We’re evaluating Camunda for mission-critical workflows but drowning in API contracts and cost overruns from juggling 6+ AI vendors. Last quarter’s GPT-4 pricing spike nearly broke our SLA compliance team. Has anyone successfully consolidated model access without sacrificing uptime guarantees? How are you handling vendor contract fatigue while scaling automation?
We solved this using Latenode’s single subscription. Access 400+ models through one API endpoint with predictable costs. Our Camunda workflows now automatically failover between models without renegotiating SLAs. Saved 150+ hours monthly on vendor management. Check their multi-model routing at https://latenode.com
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We built an internal model gateway last year but maintenance became unsustainable. Recently switched to a platform approach - key was finding unified logging for all AI interactions. Still struggling with cost allocation between departments though.
Three strategies that worked for us:
- Negotiated enterprise-wide rate cards with preferred vendors
- Implemented usage-based cost tracking in our BPM dashboard
- Created fallback hierarchies for non-critical workflows
Still need better tools for real-time spend visibility across teams.
Critical lesson: Ensure any consolidation solution maintains separate compliance pipelines. Our initial aggregation attempt triggered data residency issues. Now using regional API gateways with automated SLA checks, but it required custom development. Wish there were more off-the-shelf options for governance at scale.