/batches

Covers Batches, Files

Feature	Supported	Notes
Supported Providers	OpenAI, Azure, Vertex	-
✨ 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

$ 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" \

Create File for Batch Completion

from litellm
import os 

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

from litellm
import os 

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


retrieved_batch = await litellm.aretrieve_batch(
    batch_id=create_batch_response.id, custom_llm_provider="openai"
)
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

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

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