How to Stop Throttling AI: Build a Cost-Optimization Proxy That Saves 60% on API Costs

Hook
Imagine your engineering team finally embracing AI coding assistants—then the cloud bill arrives and your CFO demands you throttle usage. This is the reality for countless companies today. But what if you could give employees unlimited AI access without budget blowouts?

The Problem
According to a recent Hacker News discussion (source: Companies Are Throttling Employees' AI Use Because It's Too Expensive), companies are manually throttling AI usage, setting quotas, or blocking AI tools entirely because API costs are too high. This kills productivity and innovation. The alternative—switching to cheaper models—often sacrifices quality.

The Solution: A Cost-Optimization Proxy
The MVP is a lightweight proxy that sits between your team and LLM APIs. It does three things:

1. Caches responses for identical or similar queries (e.g., repeated code explanations).
2. Batches requests from multiple users into a single API call where possible.
3. Routes intelligently to the cheapest suitable model in real-time (e.g., GPT-3.5 for simple tasks, Claude for complex ones).

This proxy can reduce API costs by up to 60% without limiting access. For example, a team making 10,000 calls/day to GPT-4 could save $200/day by caching common prompts and routing simple requests to cheaper models.

How to Build It

• Use a reverse proxy (e.g., NGINX or Envoy) with a custom plugin.
• Implement a caching layer (Redis or Memcached) with TTL-based expiration.
• Integrate with LLM APIs via a unified interface (e.g., LangChain or custom wrapper).
• Add a simple dashboard for usage analytics and cost tracking.

Pricing & Go-to-Market
Charge $0.01 per API call saved, or $200/month per team for unlimited caching and routing. Target engineering managers on LinkedIn and launch on Hacker News as a Show HN.

CTA
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Originally published on Pain Radar. Discover startup opportunities daily.