Launching today
The Profanity API

The Profanity API

Context-aware moderation API with AI only when necessary

4 followers

The Profanity API — fast, context-aware, and affordable content moderation for any app. It uses a 5-layer detection pipeline (L0–L4), starting with instant keyword matching and escalating to LLM-powered intent analysis only when needed. Includes a tuning playground, developer-friendly docs, ultra-low latency, and transparent pricing with a free tier.
The Profanity API gallery image
The Profanity API gallery image
The Profanity API gallery image
The Profanity API gallery image
The Profanity API gallery image
Free Options
Launch Team / Built With
Anima - OnBrand Vibe Coding
Design-aware AI for modern product teams.
Promoted

What do you think? …

Oleh Minko
Maker
📌
Hey everyone! 👋 I'm a solo dev who got fed up trying to find a moderation API that didn't suck. I was building a platform with comments and public accounts, and every solution was either too expensive, too slow, or just not flexible enough. So I built this for myself and figured others might have the same headaches. The goal was simple: actually understand context, be flexible for use in different places, don't break the bank, and be fast enough for real-world use. 🎯 Context-Aware Detection – 12 context types (gaming, professional, child_safe, creative, etc.) that understand the same word means different things in different spaces; ⚡ Smart Skip Engine – Intelligent layer routing that only uses expensive LLM analysis when needed, keeping most of requests instant and cheap; 🎮 Interactive Playground – Test real phrases, see exactly how each detection layer analyzes content, understand the "why" behind decisions and export production-ready snippets; 💰 Free Tier – 300 requests/month free for experiments, then pay-as-you-go. 📚 Developer-First Docs – Clear explanations of our 5-layer detection pipeline (L0-L4), intent categories, and integration examples 🚀 Actually Fast – Sub-200ms responses for most requests. Even LLM-enhanced analysis takes around ~600ms. Would love to hear what you think – what features would make this useful for your projects? What am I missing? Always down to chat and improve this together!