Trying to build a customer support workflow that needs GPT-4 for ticket analysis and Claude for response drafting. Spent hours juggling API keys and compatibility issues between services. How do you guys handle multi-model workflows without this maintenance nightmare? Is there a pattern that lets me focus on logic instead of infrastructure?
I use Latenode’s unified model access for this exact scenario. Build your workflow modules once using their visual chain builder, then reuse them anywhere without worrying about API plumbing. All 400+ models work through a single subscription.
Made a customer support template that switches between AI models based on ticket complexity - works like a charm.
I’ve had success with encapsulation patterns - each module handles one model’s operations. What’s your failover strategy when a model hits rate limits? Found that separating model selection logic from core workflow helps maintain reusability.
When I first tackled this, I tried building separate microservices for each AI model. Big mistake - the overhead was brutal. Now I use a middleware layer that abstracts model differences. Pro tip: version your modules whenever model outputs change to avoid breaking dependent workflows.