Batch predictions

Note: Billing for batch prediction for OpenMaaS embedding models is enabled on October 3, 2025.

Batch predictions lets you efficiently send multiple text-only prompts thataren't latency sensitive to a model. Compared to online predictions, where yousend one input prompt for each request, you can batch a large number of inputprompts in a single request.

Supported models

Vertex AI supports batch predictions for the following models.

Llama:

OpenAI gpt-oss:

Qwen:

DeepSeek:

Embedding models:

Prepare input

Before you begin, prepare your inputs in a BigQuery table or as aJSONL file in Cloud Storage. The input for both sources must follow theOpenAI API schema JSONL format, as shown in the following examples.

Large language models:

{"custom_id": "test-request-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "MODEL_ID", "messages": [{"role": "user", "content": "Give me a recipe for banana bread"}], "max_tokens": 1000}}

Embedding models:

{"custom_id": "test-request-0", "method": "POST", "url": "/v1/embeddings", "body": {"model": "MODEL_ID", "input": "Hello World"}}

Note: Vertex AI doesn't use thecustom_id,method,url, andmodelfields. You can include them but they are ignored by the batch prediction job.

BigQuery

Your BigQuery input table must adhere to the followingschema:

Column name	Description
custom_id	An ID for each request to match the input with the output.
method	The request method.
url	The request endpoint.
body(JSON)	Your input prompt.

Your input table can have other columns, which are ignored by the batchjob and passed directly to the output table.
Batch prediction jobs reserve two column names for the batch predictionoutput:response(JSON) andid. Don't use these columns in the inputtable.
Themethod andurl columns are dropped and not included in theoutput table.

Cloud Storage

For Cloud Storage, the input file must be a JSONL file that islocated in a Cloud Storage bucket.

The global endpoint for open models isn't supported.

Request a batch prediction

Make a batch prediction against a model by using input from BigQuery orCloud Storage.You can independently choose to output predictions to either aBigQuery table or a JSONL file in a Cloud Storagebucket.

BigQuery

Specify your BigQuery input table, model, and output location.The batch prediction job and your table must be in the same region.

REST

After youset up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

LOCATION: A region that supports the model.
PROJECT_ID: Yourproject ID.
MODEL: The name of themodel to tune.
INPUT_URI: The BigQuery table where your batch prediction input is located such asmyproject.mydataset.input_table.
OUTPUT_FORMAT: To output to a BigQuery table, specifybigquery. To output to a Cloud Storage bucket, specifyjsonl.
DESTINATION: For BigQuery, specifybigqueryDestination. For Cloud Storage, specifygcsDestination.
OUTPUT_URI_FIELD_NAME: For BigQuery, specifyoutputUri. For Cloud Storage, specifyoutputUriPrefix.
OUTPUT_URI: For BigQuery, specify the table location such asmyproject.mydataset.output_result. For Cloud Storage, specify the bucket and folder location such asgs://mybucket/path/to/outputfile.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs

Request JSON body:

'{  "displayName": "JOB_NAME",  "model": "publishers/PUBLISHER/models/MODEL_ID",  "inputConfig": {    "instancesFormat":"bigquery",    "bigquerySource":{      "inputUri" : "INPUT_URI"    }  },  "outputConfig": {    "predictionsFormat":"OUTPUT_FORMAT",    "DESTINATION":{      "OUTPUT_URI_FIELD_NAME": "OUTPUT_URI"    }  }}'

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by running gcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs"

PowerShell

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by running gcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{"name":  "projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/BATCH_JOB_ID",  "displayName": "JOB_NAME",  "model": "publishers/PUBLISHER/models/MODEL_ID",  "inputConfig": {    "instancesFormat":"bigquery",    "bigquerySource":{      "inputUri" : "INPUT_URI"    }  },  "outputConfig": {    "predictionsFormat":"OUTPUT_FORMAT",    "DESTINATION":{      "OUTPUT_URI_FIELD_NAME": "OUTPUT_URI"    }  },  "state": "JOB_STATE_PENDING",  "createTime": "2024-10-16T19:33:59.153782Z",   "updateTime": "2024-10-16T19:33:59.153782Z",   "labels": {    "purpose": "testing"  },  "modelVersionId": "1"}

Cloud Storage

Specify your JSONL file's Cloud Storage location, model, and outputlocation.

