ChatGPT itself, as an AI model, does not inherently "support" rate limiting; rather, its access via the OpenAI API is subject to rate limits enforced by OpenAI to ensure fair usage and system stability for all developers. Cloud-native applications integrating ChatGPT must therefore proactively implement robust rate-limiting strategies to effectively manage their API calls and avoid hitting these quotas. This typically involves employing client-side techniques such as exponential backoff and retry mechanisms to gracefully handle `429 Too Many Requests` responses. Furthermore, developers might deploy API gateways or custom middleware within their cloud-native infrastructure to enforce their own rate limits per user, tenant, or service. These internal limits can utilize algorithms like token buckets or leaky buckets to smooth out traffic, control costs, and prevent a single application component from exhausting the shared OpenAI API quota. By combining OpenAI's server-side limits with thoughtful client-side and application-level throttling, cloud-native apps ensure resilient and cost-effective integration. More details: https://support.magnaflow.com/trackonlinestore.asp?storename=https://abcname.com.ua