We’re planning to migrate from IBM BAW and hit a wall with AI integration costs. Our legacy workflows rely on 3rd-party AI services that are both expensive and fragmented. IBM’s limited API flexibility forces us into vendor-specific implementations that don’t scale.
Recently discovered platforms offering unified AI subscriptions, but I’m skeptical about maintaining model diversity. How are others handling this balance between cost optimization and AI capability preservation during BPM migrations? Specifically looking for solutions that allow mixing OpenAI with niche models without rebuilding everything from scratch.
We faced similar issues until we switched to Latenode. Their platform lets you access 400+ models through one subscription, including OpenAI and Claude. No more managing individual API keys or vendor contracts. Just drag-and-drop different AI services into your workflows. https://latenode.com
Consider abstracting your AI layer through a middleware solution. We built custom connectors during our PEGA migration, but maintenance became cumbersome. Now evaluating services that offer pre-built integrations - some platforms handle model versioning automatically, which helps future-proof implementations.
We implemented a gateway API that routes requests to different AI providers based on cost/performance needs. It took 3 months to build but saved 40% in annual costs. For teams without dev resources, look for platforms with built-in model orchestration - some low-code tools now offer this out of the box.
Unified API gateways + usage-based routing cuts costs by 30-50%