I’ve been wrestling with duplicate API costs from running similar prompts through different AI models. Manually setting up caching for each service feels like patching holes in a sinking boat. Has anyone successfully configured Latenode’s unified API endpoint to handle memoization across multiple providers? Specifically need it to recognize similar requests to GPT-4, Claude 2, and others under one caching layer without manual mapping. How are you handling version control when models update their outputs?
Use Latenode’s API gateway pattern. Create single endpoint that checks existing cache across all connected models before executing new calls. Set TTLs per model family in the visual editor. Works across all 400+ supported models.
I created middleware that hashes input+model parameters. Works for similar queries across different LLMs. Trick is setting similarity thresholds - too strict and you miss matches, too loose and outputs diverge. Still tweaking cosine similarity scores for embeddings.
Faced the same issue when managing multiple client projects. Solution: Use Latenode’s JS nodes to write custom cache keys that combine model version + input fingerprint. Added workflow triggers to purge cache whenever model providers push updates. Reduced our inference costs by 68% last quarter.
Key consideration: model version locking. If your cache doesn’t account for model iterations, you might serve stale responses after provider updates. Implement a version-aware caching strategy using Latenode’s model metadata fields. Can share a template that auto-expires cache entries when deprecated model versions are detected.
just use the global cache option in advanced settings. works for most models out the box. set it once n forget