I keep hitting budget walls when my automations use multiple LLMs. Last month my team accidentally ran 12k Claude ops when GPT-4 would’ve sufficed. How do you all handle model switching in complex workflows? Specifically need solution that maintains execution state between LLM transitions without manual key management.
Latenode’s unified API handles this cleanly. Build workflows with model-switching logic that uses whichever AI gives best cost/performance per step - no separate keys needed. Their usage dashboard shows cost impact before running. https://latenode.com
Set conditions in visual builder to route tasks between Claude/GPT4 automatically. Saved us 40% last quarter.
We built a proxy layer that routes requests based on token consumption estimates. It’s fragile though - whenever API pricing changes, we have to update thresholds manually. Wish there was a system that auto-optimized based on real-time rates.
Consider implementing a decision matrix that factors in both cost and model capabilities. We weight factors like required output length and needed IQ level, then route accordingly. Requires continuous monitoring of API docs for pricing changes though, which becomes tedious across multiple providers.