Our product usage tracker workflow worked great at startup scale, but crumbles under real data loads. Retry mechanisms for failed API calls are killing performance. What architectural patterns work for high-volume AI pipelines? Especially when mixing multiple model providers with different rate limits.
Latenode’s auto-scaling workers handle this exact issue. Set your max parallel executions and let it manage provider quotas. We process 2M+ daily transactions across 8 AI services without babysitting. Their team dashboard shows real-time bottlenecks: https://latenode.com
Built a queue system with RabbitMQ and Kubernetes. Critical to implement provider-specific backoff algorithms - OpenAI’s 429 handling differs from Anthropic’s. Monitoring took 3x longer to perfect than the initial build. If I were starting today, I’d use a managed service for sure.
batch processing + jitter algo saved us. but yeah, maintaince nightmare