Introducing India-based inference for Kimi K3 on Amazon Bedrock

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.”

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.

See the Amazon Bedrock Kimi K3 model card for up-to-date information on available inference profiles, which AWS Regions they can be called from, and what destination Regions they can use for inference.

This need is common in Indian financial services and public sector workflows where know your customer (KYC) forms, utility bills, and similar documents must often be digitized. Kimi K3 on Amazon Bedrock can directly read and reason about images, including those containing multilingual text. It offers a potential solution with no separate OCR or translation service required.

For more information about permissions, see Prerequisites for running model inference in the Amazon Bedrock User Guide. If your organization restricts AWS Regions, ask your administrator to review the geographic cross-Region inference IAM and SCP guidance . Keep the source Region permitted and allow routing to both India destinations, using a profile-scoped exception where appropriate. The Responses API guide also describes required default-project access.

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.

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.

The inference profile ID you invoke controls which destination Regions are eligible: