Inference Providers vs. API Routers: where do tokens come from?

When using large language models (LLMs) via API, it is crucial to understand the distinction between direct inference providers and API routers. Direct providers secure dedicated GPU compute and control both the API endpoint and hardware, ensuring a consistent execution of requests. In contrast, API routers like OpenRouter act as intermediary layers that forward requests to upstream providers without processing them directly, akin to marketplace platforms like DoorDash. While routers can enhance reliability by rerouting traffic to avoid overloaded endpoints, they inherently add latency compared to direct access. Furthermore, routers may have limited control over data privacy and security, especially concerning shadow traffic, which involves duplicating requests for evaluation and is undetectable in logs. This suggests caution for compliance-sensitive workloads, as routers cannot enforce data retention policies on upstream providers. Ultimately, understanding the infrastructure behind API endpoints is essential, particularly for workloads with high data sensitivity or low latency requirements.

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