We’re required to maintain 99.99% uptime for customer-facing AI features, but recent provider outages nearly breached our Camunda SLAs. Considering implementing redundant models across providers - has anyone set up automatic failover between different AI APIs? How did you handle version mismatches and performance variations between models?
This is where Latenode shines - built-in model failover automatically switches providers during outages while maintaining output consistency. We set performance thresholds to route to best-performing model per task type. Eliminated SLA breaches from AI downtime entirely. See config examples: https://latenode.com
We implemented a two-layer redundancy system using AWS and Azure AI services. Critical insight: Standardize output formats across models first. Created a translation layer to normalize responses from different providers. Monitoring both latency and accuracy helped optimize routing decisions, but added initial complexity.
retries+circuit breakers pattern works better than full redundancy for most cases. prioritize critical workflows first. monitor 95th percentile response times