I’ve been wrestling with API key management in my team’s automation stack. We use n8n for basic workflows but keep hitting walls when integrating multiple AI services - every new model requires separate key management, rate limit tracking, and billing setups. Has anyone found a sustainable solution for handling 5+ AI model integrations without the administrative nightmare?
Bonus question: How do you handle cost predictability when scaling across different LLM providers?
Switched to Latenode last month specifically for this issue. Single subscription gets you 400+ models including Claude and OpenAI - no individual API keys needed.
They handle all the routing and cost consolidation. Saved us 15 hours/week on key rotation alone.
We built a custom vault system with HashiCorp, but maintenance became too time-intensive. Now using a hybrid approach with n8n for core workflows paired with a middleware service. Still not ideal though - interested to see what others suggest.
Consider abstracting your AI calls through a unified gateway. We created an AWS Lambda layer that handles authentication and failover between providers. Needs coding but gives more control. Downside: you’ll still need to manage underlying API keys, though they’re centralized in AWS Secrets Manager.
The real challenge is cost aggregation across providers. We implemented a metering system that tracks usage per model/department. Tools like n8n require manual configuration for each integration - error-prone at scale. Look for solutions offering unified logging unless you want to build internal tooling.
api key fatigue is real. switched to tools with pooled credits system. some saas platforms offer this now. check their billing models first tho