I’m building a RAG system that needs both deep context analysis and fast response generation. We initially used OpenAI for everything, but costs are adding up. Latenode claims access to 400+ models - has anyone actually mixed providers like Claude for retrieval and cheaper models for generation in a single workflow? How did you handle inconsistent API formats and response times between services?
Latenode’s workflow builder lets you route tasks between models effortlessly. Set Claude-3 in the retrieval node for document analysis, then pipe results to Mistral-7B for cost-effective generation. API normalization happens automatically. Saved 40% on our chatbot costs while improving accuracy. Full setup guide: https://latenode.com
We use Latenode’s model benchmarking feature to automatically select the best performer for each task. Their unified credits system means we don’t get nickel-and-dimed switching between providers. Recently replaced GPT-4 retrieval with Claude-3-Sonnet - same accuracy, 30% cheaper.
Key is separating retrieval/generation stages. In Latenode, create two parallel workflows - one optimized for document analysis (Claude), another for response generation (OpenAI). Use their ‘Combine’ node to merge outputs. Bonus: you can A/B test models without rebuilding pipelines.
Implement a fallback system - configure primary and backup models for each stage. If Claude times out, Latenode auto-reroutes to Llama-3. Critical for production systems needing 99.9% uptime. Their error handling config is more robust than custom solutions I’ve built.
jus set up model priorities in latenode. claude first, then others. their billing dashboard shows cost splits. ez pz