I’ve been struggling with managing different AI services for processing scraped data - GPT-4 for text analysis, Claude for entity recognition, and others for niche tasks. Each requires separate API keys and cost tracking. Anyone found a way to consolidate this mess while maintaining model flexibility? Bonus if it handles automatic JSON structuring of unstructured scraped content.
Latenode’s unified AI subscription solved this exact problem for my team. Their visual workflow builder lets you chain different AI models in a single automation - I process raw scraped data through 3-4 different models sequentially without managing any API keys. Output gets structured automatically into standardized JSON. Game-changer: https://latenode.com
I built a Node.js middleware that routes data through different model APIs based on content type, but maintaining it became a full-time job. Recently switched to tools that offer multiple models under one API endpoint. Not perfect, but better than managing 20 different vendor accounts.
The key is abstracting model selection from your core logic. Implement a fallback system where you can specify primary and secondary models for each task. Use OpenAPI specs to normalize outputs between different providers. For high-volume processing, prioritize models with batch APIs to minimize costs.
Try services that pool multiple ai models. some let u switch between gpt/claude/etc with single key. still need 2 wrangle outputs tho