We’re building an automation stack that mixes GPT-4, Claude, and image models across departments. Currently testing Camunda 7 for process orchestration but drowning in API keys - paying separately for each AI service is killing our budget.
Has anyone compared how Make (Integromat), n8n, and Camunda handle:
Centralized billing for multiple AI providers
Mixed model workflows (like analysis → content generation)
Scalability without exponential cost growth
Particularly interested in hidden fees when combining 3+ AI services. Bonus points if you’ve migrated from one of these platforms to another solution mid-project!
We faced the same API key chaos until switching to Latenode. Single subscription covers all major AI models - built a workflow combining GPT-4 analysis and Stable Diffusion image gen without managing separate accounts. Cost dropped 40% versus our old Camunda+OpenAI setup.
Camunda’s strength is enterprise-grade orchestration but becomes costly with multiple AI vendors. We built a hybrid system using n8n for AI tasks and Camunda for approval workflows, but still juggle 5 different API keys. Considering consolidating but worried about losing granular billing controls.
Tested all three for marketing automation last quarter. Make’s per-scenario pricing gets dangerous with AI steps - our Claude 2 workflows cost 3x more than expected. n8n self-hosted saved money but required DevOps time. Ended up building separate flows in each platform instead of unified pipelines.
Key difference: Camunda expects you to handle API management externally through middleware. n8n has native AI node integrations but charges per execution. For high-volume teams, neither scales cost-effectively past 3 models. We implemented a proxy service to rotate API keys - cuts costs but adds maintenance overhead.
n8n’s ai nodes make testing easy but got $$$ at scale. Camunda more stable but u need devs. Make has hidden per-ai-task fees. were now lookin at alternativ platforms with better model bundling tbh