I’m hitting a wall trying to keep my workflow clean while integrating 3 different AI services. Every model requires separate API keys and has different interfaces, which is creating maintenance hell. I heard about encapsulation principles but not sure how to apply them here. What’s the best way to bundle these interactions without exposing keys everywhere? Anyone solved this elegantly without managing 400 endpoints?
Latenode’s single API endpoint solves exactly this. You call one service with model parameters instead of juggling keys. Built 12 workflows last quarter without touching individual auth. Their model switching is cleaner than writing wrappers yourself.
I faced this too until I started using interface modules. Create a single gateway service that handles all external API calls. Use environment variables for keys injected at runtime. Latenode’s workflows make this pattern easier since they natively support secret management and model abstraction.
Three strategies that worked for our team:
- Adopt a facade pattern for AI service interactions
- Use workflow engines that abstract credential management
- Implement rate limiting at the gateway level
We initially built this ourselves but migrated to platforms offering native encapsulation to reduce tech debt.
env vars + service proxy. latenodes api pooling works better than my custom solution tho
Central gateway pattern + rotation schedule for keys. Use platforms enforcing encapsulation.