{
  "id": 12464208,
  "title": "How I Built a Unified API Gateway for 200+ AI Models (Architecture Deep Dive)",
  "url": "https://urgent.news/2026/10/06/how-i-built-a-unified-api-gateway-for-200-ai-models-architecture-deep",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-06T20:37:16.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/sarangai_id/how-i-built-a-unified-api-gateway-for-200-ai-models-architecture-deep-dive-3559"
  },
  "original_language": "en",
  "account": "Developers who work with multiple AI providers understand the frustration of dealing with different APIs, billing systems, and documentation. This can significantly hinder experimentation with new models. To address this, I created SarangAI, a unified gateway that routes requests to over 200 AI models via a single, OpenAI-compatible endpoint. Here, I'll detail the architecture, design choices, and technical hurdles of SarangAI.\n\nThe architecture of SarangAI is centered around four key components: the API Gateway, Router, Provider Adapters, and Billing & Rate Limiter. The gateway receives client requests and passes them to the router, which decides which model to use. The provider adapters then translate the OpenAI-compatible format into each provider's specific format, while the billing and rate limiter manages prepaid balances and per-user limits.\n\nOne of the main design decisions was to standardize on the OpenAI-compatible format. This choice simplifies integration, as developers can easily switch from existing SDKs by merely changing the base URL and API key. The OpenAI format's widespread adoption ensures a vast ecosystem of libraries and tools, as well as a familiar structure for developers.\n\nTechnical challenges arose in normalizing responses, handling streaming, and implementing rate limiting. To tackle these, I used an adapter pattern for each provider, normalizing all streaming responses to the same Server-Sent Events (SSE) format used by OpenAI, and implementing a prepaid balance system using Redis and a database for real-time tracking and audit trails.\n\nAnother key feature is instant model switching, which requires a stateless router capable of handling multiple configurations without downtime. The SarangAI CLI tool, accessible via npm install -g sarangai-cli, provides an interactive command-line interface for switching models, checking token usage, and reviewing balances. This tool is especially valuable for developers who prefer working in the terminal.\n\nThrough my experience building SarangAI, I learned that standards are crucial, adapter patterns simplify integration, prepaid subscriptions appeal to developers, and comprehensive documentation is essential for adoption. I encourage those who regularly work with multiple AI models to try SarangAI and share their setups in the comments for a discussion on handling multiple AI providers.",
  "summary": "Every developer who has worked with more than one AI provider knows the pain: OpenAI has its own API key Anthropic has its own billing dashboard Google has its own SDK DeepSeek has its own rate limits If you want to use 4 different models, you have to manage 4 accounts, 4 invoices, and 4 sets of documentation. This isn't just annoying — it's a bottleneck that stops developers from experimenting…",
  "key_points": [
    "SarangAI is a unified API gateway routing requests to over 200 AI models via a single endpoint",
    "OpenAI-compatible format simplifies integration with existing SDKs and libraries",
    "Adapter pattern normalizes streaming responses to Server-Sent Events (SSE) format"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}