Amazon Bedrock Adds India Inference for Kimi K3
Amazon Bedrock has introduced in-country inference for Moonshot AI's Kimi K3 foundation model, allowing local workloads to meet strict data residency requirements by keeping processing within the Mumbai and Hyderabad Regions.
Radwan AbdulhadiEditor and technology writer
In-Country Inference for Kimi K3 in India
Amazon Bedrock has expanded its capabilities by offering in-country inference in India for Kimi K3, a multi-modal, open-weight foundation model developed by Moonshot AI. According to provider Moonshot AI, the model achieved frontier-level coding and general agentic intelligence at its release.
This update allows organizations with strict data residency requirements to run local workloads while keeping data processing entirely within the Mumbai and Hyderabad Regions. The feature is managed through cross-Region inference profiles, letting users invoke the model from various source Regions while securely processing requests in designated destination Regions on the AWS network.
Early Production Adoption and Cost Savings
Early adopters are already implementing the service in production environments. Companies like Ultrahuman have integrated the technology into their mobile application to power features like their AI Health and Wellness assistant, Jade, which answers questions using individual ring and glucose data across thousands of daily conversations.
Aditya Patawari, VP of Engineering at Ultrahuman, noted that Kimi K3 on Amazon Bedrock enabled the company to add a leading open-weight model through their existing API while keeping health data inside AWS with Mumbai as a fallback Region. Patawari stated that prompt caching helped cut large language model costs by 80 percent and accelerated multi-turn chats by 85 percent.
Indic-Language Document Understanding and OCR
The integration opens up practical use cases across Indian financial services and public sector workflows, such as processing know your customer forms and utility bills. Kimi K3 can directly read and reason about multilingual images, performing optical character recognition and answering follow-up questions in code-mixed Hinglish without requiring separate digitization or translation services.
Developers looking to test or build these document processing workflows can access example code and implementation guidance directly on GitHub. The repository includes pipelines for transcribing documents in their original script, extracting key fields as JSON using configurable schemas, and handling multi-turn follow-up conversations.
Getting Started and Configuration Prerequisites
To set up and run inference workflows with Kimi K3 on Amazon Bedrock, developers need an active AWS account along with Python installed on their system. Additionally, users must install the required PyPI packages and configure the AWS Command Line Interface (AWS CLI) with proper permissions to invoke the model through the India inference profile.
Sources
- AWS Machine Learning BlogIntroducing India-based inference for Kimi K3 on Amazon Bedrock
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