of the month with no way to attribute them to a feature or team.
- Real security risk. Sensitive data leaks into public models, prompts can be hijacked, and there is no audit trail when something goes wrong.
A gateway turns this chaos into infrastructure — one place to see everything, enforce policy, and evolve safely as new models appear every week.
How is it different from an API gateway?
A traditional API gateway cares about endpoints: is the request authenticated, is the server healthy, is the response a valid JSON schema. An AI gateway cares about the same things, plus the meaning and cost of what is flowing through.
| Dimension | API gateway | AI gateway |
|---|---|---|
| Primary concern | Endpoint uptime & auth | Model behaviour & governance |
| Rate limiting | Requests per second | Tokens per minute & per user |
| Routing | Load balancing servers | Choosing between LLMs by cost, latency or capability |
| Caching | Exact-match response cache | Semantic cache of similar prompts |
| Inspection | Headers & schemas | PII, prompt injection, output guardrails |
| Protocols | REST, gRPC | REST + streaming (SSE / WebSockets), MCP, agent protocols |
Core capabilities
The features that separate a real AI gateway from a thin proxy.
Smart model routing
Send each request to the right model — cheap and fast for simple tasks, high-end reasoning for complex ones — and fall over to a backup provider when one is slow or down.
Security & guardrails
Strip PII before prompts leave your network, block prompt-injection attempts, vault provider keys, and enforce output policies so models can't leak sensitive data.
Rate limits & quotas
Throttle by tokens-per-minute (not just requests) per user, team, or app so one runaway agent can't drain the shared budget.
Cost tracking & caching
Attribute every token to a team or feature, and use semantic caching to reuse answers to similar questions instead of paying for the same generation twice.
Observability & audit
Log every prompt, response, latency and cost in one place — the paper trail you need for debugging, compliance and continuous improvement.
Unified API & tools
One interface for OpenAI, Anthropic, Google, open-source and self-hosted models, plus a governed entry point for agent tools and MCP servers.
What the business gets in return
Predictable AI spend
Token usage is attributed to teams and features, so budgets stop being surprises and start being decisions.
No vendor lock-in
Swap or blend providers without touching product code — negotiate from a position of strength and adopt new models on day one.
Faster, safer delivery
Developers ship against a single, stable interface. Security, compliance and observability are built in from the first call.
A foundation for agents
As you move from chatbots to multi-agent systems, the gateway is the shared plane that keeps thousands of model-to-model calls governed and observable.
Where to start
You don't need to build the perfect system on day one. The most successful rollouts follow a simple sequence:
- Get visibility first. Move all provider keys into the gateway's vault and audit who is calling what today.
- Turn on the basics. Rate limits, per-team cost tracking and exact-match caching pay for themselves within weeks.
- Add intelligence. Introduce semantic caching, smart routing and fallbacks so you can safely try new models without risk.
- Extend to agents. Use the same gateway to govern tools and MCP servers, so the leap from chatbots to agents doesn't mean starting over on governance.
Thinking about an AI gateway for your organisation?
We help enterprises design and roll out the governance layer their AI programmes need — from first audit to production.