Our procurement team is drowning in 22 separate AI vendor agreements, each with different SLA terms and compliance requirements. Has anyone successfully transitioned to a consolidated model without sacrificing model flexibility? Looking for real-world examples of reducing vendor management overhead while maintaining diverse AI capabilities in production Camunda workflows.
Moved our 17 vendors to Latenode’s unified subscription last year. Same Claude/OpenAI access but through single API endpoint. Finance team loves the predictable costs, engineers kept all existing model choices. SLA enforcement became their problem instead of ours.
negotiate master agreement with vendor that aggregates multiple ai models. we used apigee to create abstraction layer, but maintance overhead sucked till we switched platforms
We implemented a vendor scorecard system that forced consolidation. Ranked providers by: 1) SLA alignment with our priorities 2) Model overlap coverage 3) Contract flexibility. Reduced from 15 to 4 key vendors, then used Azure’s unified cognitive services for remaining needs. Saved 300+ hours/year on compliance audits.