Configuring Azure Blob Storage for Azure OpenAI

2025-05-19

Azure OpenAI now supports using Azure Blob Storage for Azure OpenAI Batch input and output files. By using your own storage, you aren't subject to the batch restrictions on the number of files.

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Azure Blob Storage configuration

Prerequisites

An Azure Blob Storage account.
An Azure OpenAI resource with a model of the deployment type Global-Batch or DataZoneBatch deployed. You can refer to the resource creation and model deployment guide for help with this process.

Managed identity

In order for your Azure OpenAI resource to securely access your Azure Blob Storage account you need setup your resource with a system assigned managed identity.

Note

Currently user assigned managed identities aren't supported.

Sign in to https://portal.azure.com.
Find your Azure OpenAI resource > Select Resource Management > Identity > System assigned > set status to On.

Role-based access control

Once your Azure OpenAI resource has been configured for system assigned managed identity, you need to give it access to your Azure Blob Storage account.

From https://portal.azure.com find and select your Azure Blob Storage resource.
Select Access Control (IAM) > Add > Add role assignment.
Search for Storage Blob Data Contributor > Next.
Select Managed identity > +Select members > Select your Azure OpenAI resources's managed identity.

If you prefer using custom roles for more granular access, the following permissions are required:

Input data:

Microsoft.Storage/storageAccounts/blobServices/containers/blobs/read

Output data/folders:

Microsoft.Storage/storageAccounts/blobServices/containers/blobs/read
Microsoft.Storage/storageAccounts/blobServices/containers/blobs/write

Create containers

For this example you'll create two containers named batch-input, and batch-output. You can name these whatever you want, but if you use an alternate name you'll need to adjust the examples in the following steps.

To create a container under Data storage > Select +Container > Name your containers.

Once your containers are created retrieve the URL for each container by selecting the container > Settings > Properties > Copy the URLs.

In this case we have:

https://{AZURE-BLOB-STORAGE-RESOURCE-NAME}.blob.core.windows.net/batch-input
https://{AZURE-BLOB-STORAGE-RESOURCE-NAME}.blob.core.windows.net/batch-output

Create input file

For this article, we'll create a file named test.jsonl and will copy the contents below to the file. You'll need to modify and add your global batch deployment name to each line of the file.

{"custom_id": "task-0", "method": "POST", "url": "/chat/completions", "body": {"model": "REPLACE-WITH-MODEL-DEPLOYMENT-NAME", "messages": [{"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": "When was Microsoft founded?"}]}}
{"custom_id": "task-1", "method": "POST", "url": "/chat/completions", "body": {"model": "REPLACE-WITH-MODEL-DEPLOYMENT-NAME", "messages": [{"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": "When was the first XBOX released?"}]}}
{"custom_id": "task-2", "method": "POST", "url": "/chat/completions", "body": {"model": "REPLACE-WITH-MODEL-DEPLOYMENT-NAME", "messages": [{"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": "What is Altair Basic?"}]}}

Upload training file

From your Azure Blob Storage account, open your batch-input container that you created previously.

Select Upload and select your test.jsonl file.

During the time that we're processing your jsonl file as part of the batch job, you can't make any changes to the file. If a file changes while the batch job is running the job will fail.

Create batch job

Note

metadata is currently not supported with this capability.

Python
REST

import os
from datetime import datetime
from openai import AzureOpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
)

client = AzureOpenAI(
  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
  azure_ad_token_provider=token_provider,
  api_version="2025-04-01-preview"
)

batch_response = client.batches.create(
    input_file_id=None,
    endpoint="/chat/completions",
    completion_window="24h",
      extra_body={ 
        "input_blob": "https://{AZURE-BLOB-STORAGE-RESOURCE-NAME}.blob.core.windows.net/batch-input/test.jsonl",
        "output_folder": {
                "url": "https://{AZURE-BLOB-STORAGE-RESOURCE-NAME}.blob.core.windows.net/batch-output",
        }
    }   
)

# Save batch ID for later use
batch_id = batch_response.id

print(batch_response.model_dump_json(indent=2))

curl -X POST https://YOUR_RESOURCE_NAME.openai.azure.com/openai/batches?api-version=2025-04-01-preview \
  -H "Authorization: Bearer $AZURE_OPENAI_AUTH_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "input_file_id": null,
    "endpoint": "/chat/completions",
    "completion_window": "24h",
    "input_blob": "https://{AZURE-BLOB-STORAGE-RESOURCE-NAME}.blob.core.windows.net/batch-input/test.jsonl",
    "output_folder": {
      "url": "https://{AZURE-BLOB-STORAGE-RESOURCE-NAME}.blob.core.windows.net/batch-output"
    }
  }'

Output:

