Build Your Own ChatGPT Wrapper Safely — Architecture, Auth, Rate Limit, Logging

Manish Garg
Manish Garg Associate of (ISC)² · RingSafe
Apr 29, 2026
10 min read
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Half the SaaS launches in 2024-2025 were “ChatGPT for X.” Most shipped with embarrassing security gaps: hardcoded API keys, no rate limiting, no abuse logging, prompt injection that leaks system prompts. This module is the production architecture for a chat wrapper that does not embarrass you when it gets popular.

A “ChatGPT wrapper” sounds trivial: take user input, send to OpenAI/Anthropic, return response. The minimal version is 30 lines of Python. The production version handles auth, billing, abuse, prompt injection, content moderation, observability, kill-switches, and PII redaction. This module gives you the reference architecture that costs 200 lines of code and saves you from the most common public failures.

The minimum viable architecture

Three components. Frontend: chat UI (Vercel AI SDK works well). Sends user message to your backend. Never calls OpenAI directly — that would expose the API key. Backend: REST endpoint that validates user auth, applies rate limit, does pre-flight content moderation, calls OpenAI/Anthropic with constructed prompt, post-flight moderates response, streams back. Logging + observability: every request logged with user_id, prompt hash, token counts, model version, latency, cost. Stack: Next.js app router + Vercel AI SDK + Supabase auth + Redis (Upstash) for rate limit + Sentry for errors + LangSmith or Helicone for AI observability.

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