Kimi K3 is a multi-modal, open-weight foundation model (FM) that provider Moonshot AI reported achieved frontier-level coding and general agentic intelligence at release.

In addition to global and US-based inference, Amazon Bedrock now supports in-country inference in India for Kimi K3: unblocking local workloads with strict data residency requirements. Customers like Ultrahuman are already seeing the benefits in production:

“Jade, an AI Health and Wellness assistant in the Ultrahuman app, answers questions in plain language using each person’s own Ring and glucose data, across more than 14,000 conversations a day. Kimi K3 on Amazon Bedrock let us add a leading open-weight model through the API we already use, with health data staying inside AWS and Mumbai as a fallback Region. With prompt caching, we’ve cut LLM costs by 80 percent and made multi-turn chats 85 percent faster.”

In this post, we explain the different global and geographic inference options available, and share code for an example use case: optical character recognition (OCR) and understanding of Indic-language documents, with follow-up question answering in code-mixed Hinglish.

Kimi K3 on Amazon Bedrock is offered through cross-Region inference profiles. You can invoke the model from any of a wide range of AWS Regions (the “source”). Amazon Bedrock then securely processes your request in one of multiple possible “destination” Regions.

Cross-Region inference is fully managed by AWS: Data is encrypted in transit between Regions and remains on the AWS network, never traversing the public internet or requiring you to set up any multi-Region virtual private cloud (VPC) infrastructure.

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