Handling LLM API rate limits and downtime in production usually means adding retry logic with backoff, queuing requests during traffic spikes, and, for critical paths, having a fallback such as a secondary model provider or a cached response. Monitoring actual usage against the provider's published limits ahead of time avoids most rate-limit failures before they reach users.
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Yanka focuses on deep learning applied to visual inspection and natural-language analytics products. Based in Germany, she brings about 4 years of commercial delivery as an AI Engineer, translating ambiguous business question... Read More
Mykyta specializes in designing high-throughput backend services in Java for transaction-heavy platforms. Over ~10 years he has delivered microservices, REST APIs, and event-driven workflows with Kafka, taking features from a... Read More
Mustafa is a Fullstack or Backend Developer with a strong emphasis on Java and Spring technologies. With 15 years of experience, he has developed a deep expertise in building robust applications, utilizing Java, Spring Boot, ... Read More
Victoriia is a skilled Flutter Developer with 4 years of experience in mobile application development. She specializes in frameworks such as Flutter, leveraging JavaScript, DART, and utilizes databases like MySQL and Firebase... Read More
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