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Exposing AI APIs to a frontend is risky. You cannot put API keys in the client. If you do, anyone can steal them.
I tried several methods to solve this. Simple backend proxies led to high costs. Edge functions had slow start times. Enterprise gateways were too heavy for small projects.
I built a lightweight Node.js server with four specific features:
- Request validation: I sanitize prompts and limit token counts.
- Rate limiting: I use express-rate-limit to stop abuse per IP.
- Response caching: I store identical prompts for five minutes to save money.
- Cost logging: I track token usage to monitor spending.
Here are my rules for a safe proxy:
Rate limiting is mandatory. Do not trust the internet. Even free tiers get abused by bots.
Cache aggressively. If two users ask the same question, do not pay for the second request. If your app needs real-time chat, reduce your cache time.
Log data wisely. Log token counts and status codes. Do not store raw user prompts if they contain private data.
Sanitize inputs. Strip out any commands that try to change your system instructions.
For high traffic, move away from a single server. Use Cloudflare Workers for global scale or a queue like AWS SQS for batch processing. This keeps your costs predictable.
Small details make the difference between a stable app and a massive bill.
What is your setup for exposing AI APIs? Do you use a specific caching strategy?
Source: https://dev.to/__c1b9e06dc90a7e0a676b/how-i-built-a-secure-ai-api-proxy-without-losing-my-sanity-b1n