REST

After youset up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

LOCATION: A region that supports the model.
PROJECT_ID: Yourproject ID.
MODEL: The name of themodel to tune.
INPUT_URI: The Cloud Storage location of your JSONL batch prediction input such asgs://bucketname/path/to/jsonl.
OUTPUT_FORMAT: To output to a BigQuery table, specifybigquery. To output to a Cloud Storage bucket, specifyjsonl.
DESTINATION: For BigQuery, specifybigqueryDestination. For Cloud Storage, specifygcsDestination.
OUTPUT_URI_FIELD_NAME: For BigQuery, specifyoutputUri. For Cloud Storage, specifyoutputUriPrefix.
OUTPUT_URI: For BigQuery, specify the table location such asmyproject.mydataset.output_result. For Cloud Storage, specify the bucket and folder location such asgs://mybucket/path/to/outputfile.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs

Request JSON body:

'{  "displayName": "JOB_NAME",  "model": "publishers/PUBLISHER/models/MODEL_ID",  "inputConfig": {    "instancesFormat":"jsonl",    "gcsSource":{      "uris" : "INPUT_URI"    }  },  "outputConfig": {    "predictionsFormat":"OUTPUT_FORMAT",    "DESTINATION":{      "OUTPUT_URI_FIELD_NAME": "OUTPUT_URI"    }  }}'

To send your request, choose one of these options:

curl

Save the request body in a file namedrequest.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs"

PowerShell

Save the request body in a file namedrequest.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{"name":  "projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/BATCH_JOB_ID",  "displayName": "JOB_NAME",  "model": "publishers/PUBLISHER/models/MODEL_ID",  "inputConfig": {    "instancesFormat": "jsonl",    "gcsSource": {      "uris": [        "INPUT_URI"      ]    }  },  "outputConfig": {    "predictionsFormat":"OUTPUT_FORMAT",    "DESTINATION":{      "OUTPUT_URI_FIELD_NAME": "OUTPUT_URI"    }  },  "state": "JOB_STATE_PENDING",  "createTime": "2024-10-16T19:33:59.153782Z",   "updateTime": "2024-10-16T19:33:59.153782Z",   "labels": {    "purpose": "testing"  },  "modelVersionId": "1"}

Get the status of a batch prediction job

Get the state of your batch prediction job to check whether it has completedsuccessfully. The job length depends on the number input items that yousubmitted.

REST

After youset up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

PROJECT_ID: Yourproject ID.
LOCATION: The region where your batch job is located.
JOB_ID: The batch job ID that was returned when you created the job.

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/JOB_ID

To send your request, choose one of these options:

curl

Execute the following command:

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/JOB_ID"

PowerShell

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/JOB_ID" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{"name":  "projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs/BATCH_JOB_ID",  "displayName": "JOB_NAME",  "model": "publishers/PUBLISHER/models/MODEL_ID",  "inputConfig": {    "instancesFormat":"bigquery",    "bigquerySource":{      "inputUri" : "INPUT_URI"    }  },  "outputConfig": {    "predictionsFormat":"OUTPUT_FORMAT",    "DESTINATION":{      "OUTPUT_URI_FIELD_NAME": "OUTPUT_URI"    }  },  "state": "JOB_STATE_SUCCEEDED",  "createTime": "2024-10-16T19:33:59.153782Z",   "updateTime": "2024-10-16T19:33:59.153782Z",   "labels": {    "purpose": "testing"  },  "modelVersionId": "1"}

Retrieve output

When a batch prediction job completes, retrieve the output from the locationthat you specified. For BigQuery, the output is in theresponse(JSON) column of your destination BigQuery table. ForCloud Storage, the output is saved as a JSONL file in the outputCloud Storage location.

What's next

Learn how toCall MaaS APIs for open models for streaming and non-streaming use cases.

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Last updated 2025-12-15 UTC.

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Batch predictions Stay organized with collections Save and categorize content based on your preferences.

Supported models

Prepare input

BigQuery

Cloud Storage

Request a batch prediction

BigQuery

REST

curl

PowerShell

Response

Cloud Storage

REST

curl

PowerShell

Response

Get the status of a batch prediction job

REST

curl

PowerShell

Response

Retrieve output

What's next

Batch predictions