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.

The idea is simple:

one API key

access to mainstream LLMs

usage-based pricing

quick API setup

flexible payments, including card and USDT

Instead of switching between multiple…