In recent developments, Uber and OpenAI have transitioned their rate-limiting strategies from traditional counter-based systems to adaptive, policy-driven architectures. Both companies now utilize proprietary platforms at the infrastructure level that implement soft controls, enhancing system resilience without hindering user engagement. Uber’s Global Rate Limiter (GRL) replaces per-service limits with a feedback loop, efficiently managing over 80 million requests per second and reducing latency while safeguarding against DDoS attacks. OpenAI’s approach focuses on user experience for its Codex and Sora applications, introducing a credit-based system that maintains continuity during usage. This model creates a seamless experience with integrated billing, allowing users to utilize services without interruptions. By replacing hard constraints with soft limits, both firms have achieved operational goals, optimizing performance and reducing maintenance risks while ensuring that users can explore their platforms freely. This shift towards automated, adaptive controls highlights the importance of scalable solutions in high-demand environments.
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