An LLM gateway between your product and the model
with a budget it cannot blow through
An AI feature with no gateway in front of it has no budget, no fallback when a provider is down, and no record of what a single feature actually costs to run. We build the gateway layer that adds all three before a surprise invoice forces the conversation.
What it is
An LLM gateway sits between your application and the AI providers it calls, Claude, GPT, Gemini, and handles the things that get expensive or fragile to manage per-feature: tracking how much each feature actually costs to run, enforcing a budget so a bug or a traffic spike cannot run up an unbounded bill, caching requests that repeat, and falling back to a second provider automatically if the first one is down or rate-limited. Without a gateway, these concerns get solved, if at all, separately in every feature that calls a model, inconsistently and usually only after a problem has already happened.
When you need it (and when you do not)
You need this once you have more than one AI feature in production, or one feature whose usage scales with something you do not fully control, user messages, document volume, where “it costs more if people use it more” needs a ceiling before it becomes a problem instead of after. It is also the right build once a single provider outage, which happens to every provider occasionally, would mean your AI feature stops working entirely instead of degrading gracefully.
You do not need a dedicated gateway for a single low-volume AI feature where the cost is already small and predictable, that is solving a problem that does not exist yet. The tell that you have outgrown a direct API call is not being able to answer “what does this feature cost us per month” without checking a provider invoice after the fact.
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, so switching providers, or running two in parallel for redundancy, is a configuration change, not a code 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 normal usage patterns and deserve different limits. Caching targets genuinely repeated or near-duplicate requests, an FAQ-style question asked often, not 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, with the switch logged so you know it happened, rather than failing silently or loudly in production. Cost and token usage are logged per request and rolled up per feature, which is what let a seven-channel sales agent’s AI costs stay visible and attributable rather than arriving as one unexplained number on an invoice.
What to watch
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, model downgrade, queueing, a clear message, before launch, not during an incident. Caching has to be applied conservatively: caching a response that should reflect fresh context produces answers that are technically fast and factually wrong, which is worse than a slow correct answer. Provider pricing and capability shift fast, which is exactly why we build the gateway provider-agnostic from the start, the whole point is not being stuck with whichever provider you picked eighteen months ago when a better or cheaper option exists today.
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, and that comparison only becomes visible once you track it end to end rather than per call.
Price and timeline
| Scope | Price | Timeline |
|---|---|---|
| Single provider, budget enforcement | from $1,500 | 1 to 2 weeks |
| Multi-provider, fallback, full cost reporting | from $3,500 | 2 to 3 weeks |
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.
FAQ
How much does an LLM gateway cost?
A gateway in front of one or two AI features with budget enforcement and basic caching starts at $1,500. A fuller multi-provider setup with fallback routing and detailed per-feature cost reporting runs $2,500 to $5,000.
How long does it take?
1 to 3 weeks. Wiring the gateway into existing features is usually fast; most of the time goes to setting sensible budget limits and testing fallback behavior under a simulated provider outage.
What is the stack?
LiteLLM or a lightweight custom proxy for routing between Claude, GPT and Gemini, with Redis for caching and budget tracking. The gateway sits in your own backend, not a third-party SaaS that adds its own markup on top of provider pricing.
Does caching affect answer quality?
Only for requests that are genuinely repeated or near-identical, we cache conservatively and never cache a response to a request involving fresh user-specific context that should get a real answer each time.
What happens when a budget limit is hit?
That is a decision we make with you per feature: degrade to a cheaper model, queue the request for later, or show a clear message to the user. A hard limit with no fallback behavior defined is the mistake we build this specifically to avoid.