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Kimi K3 on Amazon Bedrock: India Inference, Pricing and Data Residency
AWS now offers India-only inference routing for Kimi K3, with a distinction between where a request runs and where its logs remain.
AWS announced India-based inference for Kimi K3 on Amazon Bedrock on November 3. The new geographic profile keeps model processing within Mumbai and Hyderabad, giving teams with India data-residency requirements another managed model option without building their own inference infrastructure.
The change is about where an existing model runs. It is not a new Kimi model or the promised K3.1 Pro weights release. For an engineering team whose deployment is blocked by processing-location requirements, that distinction can matter more than a benchmark improvement.
How India-based Kimi K3 inference works
The AWS announcement identifies in.moonshotai.kimi-k3 as the India inference profile. Requests use Bedrock's managed cross-Region routing, with Mumbai and Hyderabad as the eligible processing destinations.
India-only does not mean Mumbai-only. AWS can select either destination. An application that needs a specific single Region cannot assume this profile meets that requirement.
This also means the request endpoint alone is not enough to describe the deployment. The source Region is where the application sends its request. The inference profile determines where Bedrock can process it. Teams should record both in architecture reviews rather than treating the model name as a complete deployment specification.
Kimi K3 Bedrock pricing
AWS's Kimi K3 model card lists the following Standard-tier prices per million tokens:
| Routing option | Input | Output | Cache read | Cache write, 30 minutes |
|---|---|---|---|---|
| Global | $3.00 | $15.00 | $0.30 | $3.75 |
| India geographic | $3.30 | $16.50 | $0.33 | $4.125 |
The India rate is 10% above the global rate. That is a concrete cost for the narrower processing boundary, not evidence that the model is faster or more accurate in India.
For a document assistant, the more useful cost calculation is per completed task. Repeatedly sending a long document, producing verbose reasoning and retrying incorrect extractions can dominate a small routing premium. Cache behavior and output length deserve attention alongside the headline input price.
API access and integration limits
The model card lists Mumbai and Hyderabad as supported India-profile source Regions. Kimi K3 has a one-million-token context window and supports text and image inputs. Bedrock exposes it through Chat Completions, Responses, Converse and InvokeModel.
AWS recommends the OpenAI-compatible APIs for this model. Its documentation warns that Converse can fail when prior-turn reasoning content is included, and rejects attached documents such as PDFs and HTML. Explicit caching is also unavailable through Converse for Kimi K3. An existing framework integration therefore needs testing even when it already supports Bedrock.
A useful first workload is structured extraction from mixed-language documents. AWS demonstrates Hindi document reading followed by Hinglish questions. The sample shows a possible workflow, rather than proof that arbitrary scans or every Indic language will work reliably. Amounts, dates and identifiers still need validation before they affect a customer-facing decision.
Where the data boundary needs checking
AWS says invocation logs and metrics remain in the source Region, even when inference happens elsewhere. Model invocation logging is optional and can include prompts and outputs. The processing destination and the logging configuration should therefore be checked together.
The cross-Region inference documentation is the relevant starting point for reviewing routing and permissions. A restrictive policy must accommodate the destinations the chosen profile actually uses, rather than unintentionally blocking its fallback path.
Beyond Bedrock, an application may still send documents, traces or retrieved content to other services. A model's India processing boundary does not automatically apply to the whole application. Teams need to follow the data through storage, retrieval, monitoring and any external tools the agent calls.
What this changes for Indian AI teams
Managed geographic inference gives teams a middle option between global API routing and operating a model themselves. It can remove an infrastructure obstacle while keeping the existing Bedrock integration, although workload accuracy, permissions and application-level data handling still need review.
The practical next step is to compare a small set of real tasks under the India profile, including failed requests and logging behavior. A model becomes a usable enterprise option when the team can explain both what it can do and where the request goes when the primary destination is busy.
I'm Paras. I work at the intersection of AI, engineering and distribution.
I help teams explain complex AI infrastructure and product decisions in language developers and business buyers can use.