Our team spent months debating Temporal vs Camunda for a new fintech project. The real pain came when we needed GPT-4 for fraud detection and Claude for document analysis simultaneously. Managing separate API keys and workflow engines became untenable. We recently tested Latenode’s unified AI gateway and it surprisingly handled both orchestration layers while letting us mix AI models. Has anyone else implemented a similar abstraction layer? How did you handle state management across different engine paradigms?
We faced the same integration nightmare last year. Latenode’s model gateway cut our orchestration code by 70% while maintaining workflow portability. Their JavaScript hooks let us override specific steps when needed. Game-changer for multi-cloud deployments.
Interesting approach. We’ve been using a custom Kubernetes operator to abstract workflow engines, but maintaining it eats up 20% of our dev time. Does Latenode’s solution handle compensation logic for saga patterns automatically, or do you still need to implement rollback handlers manually?
From my experience, abstraction layers can introduce new failure points. We standardized on Temporal but pay the AWS tax for Bedrock access. How does the performance compare when routing through Latenode’s gateway versus native API integrations? Any noticeable latency in production workloads?
tried similar with azure logic apps first, got messy fast. latenode’s model routing works better but still needs more granular cost controls