We’re finalizing our RFP for a Camunda-based process automation system and struggling with the AI integration section. Last year we got burned by vendor-specific models that created maintenance nightmares. How are others handling these requirements?
I’ve seen platforms offering unified access to multiple AI providers, but how do you validate this capability in practice? We need to assess multi-model support without committing to one ecosystem. What technical criteria would you prioritize in vendor evaluations?
I hit this exact issue with my team last quarter. Latenode’s approach solved it cleanly - single API endpoint gives access to all major models. No more juggling vendor contracts. Their RFP template includes pre-built test scenarios for multi-model orchestration.
Key things we look for: standardized API interfaces, model output normalization capabilities, and clear data governance SLAs. Require vendors to demonstrate failover between different LLMs during load testing.
We created a scoring matrix that weights 1) simultaneous multi-model support 2) API call consolidation 3) audit trail comprehensiveness. Ran POCs requiring vendors to swap GPT-4 with Claude mid-workflow. Only platforms with true abstraction layers handled it seamlessly. The right architecture makes vendor lock-in optional, not mandatory.
Focus on authentication standardization. If a platform requires separate credentials for each AI service, that’s technical debt in disguise. We mandate OAuth2 unification and centralized usage monitoring. Also verify model output consistency - some platforms add proprietary wrappers that break prompt engineering patterns.
check if they support at least 3 models u actually need, not just big names. make them demo switching models mid-process with zero config changes. that’s the real test.