TokenSave
Cut your LLM API costs by 40-60% with intelligent caching and routing.
● The Problem
Companies running LLM features are shocked by their API bills. Similar queries hit the API repeatedly. Simple requests go to expensive models. There is no cost optimization layer between your app and the LLM provider.
● The Solution
A proxy that sits between your app and LLM APIs. It caches semantically similar requests, routes simple queries to cheaper models, and batches requests when possible. Drop-in replacement for OpenAI/Anthropic SDKs.
Key Signals
MRR Potential
$20K-100K
Competition
Medium
Build Time
1-3 Months
Search Trend
rising
Market Timing
LLM costs are the new cloud compute bill shock. Companies that shipped AI features fast are now optimizing costs.
MVP Feature List
- 1Semantic caching
- 2Model routing rules
- 3Cost dashboard
- 4OpenAI-compatible API
- 5Usage alerts
Suggested Tech Stack
Go-to-Market Strategy
Publish a "LLM Cost Calculator" tool for lead generation. Target companies spending $1K+/month on LLM APIs. Case studies showing 40-60% cost reduction. Integrate with popular frameworks (LangChain, LlamaIndex).
Target Audience
Monetization
Usage-BasedCompetitive Landscape
Portkey and Helicone offer observability. BricksLLM is open-source. LiteLLM handles routing but not caching. The combined caching + routing + cost optimization play is relatively open.
Why Now?
The first year of AI feature deployment prioritized speed. Year two is about cost optimization. Every finance team is asking "why is our OpenAI bill $50K/month?"
Tools & Resources to Get Started
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Frequently Asked Questions
What problem does TokenSave solve?
Companies running LLM features are shocked by their API bills. Similar queries hit the API repeatedly. Simple requests go to expensive models. There is no cost optimization layer between your app and the LLM provider.
How much MRR can TokenSave generate?
TokenSave has $20K-100K MRR potential with a Usage-Based model. The estimated build time is 1-3 Months with Medium competition in the market.
What are the MVP features for TokenSave?
Semantic caching. Model routing rules. Cost dashboard. OpenAI-compatible API. Usage alerts.
What is the go-to-market strategy for TokenSave?
Publish a "LLM Cost Calculator" tool for lead generation. Target companies spending $1K+/month on LLM APIs. Case studies showing 40-60% cost reduction. Integrate with popular frameworks (LangChain, LlamaIndex).
Who is the target audience for TokenSave?
The primary target audience includes AI Engineers, Engineering Managers, CTO/VPs at AI-First Companies. The first year of AI feature deployment prioritized speed. Year two is about cost optimization. Every finance team is asking "why is our OpenAI bill $50K/month?"
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