Building a Simple Multi-Model AI Request Layer

Connecting to an OpenAI-compatible API is easy: import OpenAI from "openai";

const client = new OpenAI({ apiKey: process.env.VECTRONODE_API_KEY, baseURL: process.env.AI_API_BASE_URL, }); However, connecting successfully is not the same as having a production-ready multi-model architecture. A durable application should separate product workloads from model providers. type Workload = | "support" | "coding" | "extraction";

type ModelPolicy = { primary: string; fallback?: string; timeoutMs: number; }; Create one policy for each workload: const policies: Record<Workload, ModelPolicy> = { support: { primary: process.env.SUPPORT_MODEL!, fallback: process.env.SUPPORT_FALLBACK_MODEL, timeoutMs: 8000, }, coding: { primary: process.env.CODE_MODEL!, fallback: process.env.CODE_FALLBACK_MODEL, timeoutMs: 20000, }, extraction: { primary: process.env.EXTRACTION_MODEL!, timeoutMs: 12000, }, }; Feature code requests a workload instead of a provider. This keeps model names out of business logic and makes future model changes easier. A production request layer should also record: Workload Selected model Latency Token usage Retry count Error type Avoid storing complete prompts by default because they may contain customer data or proprietary information. Fallbacks should only be used for temporary failures such as timeouts, rate limits, or server errors. Authentication failures and invalid parameters require configuration changes, not another model. VectorNode provides an OpenAI-compatible API layer for leading global and Chinese AI models. The gateway simplifies model access, while the application keeps control of workload policies, fallbacks, and observability. The result is a multi-model architecture that remains understandable and easier to operate.

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