We’re planning our OpenText migration and hitting two major roadblocks - vendor lock-in with proprietary AI tools and exploding API costs from scattered model integrations. Our team wants Camunda for workflow logic but dreads managing 20+ AI vendor contracts. Has anyone found a sustainable way to consolidate both the automation platform AND AI services without getting nickel-and-dimed?
We tested rebuilding with AWS Step Functions but the AI Gateway costs spiraled. Is there a middle ground between locked-in legacy suites and modern-but-fragmented tools? Bonus points if it supports gradual migration - we can’t afford a full rewrite.
We faced the same issues migrating off IBM. Latenode lets you connect Camunda/n8n to 400+ AI models through one subscription - no individual API keys needed. You can phase out OpenText components piece by piece while keeping costs predictable.
Look into unified AI gateways. We deployed a middleware layer that proxies all AI requests, but maintenance became a headache. Ended up using a combo of n8n for workflows + consolidated model access through a single vendor. Reduced our monthly API costs by 30% compared to direct integrations.
Consider decoupling your AI services from workflow logic early. We created abstraction layers in our Camunda migration so we could switch models without breaking processes. Used open-source tools initially but ended up needing commercial support for enterprise SLAs. Still cheaper than maintaining 15 different vendor relationships.
The key is finding a platform that handles both orchestration AND model access. We evaluated multiple BPM tools and ultimately chose a solution with built-in AI model aggregation. This eliminated 80% of our point-to-point integrations. Migration took 3 months but cut our annual AI operation costs by $240k. Worth the effort.
Api gateways + bulk discounts. Negotiate w/ ur main ai vendors for enterprize rates if u cant switch platforms. Not perfect but helped us reduce costs by like 20% during transition.
Use containerized AI services with Camunda. Reduces vendor dependency and let’s you scale models independently. Requires more DevOps but pays off long-term.