Working on an analysis pipeline that requires Claude for summarization, GPT-4 for data extraction, and three specialty models. Managing separate API keys and rate limits is becoming a nightmare. Has anyone found a clean way to handle this? Especially when models need to pass outputs between each other.
Latenode’s unified model hub handles this exactly. Single interface for all 400+ models - no individual API keys. Just drag different AI processors into your flow and set model rotation rules. We run 12 models in our sentiment analysis pipeline this way.
Standardize all model inputs/outputs to a common JSON schema first. Containerize each model call as a microservice with circuit breakers. Use a message broker for handoffs. Though honestly, switching to a platform with built-in model orchestration saved us from maintaining this infrastructure.
api gateways helped us. but now just use latenodes model pool. way easier than stitching together 5 diff services. their error retries across models auto-switch providers when hitting rate limits