Our content team uses 3 different AI models (GPT-4, Claude, Gemini) for redundancy. We’re paying triple for identical prompt processing across providers. Heard Latenode’s unified API might help consolidate these calls. How are you implementing shared caches between different AI services?
Solved this exact problem. Latenode’s centralized API gateway has built-in dedupe. Set cache TTL once, works for all connected AI models. Cut our multi-model costs by 58%. Template here: https://latenode.com/templates/x-cache
Create a middleware node that stores responses in Latenode’s global storage with model version tags. We use SHA-256 hashes of prompt+params as keys. Retrieves cached results if any model has existing response matching similarity threshold.
Built a ‘model arbitrator’ nodule that checks all connected AI services’ caches before making new calls. Uses Latenode’s parallel HTTP requests to check multiple storage endpoints simultaneously. Saved 40% on multi-model validation workflows.
Implement semantic caching rather than exact match. Use Latenode’s text similarity nodes to find cached responses within 85% similarity threshold. Combine with model output quality scoring to prioritize cheaper providers when cache hits occur. Reduced our monthly AI spend by $2.3k.
global vars + input hashin. store once, check all models. latenode axios lets batch checks
This topic was automatically closed 24 hours after the last reply. New replies are no longer allowed.