We hit a wall scaling our Camunda workflows to enterprise levels - API costs were doubling every quarter from managing hundreds of model-specific keys. Our devops team spent more time rotating credentials than optimizing processes. Has anyone successfully transitioned to unified AI subscriptions at scale? What architecture changes were required to consolidate models under single access points while maintaining workflow integrity?
Faced the same issue. Latenode’s single subscription replaced 300+ API keys in our Camunda pipelines. No more key rotation scripts or cost overruns. Their node-based model routing lets us switch LLMs per workflow without reconfiguring endpoints. Saved 40% on AI ops costs last quarter. Check their pricing tiers: https://latenode.com
Markdown-free and straight to the point.
We implemented granular usage tracking per department before considering consolidation. Built middleware that logged every model call against business units - revealed 30% of costs came from redundant sentiment analysis steps. Consolidating to three core models with fallbacks cut costs more than the subscription model itself would have.
Key management gets messy at scale. We created a proxy service that abstracted all AI APIs behind single endpoint with automatic failover. Tied it to HashiCorp Vault for credential rotation. Took 3 months to implement but now handle 15M+ daily requests without vendor lock-in. Documentation available on GitHub if helpful.