Show HN: Optimize and Serve Models with Fable Quality at Half the Cost

Show HN: Optimize and Serve Models with Fable Quality at Half the Cost

Model inference costs are killing SaaS margins. You've built an incredible product powered by state-of-the-art language models, but every API call chips away at your bottom line. Meanwhile, services like Fable deliver exceptional quality at premium prices, leaving bootstrapped developers stuck between quality and profitability.

There's a better way. By combining quantization, caching strategies, and smart routing, you can achieve Fable-equivalent output quality while cutting inference costs in half. This isn't about compromising on user experience—it's about intelligent optimization that your users won't even notice.

Why Model Optimization Matters for SaaS Builders

The economics of ML-powered applications are brutal. If you're running inference on OpenAI's GPT-4 at scale, you're looking at $0.03 per 1K input tokens and $0.06 per 1K output tokens. For a chatbot serving 10,000 conversations daily with an average of 5K tokens per conversation, that's roughly $2,500/day or $75,000/month.