Build an LLM Cost Routing and Caching Gateway
People search: “reduce llm api costs model routing tool” (1K+ per month)
A developer tool that sits between an app and the AI providers, routes each request to the cheapest model that can handle it, serves repeated prompts from cache, and shows teams a cost dashboard they can finally act on.
People look up reduce llm api costs model routing tool every single day, and most of what comes back is hype. Here is the honest breakdown instead: what this really is, what it costs, and how to begin.
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Difficulty
Advanced
Startup cost
$500 to $5,000
Time to first $
30 to 90 days
Revenue potential
High
Profit margin
75%-90%
Viability ⓘ
6.9 / 10
Search demand
Medium (1K+ per month on Google)
Where it runs
Online
Best for: A backend engineer who is comfortable with API infrastructure and wants a product developers adopt in an afternoon
The ideaWhat this actually is
A developer tool that sits between an application and the AI providers as an API-compatible proxy. It classifies each request, routes the easy ones to a cheaper model that can still handle them, serves repeated prompts from cache, and gives the team a spend dashboard a finance lead can actually read. Switching to it is a base-URL change, not a migration, which is the whole point.
The opportunityWhy this idea works
Every AI product team watches its model bill climb, but cost work never makes the sprint because it is nobody's feature. Meanwhile a large share of production traffic is repetitive prompts and simple tasks running on premium models. A gateway that quietly downgrades the easy calls and caches the duplicates can pay for itself the first month, which makes it one of the easiest B2B sales in the AI stack: the tool costs less than it provably saves. Pricing against measured savings keeps the pitch arithmetic rather than a leap of faith.
The openingWhy this idea is overlooked
Open-source gateways and observability platforms already exist, so builders assume the space is taken, but being a slightly different proxy is not a business. The gap is an opinionated tool that provably cuts the bill and proves it on real traffic. Cost optimization is unglamorous and cross-cutting, so it stays perpetually unstaffed inside the teams that need it most, which is exactly why an outside product can win.
The buildWhat you need to build this
| You need | Why it matters |
|---|---|
| An API-compatible proxy | Adoption depends on switching being a base-URL change, so match the request format developers already use. |
| A request classifier | Routing to the cheapest capable model requires judging length, task type, and accuracy needed on every call. |
| A caching layer with correctness controls | Exact-match caching is free money, but semantic caching needs confidence thresholds and per-route opt-in so you never serve a stale answer. |
| A readable cost dashboard | Half the product is showing a finance lead where the money goes and what the tool saved, separated by routing and cache hits. |
| A design partner with real spend | One credible before-and-after on a team spending real money monthly is worth more than any feature list. |
| Infrastructure-grade reliability | You sit in the request path, so uptime, a bypass mode, and single-digit-millisecond overhead are non-negotiable trust requirements. |
Reduce LLM API costs model routing tool: the honest path
Consider the steps below our honest answer to reduce llm api costs model routing tool: what actually works, in the order it works.
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Questions
What people ask about this idea
Aren't there already open-source gateways?
Yes, which is why you need a wedge: opinionated automatic routing with a savings guarantee, a one-line drop-in, or the clearest dashboard. Being a slightly different proxy is not a business; being the one that provably cuts the bill is.
How do you avoid serving wrong cached answers?
Exact-match caching for deterministic calls, and semantic caching only with confidence thresholds and per-route opt-in, so freshness-sensitive endpoints never cache.
How should I price it?
Against the savings, not per seat. A percentage of measured savings or volume tiers keeps the math obvious: the tool costs less than it returns.
Won't sitting in the request path add latency?
It can, so keeping overhead in single-digit milliseconds, offering a bypass mode, and publishing uptime are core requirements, not afterthoughts.
Who is the first customer?
A team with a real monthly model bill and repetitive traffic. Shadow their requests for two weeks and publish the before-and-after with permission as your launch proof.