# OpenAI API Processing in India: GPT-6 Luna Requirements and Limits

India regional processing is available for a narrow API configuration, not every model or feature exposed by an India endpoint.

By Paras Madan | 2026-11-12


OpenAI's API documentation now lists regional processing in India for GPT-6 Luna. It requires active Zero Data Retention, an India-region project and Standard pay-as-you-go requests. Changing the hostname alone is not enough, and the supported configuration does not include hosted tools.

For a team building an Indian customer-support product, the useful question is whether its complete request fits that configuration. A local inference option can solve one architectural constraint while leaving the rest of the application unchanged.

## What the India configuration supports

The current [API data-controls documentation](https://developers.openai.com/api/docs/guides/your-data) specifies this scope:

| Check | India regional-processing requirement |
| --- | --- |
| Model | `gpt-6-luna` |
| APIs | Responses and Chat Completions |
| Input and output | Text or image input; text output |
| Billing mode | Standard pay-as-you-go |
| Retention | Active Zero Data Retention; Modified Abuse Monitoring alone is insufficient |
| Project and host | India-region project and `in.api.openai.com` |
| Hosted tools | Unavailable for regional processing |

Non-US residency also requires approval for abuse-monitoring controls and a Modified Retention amendment. Image support has additional enhanced-retention approval requirements. Confirm those before treating the image row as ready for production.

**Regional storage and regional processing are different promises.** A service appearing in India's storage list does not mean its computation happens there. The documentation also excludes system data and third-party services from its residency commitment.

## Review the complete request, not just the model

Take a support-ticket assistant that reads a customer message, searches documentation, drafts an answer and records the result. Before migration, draw that path as four separate operations. Identify which component performs each operation and where its input, output and logs go.

For the first trial, keep only the drafting step. Give it a synthetic ticket and a short, approved knowledge excerpt already present in the request. This makes the test easier to interpret: you are checking a model call, not debugging retrieval, tools and deployment at once.

Then review the surrounding application independently. Where does the knowledge excerpt come from? Does your tracing service capture the full prompt? Does a failed request enter a queue in another region? None of those questions is answered by an inference hostname. They belong in the application's data-flow review.

## Keep the request inside the documented scope

An illustrative Python setup for an already eligible project is:

```python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["OPENAI_INDIA_PROJECT_KEY"],
    base_url="https://in.api.openai.com/v1",
)

response = client.responses.create(
    model="gpt-6-luna",
    input="Draft a short reply to this synthetic ticket: How do I reset my password?",
    store=False,
)
print(response.output_text)
```

This example has not been run against an approved India project. It shows the intended separation of configuration, credentials and a harmless test input; it does not enable eligibility or prove a regional-processing guarantee. Setting `store=False` is not a substitute for the required organizational retention approval.

Use a dedicated configuration for this route instead of scattering hostnames and model names through the codebase. That makes review practical: a teammate should be able to identify the project, model and destination without reading every prompt.

## Check fallback behavior before sending real customer content

If a region-constrained request fails, an automatic retry to a global endpoint could defeat the reason you selected the region. Review fallback routing explicitly. For workloads with a hard geographic requirement, my recommendation is to return a controlled failure rather than quietly change the destination.

Test that policy with synthetic inputs. Deliberately exercise a timeout or unavailable configuration in your own test harness, and inspect which destination the application attempts next. A successful ordinary response tells you very little about what happens during an outage.

OpenAI's [business data commitments](https://openai.com/business-data/) separately say API inputs and outputs are not used for model training by default. That is useful, but training policy, retention and processing location answer different questions. Keep them separate in a customer security explanation, too.

The practical opportunity is a tightly bounded workflow you can inspect and defend. Start with one eligible call and its surrounding data path before describing an entire product as operating within India.

I’m Paras. I work at the intersection of AI, engineering and distribution.

I help teams explain enterprise AI architecture and product constraints clearly to developers and customers.

[Book a 30-minute call](https://cal.com/paras-madan/30min).