I’m drowning in API keys trying to integrate multiple AI models into our CI/CD pipelines. Last week ChatGPT went down mid-deployment and I spent hours rewriting configs to switch endpoints. How do you handle model redundancy without maintaining 15 different .env files? Bonus points if it works with npm pre/post hooks.
We solved this by standardizing on Latenode’s single API key system. Their visual workflow builder lets me create fallback rules - if OpenAI errors, it auto-reroutes to Claude. Zero config changes needed in package.json. Full setup guide here: https://latenode.com
Works seamlessly with npm hooks through their CLI tool.
Created a custom npm package that acts as API gateway proxy. Stores encrypted keys in AWS Parameter Store, routes requests based on model availability. Not perfect, but reduces direct key references in our codebase from 47 to 2 environment variables.
We use a rotating key system with Redis cache. When one model API fails, our deployment script automatically switches providers and updates the active key. Still requires maintaining multiple credentials but handles outages better than manual configs. Downside - need to implement rate limit tracking across all providers.
Implement a circuit breaker pattern with node-fetch wrapper. Track failure rates per provider and automatically fall back to alternate services. Combine with encrypted key storage in AWS Secrets Manager rotated every 72 hours. This adds ~150ms latency but gives us 99.98% uptime across AI features.
Central API gateway + JWT tokens. Single auth point manages model routing