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In this post, you will learn five practical patterns for building resilient generative AI applications on AWS, progressing from native Amazon Bedrock features to multi-model orchestration using an LLM gateway. These patterns address real-world challenges such as quota exhaustion during unexpected traffic surges, maximizing availability through geographic distribution of inference, and helping prevent noisy neighbor problems in multi-tenant environments.
Implementing resilience patterns for large language model (LLM) inference is critical as generative AI workloads move from experimentation to production at scale. With LLM powered apps now in production, organizations need ways to keep LLM inference highly available, responsive, and cost-effective at scale. Existing resilience best practices like static stability and implementing backoffs and retries still apply. However, generative AI introduces new considerations including model availability, rapidly changing quotas, token limits across multiple providers, and maintaining consistency with newly released models. Amazon Bedrock provides fully managed foundation models with built-in resilience features like cross-Region inference.