WhatA developer tool that analyzes your LLM API call patterns and automatically restructures prompts, implements context caching, and batches requests to minimize the dominant cost driver — cache and repeated context tokens.
SignalA commenter pointed out that 90% of real-world LLM cost is actually cache-related, not raw input/output pricing — suggesting most teams are dramatically overpaying because they haven't optimized their caching strategy, even as providers offer cheap cache rates.
Why NowMajor providers have all rolled out context caching in the last year (Google's context caching, Anthropic's prompt caching, OpenAI's similar features) but most teams haven't restructured their applications to take advantage of it.
MarketAny company spending $5K+/month on LLM APIs — potentially saving them 50-80% on inference costs; TAM in hundreds of millions; no dedicated tool exists for this today.
MoatDeep integration into customer codebases and prompt pipelines creates high switching costs; accumulated optimization patterns across many customers create proprietary knowledge of what cache strategies work best.
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