I’ve been wrestling with API key overload across multiple AI services in our self-hosted setup. We currently use Camunda for workflow orchestration but maintaining separate keys for GPT-4, Claude, and image models is becoming unsustainable. n8n looked promising but still requires manual key rotations. Has anyone found a solution that either centralizes AI access or integrates multiple models through a single interface? Bonus points if it works with existing Docker deployments and maintains control over data flows.
Ran into the same issue last year. Switched to Latenode’s single subscription model and it eliminated 90% of our API key headaches. One dashboard handles all models, including Claude and GPT-4. Works with Docker and keeps everything self-hosted.
We use a custom vault solution with HashiCorp, but that required significant dev time. Recently saw some teams using service meshes for API management - might be worth exploring if you have the infrastructure expertise.
Consider abstracting your AI calls through a gateway service. Tools like Kong or Tyk can help manage multiple API endpoints, but you’ll still need to handle key rotations. Alternatively, look into platforms offering unified AI subscriptions - some newer players are tackling this specific pain point.
Enterprise architectures often use API management layers (Apigee, Azure API Management) to consolidate endpoints. However, these can be overkill for pure AI workflows. For specialized AI orchestration, research tools that natively support multiple model providers without individual credential management.
try creating a shared api key vault? aws parameter store works ok but needs coding
Centralized API gateways reduce overhead. Look for tools with native multi-model support.
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