{
  "id": "batch_b632a805-797b-49ed-9c9c-86eb4057f2a2",
  "completion_window": "24h",
  "created_at": 1747516485,
  "endpoint": "/chat/completions",
  "input_file_id": null,
  "object": "batch",
  "status": "validating",
  "cancelled_at": null,
  "cancelling_at": null,
  "completed_at": null,
  "error_file_id": null,
  "errors": null,
  "expired_at": null,
  "expires_at": 1747602881,
  "failed_at": null,
  "finalizing_at": null,
  "in_progress_at": null,
  "metadata": null,
  "output_file_id": null,
  "request_counts": {
    "completed": 0,
    "failed": 0,
    "total": 0
  },
  "error_blob": "",
  "input_blob": "https://docstest002.blob.core.windows.net/batch-input/test.jsonl",
  "output_blob": ""
}

You can monitor the status the same way you would previously as outlined in our comprehensive guide on using Azure OpenAI batch.

import time
import datetime 

status = "validating"
while status not in ("completed", "failed", "canceled"):
    time.sleep(60)
    batch_response = client.batches.retrieve(batch_id)
    status = batch_response.status
    print(f"{datetime.datetime.now()} Batch Id: {batch_id},  Status: {status}")

if batch_response.status == "failed":
    for error in batch_response.errors.data:  
        print(f"Error code {error.code} Message {error.message}")

Output:

2025-05-17 17:16:56.950427 Batch Id: batch_b632a805-797b-49ed-9c9c-86eb4057f2a2,  Status: validating
2025-05-17 17:17:57.532054 Batch Id: batch_b632a805-797b-49ed-9c9c-86eb4057f2a2,  Status: validating
2025-05-17 17:18:58.156793 Batch Id: batch_b632a805-797b-49ed-9c9c-86eb4057f2a2,  Status: in_progress
2025-05-17 17:19:58.739708 Batch Id: batch_b632a805-797b-49ed-9c9c-86eb4057f2a2,  Status: in_progress
2025-05-17 17:20:59.398508 Batch Id: batch_b632a805-797b-49ed-9c9c-86eb4057f2a2,  Status: finalizing
2025-05-17 17:22:00.242371 Batch Id: batch_b632a805-797b-49ed-9c9c-86eb4057f2a2,  Status: completed

Once the status is completed you can retrieve your output_blob path:

print(batch_response.model_dump_json(indent=2))

Output:

{
  "id": "batch_b632a805-797b-49ed-9c9c-86eb4057f2a2",
  "completion_window": "24h",
  "created_at": 1747516485,
  "endpoint": "/chat/completions",
  "input_file_id": null,
  "object": "batch",
  "status": "completed",
  "cancelled_at": null,
  "cancelling_at": null,
  "completed_at": 1747516883,
  "error_file_id": null,
  "errors": null,
  "expired_at": null,
  "expires_at": 1747602881,
  "failed_at": null,
  "finalizing_at": 1747516834,
  "in_progress_at": 1747516722,
  "metadata": null,
  "output_file_id": null,
  "request_counts": {
    "completed": 3,
    "failed": 0,
    "total": 3
  },
  "error_blob": "https://docstest002.blob.core.windows.net/batch-output/{GUID}/errors.jsonl",
  "input_blob": "https://docstest002.blob.core.windows.net/batch-input/test.jsonl",
  "output_blob": "https://docstest002.blob.core.windows.net/batch-output/{GUID}/results.jsonl"
}

Once your batch job is complete, you can download the error_blob and output_blob via the Azure Blob Storage interface in the Azure portal or you can download programmatically:

Note

error_blob, and output_blob paths are always returned in the response even in cases where a corresponding file isn't created. In this case there were no errors so errors.jsonl wasn't created, only results.jsonl exists.

pip install azure-identity azure-storage-blob

Keep in mind that while you have granted the Azure OpenAI resource programmatic access to your Azure Blob Storage, to download the results you might need to also give the user account that is executing the script below access as well. For downloading the file, Storage Blob Data Reader access is sufficient.

# Import required libraries
from azure.identity import DefaultAzureCredential
from azure.storage.blob import BlobServiceClient

# Define storage account and container information
storage_account_name = "docstest002" # replace with your storage account name
container_name = "batch-output"

# Define the blob paths to download
blob_paths = [
    "{REPLACE-WITH-YOUR-GUID}/results.jsonl",
]

credential = DefaultAzureCredential()
account_url = f"https://{storage_account_name}.blob.core.windows.net"
blob_service_client = BlobServiceClient(account_url=account_url, credential=credential)
container_client = blob_service_client.get_container_client(container_name)

for blob_path in blob_paths:
    blob_client = container_client.get_blob_client(blob_path)
    
    file_name = blob_path.split("/")[-1]
    
    print(f"Downloading {file_name}...")
    with open(file_name, "wb") as file:
        download_stream = blob_client.download_blob()
        file.write(download_stream.readall())
    
    print(f"Downloaded {file_name} successfully!")

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Configuring Azure Blob Storage for Azure OpenAI

Region Support

Azure Blob Storage configuration

Prerequisites

Managed identity

Role-based access control

Create containers

Create input file

Upload training file

Create batch job

See also

Feedback

Additional resources