The Problem
AI teams running high-volume LLM fleets are hemorrhaging money on redundant context, system prompts re-sent on every API call, and cache misses that break savings. A trading firm or code agent shop easily burns $50K-$100K/week on tokens that could be compressed or routed to cheaper models. Existing solutions are fragmented point tools that don't talk to each other.
What We're Building
TokenSaver is a lightweight proxy that sits between your application and LLM APIs (OpenAI, Anthropic, Gemini). It automatically deduplicates identical context across requests, compresses long prompts using semantic analysis, and routes small tasks to cheaper models (Haiku for linting, Sonnet for generation). You swap one API endpoint, we handle the rest—no code changes needed.
Who It's For
Engineering leads and DevOps at mid-to-large trading firms, autonomous agent startups, and AI code generation platforms spending $50K+/month on LLM tokens.
Key Features (Planned)
- Semantic context deduplication—detect and cache identical prompts and tool traces across requests
- Intelligent model routing—automatically send small tasks to Haiku, medium to Sonnet, complex to Claude 200K
- Token-level compression—optional LLMLingua-style compression for long contexts without cache hits
- Zero-code integration—swap one API endpoint, works with OpenAI, Anthropic, Gemini
- Real-time cost tracking—per-request savings dashboard, total ROI metrics
We're validating this idea before writing a single line of code. If this resonates with you, we'd love your feedback:
If you could cut your LLM bill by 30% without touching your code, what's the first thing you'd do with the savings?
Check out the concept page and let us know what you think.
Built by Jenavus — AI-powered business intelligence
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