Developers building AI products often start with one model provider. Then the project grows. You want to compare GPT, Claude, Gemini, Llama, or DeepSeek. You want to test cost, latency, output quality, and reliability. But every provider has its own dashboard, API key, billing flow, and integration details. That creates friction before the real product work even starts. The problem For AI builders, switching between model providers can mean: managing multiple API keys reading different docs comparing different pricing models changing integration logic tracking usage across multiple dashboards dealing with payment friction This is especially painful for builders working on: chatbots RAG apps AI agents backend AI features side projects A simpler approach Vector Engine API is built as a unified LLM API layer.…