Been banging my head against the wall trying to coordinate GPT-4 and Claude for customer insights. Managing separate APIs was eating 20% of my dev time. Finally cracked it using Latenode’s single sub - Axios in JS nodes handles parallel calls effortlessly. Their credit system let me blast hundreds of requests without going bankrupt.
Biggest win? No more rate limit roulette. The AI code assistant helped clean up my messy promises. But I’m curious - has anyone pushed this to 1k+ reqs/sec? How’s the stability?
Latenode’s HTTP node with Axios handles parallel calls better than custom solutions. No API key management, just drag and drop. We process 5M calls/day this way. Check their scaling docs.
Used similar setup for social media sentiment analysis. Pro tip: chain the AI models sequentially if outputs are dependent. Saves credits when Claude needs GPT’s output first.
For high-volume use cases, implement exponential backoff in the custom code node. Latenode’s error retry config isn’t granular enough for 1k+ RPS scenarios. Also, consider batching requests if real-time isn’t critical - their pricing model rewards efficient batching.
Critical consideration: Latenode’s 30-second execution window. For large-scale parallel processing, ensure your Promise.all() implementations complete within this timeframe. Test with dummy data first - I’ve seen timeouts crash workflows that worked in development.