An LLM gateway between your product and the model
with a budget it cannot blow through

What a gateway actually catches

An LLM gateway sits between your application and the AI providers it calls, Claude, GPT, Gemini. It handles the things that get expensive or fragile to manage per feature. It tracks what each feature actually costs to run. It enforces a budget, so a bug or a traffic spike cannot run up an unbounded bill. It caches requests that repeat, and switches to a second provider automatically if the first is down or rate-limited. Without a gateway, these get solved separately in every feature that calls a model, inconsistently, and usually only after a problem has already happened.

When the direct API call stops being enough

You need this once you have more than one AI feature in production. The same is true for one feature whose usage scales with something you do not fully control, like user messages or document volume. “It costs more if people use it more” needs a ceiling before it becomes a problem, not after. It is also the right build once a provider outage, which happens to every provider occasionally, would stop your AI feature entirely instead of letting it degrade gracefully.

You do not need a dedicated gateway for a single low-volume AI feature where the cost is already small and predictable. That solves a problem that does not exist yet. Here is the tell. If you cannot answer “what does this feature cost us per month” without checking a provider invoice after the fact, you have outgrown a direct API call.

How we build it

We use LiteLLM or a lightweight custom proxy to route requests to Claude, GPT or Gemini behind a single internal interface. Switching providers, or running two in parallel for redundancy, becomes a configuration change, not a rewrite across every feature that calls a model. Budgets are set per feature, not globally, because a support chatbot and a content generation pipeline have very different usage patterns and deserve different limits.

Caching targets requests that are genuinely repeated or near-duplicate, an FAQ-style question asked often. It never touches fresh, user-specific context that should get a real, current answer every time. Fallback routing switches to a second provider automatically on an outage or a rate limit. It logs the switch, so you know it happened instead of finding out from a user complaint.

Cost and token usage get logged per request and rolled up per feature. That is what kept a seven-channel sales agent’s AI costs visible and attributable, instead of arriving as one unexplained number on an invoice.

Where gateways go wrong

The real risk of an LLM gateway done badly is a budget limit with no defined fallback behavior. A feature that simply stops answering once a cap is hit is often worse for the user than no budget at all. We define what happens at the limit before launch, not during an incident: a model downgrade, queueing, or a clear message to the user.

Caching has to stay conservative. Caching a response that should reflect fresh context produces answers that are fast and factually wrong, worse than a slow correct one. Provider pricing and capability shift fast, which is exactly why we build the gateway provider-agnostic from the start. The point is not getting stuck with whichever provider you picked eighteen months ago.

We also track cost per successful outcome, not just cost per request. A cheap model that needs three retries to get a usable answer can end up costing more than a pricier model that gets it right the first time. That comparison only shows up once you track it end to end, not per call.

Price and timeline

Related

Built as part of AI agents and custom development. Pairs with model evaluation and test harness and sits underneath an AI agent runtime with tools and approvals. See it managing real cost at scale in a seven-channel AI sales agent and an 11-type content agent across two brands. Tell us which AI features need a real budget: get in touch.