What is the difference between an AI gateway and an API gateway? An API gateway authenticates, rate-limits and routes HTTP requests without understanding what they carry. An AI gateway understands model requests: it counts tokens, prices each request, limits by tokens or dollars rather than by request count, fails over between providers that speak different APIs, handles streaming and long-running media jobs, and records which model served a request and what it cost.
The two have converged. Kong and Azure API Management now add AI plugins and policies to their API gateways12. So the useful question is not which category a product belongs to, but which AI-specific jobs your traffic needs done.
Why AI requests are different
| Property | A typical API call | An AI model call |
|---|---|---|
| Cost per call | Roughly constant, often negligible | Varies by orders of magnitude with tokens and model |
| Duration | Milliseconds | Seconds to minutes; media jobs longer |
| Response | One body | Often a stream of events |
| Upstream | Your own service | A third-party provider with its own limits and outages |
| Interchangeable upstreams | Rarely | Often: same model on several providers, or similar models |
| What to meter | Requests | Input, cached and output tokens, images, seconds, characters |
| Failure modes | Errors and timeouts | Errors, timeouts, rate limits, overloads, content refusals, spend caps |
Each row breaks an assumption API gateways were built on.
Request counts do not bound cost
A rate limit of 100 requests a minute bounds an ordinary API's load. On a model API it bounds almost nothing: one request with a 200,000-token prompt to a frontier model can cost more than ten thousand short requests to a small one. Controlling AI spend needs limits in the unit that is billed, and a price for each model.
Tokens are capacity; dollars are spend
API gateways extended for AI usually limit tokens. Azure API Management's llm-token-limit policy limits tokens per minute, a token quota per hour, day, week, month or year, or both, keyed by subscription, IP address or any expression, answering 429 for the rate and 403 for the quota3. That is the right tool for sharing a deployment's tokens-per-minute capacity between applications.
It is not a budget. A token on one model and a token on another differ in price by orders of magnitude, so a token quota means a different amount of money every time a team changes model. Microsoft's page also notes that the policy counts prompt and completion tokens only, and that concurrent requests can temporarily exceed the limit because usage is known only after responses arrive3. A dollar budget needs a price per model and a check before the call. Kong's AI Gateway takes the other route, computing per-request cost so its advanced AI rate limiting can enforce spend limits against it1.
Interchangeable upstreams with different APIs
An API gateway load-balances between identical instances of one service. AI traffic often fails over between different providers serving the same or a similar model, each with its own API shape, model ids, error codes and parameters. Doing that needs translation between dialects, per-provider model id mapping and rules about which errors to retry where. API Management now offers a unified model API in preview that exposes several backends through one OpenAI-compatible endpoint with format translation2, which is exactly this job moving into the API gateway.
Streams and jobs
Streamed responses change failure handling: once the first bytes reach the client, a failover would show the user two different answers. Video and some audio generation are asynchronous jobs that finish minutes later and deliver by webhook. An AI gateway needs rules for both; a generic proxy passes them through without understanding them.
What API gateways now do for AI
Both large API gateway vendors have documented AI capabilities:
- Azure API Management: language model APIs in OpenAI, Anthropic Messages and Vertex AI shapes, models in Microsoft Foundry or other providers such as Amazon Bedrock, token rate limits and quotas, semantic caching with Azure Managed Redis, Azure AI Content Safety checks, load balancing (round-robin, weighted, priority-based and session-aware) with circuit breakers that honour
Retry-After, token metrics with custom dimensions, and MCP servers and agent APIs2. - Kong AI Gateway: one API across major providers, failover to another provider or model, a semantic cache, model cost management with spend limits through AI Rate Limiting Advanced, guardrails with PII redaction, and MCP tooling, with data planes self-hosted, in the cloud or on Kubernetes1.
If your platform team already operates one of these, extending it to AI traffic keeps one operating model, one policy language and one on-call rotation. That is a strong reason to start there.
What to check before deciding an API gateway is enough
| Requirement | Ask |
|---|---|
| Spend control | Are limits in dollars, per model, checked before the call? Do they hold under concurrent requests? |
| Cost attribution | Is each request priced at your negotiated rate and tagged by project, team and end customer? |
| Cross-provider failover | Can a request fail over from one provider's API to another's, with model ids mapped? |
| Media | Are image, video and audio requests priced and limited in their own units? |
| Provider lifecycle | Will you know which services still call a model the provider is retiring? |
| Reconciliation | Is the estimate checked against the provider's own cost data? |
| Operations | Who writes and maintains the policies, and how quickly do new provider features get supported? |
If the answers are yes, or you do not need those rows, your API gateway is enough. If several are no, you need AI-specific logic somewhere, either a dedicated AI gateway or significant custom policy work.
A worked example: why request limits miss
Using both
The common architecture runs both, at different points:
- Your API gateway fronts your public APIs: authentication of your customers, request limits, WAF.
- Your services decide when a model call is needed.
- The AI gateway sits between your services and the model providers: provider keys, model routing and failover, token and dollar limits, cost records.
They rarely conflict. Where they meet, decide which one owns retries and timeouts so a slow provider is not retried at both layers.
Where INFRO sits
INFRO is a hosted AI gateway and control plane on your own provider accounts. It speaks OpenAI's API, Anthropic's Messages API and OpenAI's Responses API, routes across your connections with failure rules per kind of error, prices every request at list or negotiated rates in the unit the provider bills — tokens, images, seconds or characters — and checks dollar budgets at organization, project, member and key level before the provider is called. It is not a general API gateway: it does not front your own APIs, and it has no policy language. Teams that already run Kong or API Management can keep it in front of their estate and use INFRO for budgets, attribution and audit across providers.
Summary
An API gateway understands HTTP; an AI gateway understands models, tokens and cost. Modern API gateways cover a growing share of AI needs — token limits, caching, content safety, load balancing — and are the right start for teams that already run them. Dollar budgets before the call, cost attribution at your own rates and failover across provider dialects are the requirements that most often call for AI-specific logic.