why we build this
Making AI development
sustainable from day one
FGY exists because building with AI should not require a large budget to get started, and it should not waste resources once you do.
The problem we saw
AI is the most powerful technology available to individual builders today. But the economics of inference create a barrier that hits hardest where it matters most — small teams, solo developers, early-stage products iterating fast on limited budgets.
Every time the same prompt is sent to an LLM provider, the same tokens are processed, the same compute is consumed, and the same cost is incurred. For applications with any degree of repetition — chatbots handling similar questions, pipelines running recurring jobs, development environments iterating on the same prompts — this is pure waste.
We know this because we lived it. Building AI-powered products on small budgets means watching credits drain on requests you've already paid for. It means choosing between iterating enough to get the product right and keeping the lights on. That tension is unnecessary.
What FGY does about it
FGY is an inference caching layer. It sits between your application and your LLM provider, intercepts traffic, and serves cached responses when possible. Exact matches are served from ETS in microseconds. Semantically similar prompts are matched via pgvector cosine similarity in under 10ms. Concurrent identical requests are coalesced into a single upstream call.
The result: you pay your provider once for a given response, and every subsequent cache hit costs a fraction of what the provider would have charged. Misses pass through free — you only pay FGY when we save you money.
This is not a complex optimization that requires rethinking your architecture. It is a drop-in proxy. Change your base URL, keep your provider key, and the caching layer works transparently. Streaming, multi-provider routing, and all the plumbing is handled for you.
The sustainability angle
There is also an environmental question that we think deserves more honest consideration. LLM inference is computationally expensive. Every redundant request consumes GPU cycles, energy, and cooling capacity that could serve a new, unique request instead.
We are not claiming that caching solves the energy footprint of AI. But we do believe that needlessly re-computing answers we already have is wasteful in every sense of the word — financially, computationally, and environmentally. The answer to "should we avoid redundant inference?" requires no complicated analysis. If the same output exists, serve it from cache.
The stack to answer that question well — exact matching, semantic similarity, request coalescing, distributed cache coherence — is not trivial to build and operate. That is the service. We put in the engineering so you can focus on building.
Principles
01
Aligned incentives
We only charge when we save you money. If the cache does not help, it costs you nothing. Our revenue is directly tied to the value we create for you.
02
Builder-first
FGY is built for developers shipping real products. Drop-in integration, transparent pricing, no platform lock-in. Your provider key stays yours, your data stays in your tenant.
03
No waste
If an answer already exists, serve it. Every redundant inference call is wasted money and wasted compute. Caching is the simplest, most effective optimization available for repetitive LLM workloads.
04
Transparent operation
Every charge in an auditable ledger. Every cache hit and miss visible in your dashboard. No hidden fees, no opaque pricing tiers, no surprise invoices. You see exactly what you pay for and what you save.
Start saving on inference
Change your base URL, keep your provider key, and let the cache work for you. Misses are free. You pay only from realized savings.