/batches

Covers Batches, Files

Feature	Supported	Notes
Supported Providers	OpenAI, Azure, Vertex, Bedrock	-
✨ Cost Tracking	✅	LiteLLM Enterprise only
Logging	✅	Works across all logging integrations

Quick Start

• Create File for Batch Completion

• Create Batch Request

• List Batches

• Retrieve the Specific Batch and File Content

LiteLLM PROXY Server

$ export OPENAI_API_KEY="sk-..."

$ litellm

# RUNNING on http://0.0.0.0:4000

Create File for Batch Completion

curl http://localhost:4000/v1/files \
    -H "Authorization: Bearer sk-1234" \
    -F purpose="batch" \
    -F file="@mydata.jsonl"

Create Batch Request

curl http://localhost:4000/v1/batches \
        -H "Authorization: Bearer sk-1234" \
        -H "Content-Type: application/json" \
        -d '{
            "input_file_id": "file-abc123",
            "endpoint": "/v1/chat/completions",
            "completion_window": "24h"
    }'

Retrieve the Specific Batch

curl http://localhost:4000/v1/batches/batch_abc123 \
    -H "Authorization: Bearer sk-1234" \
    -H "Content-Type: application/json" \

List Batches

curl http://localhost:4000/v1/batches \
    -H "Authorization: Bearer sk-1234" \
    -H "Content-Type: application/json" \

SDK

Create File for Batch Completion

import litellm
import os 
import asyncio

os.environ["OPENAI_API_KEY"] = "sk-.."

file_name = "openai_batch_completions.jsonl"
_current_dir = os.path.dirname(os.path.abspath(__file__))
file_path = os.path.join(_current_dir, file_name)
file_obj = await litellm.acreate_file(
    file=open(file_path, "rb"),
    purpose="batch",
    custom_llm_provider="openai",
)
print("Response from creating file=", file_obj)

Create Batch Request

import litellm
import os 
import asyncio

create_batch_response = await litellm.acreate_batch(
    completion_window="24h",
    endpoint="/v1/chat/completions",
    input_file_id=batch_input_file_id,
    custom_llm_provider="openai",
    metadata={"key1": "value1", "key2": "value2"},
)

print("response from litellm.create_batch=", create_batch_response)

Retrieve the Specific Batch and File Content

    # Maximum wait time before we give up
    MAX_WAIT_TIME = 300  

    # Time to wait between each status check
    POLL_INTERVAL = 5
    
    #Time waited till now 
    waited = 0

    # Wait for the batch to finish processing before trying to retrieve output
    # This loop checks the batch status every few seconds (polling)

    while True:
        retrieved_batch = await litellm.aretrieve_batch(
            batch_id=create_batch_response.id,
            custom_llm_provider="openai"
        )
        
        status = retrieved_batch.status
        print(f"⏳ Batch status: {status}")
        
        if status == "completed" and retrieved_batch.output_file_id:
            print("✅ Batch complete. Output file ID:", retrieved_batch.output_file_id)
            break
        elif status in ["failed", "cancelled", "expired"]:
            raise RuntimeError(f"❌ Batch failed with status: {status}")
        
        await asyncio.sleep(POLL_INTERVAL)
        waited += POLL_INTERVAL
        if waited > MAX_WAIT_TIME:
            raise TimeoutError("❌ Timed out waiting for batch to complete.")

print("retrieved batch=", retrieved_batch)
# just assert that we retrieved a non None batch

assert retrieved_batch.id == create_batch_response.id

# try to get file content for our original file

file_content = await litellm.afile_content(
    file_id=batch_input_file_id, custom_llm_provider="openai"
)

print("file content = ", file_content)

List Batches

list_batches_response = litellm.list_batches(custom_llm_provider="openai", limit=2)
print("list_batches_response=", list_batches_response)

Supported Providers:

Azure OpenAI

OpenAI

Vertex AI

Bedrock

How Cost Tracking for Batches API Works

LiteLLM tracks batch processing costs by logging two key events:

Event Type	Description	When it's Logged
acreate_batch	Initial batch creation	When batch request is submitted
batch_success	Final usage and cost	When batch processing completes

Cost calculation:

• LiteLLM polls the batch status until completion
• Upon completion, it aggregates usage and costs from all responses in the output file
• Total token and response_cost reflect the combined metrics across all batch responses

Swagger API Reference