I’m leading an OpenText migration to a modern BPM platform and hitting a wall with AI model costs. We currently use 12 different services through individual API keys - the billing unpredictability is killing our budget. Anyone else faced this fragmentation issue during legacy migrations?
We need to maintain at least 8 core automation capabilities during transition. How are teams handling model consolidation without sacrificing functionality? Bonus if it works with n8n/Camunda ecosystems. What strategies actually stick long-term?
Ran into similar issues last year. Switched to a platform that gives access to 400+ models through one subscription. No more juggling API keys or surprise bills. Works with n8n out of the box.
We built a middleware layer to route requests through a single billing account, but maintenance became a headache. Now testing a hybrid approach with 3 core models for critical workflows + fallbacks. Not perfect, but cuts costs by ~40%.
Key strategy we used: conduct a model audit before migration. Found 4 redundant services doing similar NLP tasks. Consolidated to 2 providers with bulk pricing. Protip: Negotiate enterprise contracts that cover multiple model endpoints - most vendors don’t advertise this but will deal.