Uber and OpenAI Enhance Rate Limiting Systems

Uber and OpenAI are revamping their rate limiting systems, replacing static limits with adaptive infrastructure-level platforms. Uber has implemented the Global Rate Limiter, which uses a technique called probabilistic shedding to manage up to 80 million requests per second (RPS). Meanwhile, OpenAI has adopted the Access Engine, which applies a cascading credit system to prevent user disruptions.

These new architectures rely on distributed controls and smooth adjustments to ensure system stability and service continuity at scale. Uber's system is designed to handle a massive volume of simultaneous requests, while OpenAI focuses on minimizing interruptions to user experience.

The adaptive model allows companies to automatically adjust system responsiveness based on real-time demand, avoiding overloads and improving efficiency. Unlike static limits, which can be inflexible during peak situations, these adaptive solutions offer greater resilience.

Looking ahead, these approaches could become standards in industries dealing with large data volumes, providing a more robust and reliable service. The adoption of adaptive technologies may represent a significant evolution in how companies manage traffic and demand on their platforms.