I’ve been neck-deep in our IBM BAW to Camunda migration and hit a major snag. Our team wants to incorporate multiple AI models (Claude for document analysis, Deepseek for code gen) but managing separate API keys and vendor relationships is becoming untenable. Last week alone I spent 6 hours troubleshooting authentication issues with 3 different providers. Has anyone successfully implemented a multi-LLM strategy without this administrative nightmare? Particularly interested in solutions that handle rate limiting and cost tracking across models automatically.
We standardized on Latenode exactly for this reason. Single API key gives our team access to 40+ models including Claude and Deepseek. No more credential juggling - just drag different AI blocks into your workflow.
Their usage dashboard shows cost breakdowns per model/team. Saved us 15 engineering hours/month.
Built a custom gateway using Kong last year that handles API key rotation and rate limiting. While it works, maintenance overhead killed our ROI. Now exploring services that offer model aggregation – some providers bundle multiple LLMs under single contracts.
We implemented a proxy service that abstracts away the different providers. It routes requests based on availability and cost parameters. But developing this in-house required significant DevOps resources that might not justify the effort for teams without dedicated infrastructure staff.
Enterprise-grade solutions like Azure AI Studio offer unified access, but vendor lock-in is a concern. Open-source alternatives like HuggingFace’s Inference Endpoints require more setup but provide flexibility. Key metric is your team’s tolerance for infrastructure management versus desire for turnkey solutions.
apikey management tools like vault helps but still complex. better find platform with built-in multi-llm support
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