Standard PayGo

Related topics

• batch inference is an alternative to Standard PayGo From the official documentation: “For asynchronous jobs such as processing large datasets, batch processing is significantly cheaper (50%) than processing requests sequentially using Standard PayGo.”
• Priority PayGo is an alternative to Standard PayGo From the official documentation: “Priority pay-as-you-go (Priority PayGo) is a consumption option that provides more consistent performance than Standard PayGo without the upfront commitment of Provisioned Throughput.”
• Provisioned Throughput works with Standard PayGo From the official documentation: “By default, traffic that exceeds your Provisioned Throughput baseline (called *spillover traffic*) is handled by [Standard PayGo](/gemini-enterprise-agent-platform/models/standard-paygo).”
• Standard PayGo is part of Gemini Enterprise Agent Platform From the official documentation: “For the default pay-as-you-go model, Agent Platform uses [Standard pay-as-you-go (Standard PayGo)](/gemini-enterprise-agent-platform/models/standard-paygo).”
• BigQuery is an alternative to Cloud Storage From the official documentation: “The `import_result_sink` must be a Cloud Storage path or a BigQuery table.”
• BigQuery works with Cloud Storage From the official documentation: “External BigQuery data source backed by Cloud Storage”
• BigQuery works with Cloud Monitoring From the official documentation: “You can use Monitoring to export your metrics to BigQuery.”
• BigQuery is an alternative to Google Cloud Storage From the official documentation: “The training data can be either a CSV file in Cloud Storage or a table in BigQuery.”
• BigQuery works with Google Cloud Storage From the official documentation: “Load Cloud Storage data into BigQuery”
• BigQuery works with Gemini Enterprise Agent Platform From the official documentation: “The BigQuery dataset stores all versions for the associated Gemini Enterprise Agent Platform dataset.”
• batch inference works with BigQuery From the official documentation: “This page describes how to get batch inferences using BigQuery.”
• batch inference works with Cloud Storage From the official documentation: “Batch inference for Gemini models accepts one JSON Lines (JSONL) file stored in Cloud Storage as input data.”
• Cloud Storage works with Gemini Enterprise Agent Platform From the official documentation: “This page describes how to use Gemini Enterprise Agent Platform to export your image AutoML Edge models to Cloud Storage.”
• batch inference is part of Gemini Enterprise Agent Platform From the official documentation: “Gemini Enterprise Agent Platform provides two options for projecting future values using your trained forecast model: online inferences and batch inference.”
• batch inference works with Google Cloud Storage From the official documentation: “Batch text prediction with Gemini model using Google Cloud Storage”
• batch inference is an alternative to online inference From the official documentation: “Gemini Enterprise Agent Platform provides two options for projecting future values using your trained forecast model: online inferences and batch inference.”
• BigQuery depends on Cloud Storage From the official documentation: “BigQuery relies on Cloud Storage for staging file data and supports reading from and writing to several other sources and Google Cloud services.”
• Cloud Monitoring works with Gemini Enterprise Agent Platform From the official documentation: “Gemini Enterprise Agent Platform exports metrics to [Cloud Monitoring](/monitoring/docs).”
• Cloud Monitoring works with Google Cloud Storage From the official documentation: “You can configure Cloud Storage client libraries to to generate gRPC related metrics in Cloud Monitoring.”
• Cloud Monitoring works with Provisioned Throughput From the official documentation: “The following Cloud Monitoring metrics are available on the `aiplatform.googleapis.com/PublisherModel` resource for the Gemini models. Use the `dedicated` request types to filter for Provisioned Throughput usage.”
• Gemini Enterprise Agent Platform depends on Cloud Storage From the official documentation: “Certain tasks in Agent Platform require that you use additional Google Cloud products besides Agent Platform. For example, in most cases, you must use Cloud Storage and Artifact Registry when you create a custom training pipeline.”
• Gemini Enterprise Agent Platform works with Google Cloud Storage From the official documentation: “This page describes how to use Gemini Enterprise Agent Platform to export your AutoML tabular model to Cloud Storage, download the model to an on-premises server or a server hosted by another cloud provider, and then use Docker to make the model available for predictions.”
• online inference is part of Gemini Enterprise Agent Platform From the official documentation: “Gemini Enterprise Agent Platform provides two options for projecting future values using your trained forecast model: online inferences and batch inference.”
• Priority PayGo is an alternative to Provisioned Throughput From the official documentation: “Priority pay-as-you-go (Priority PayGo) is a consumption option that provides more consistent performance than Standard PayGo without the upfront commitment of Provisioned Throughput.”
• Provisioned Throughput works with Supervised fine-tuning From the official documentation: “Provisioned Throughput can be applied to both base models and supervised fine-tuned versions of those base models.”
• Supervised fine-tuning is part of Gemini Enterprise Agent Platform From the official documentation: “Agent Platform supports supervised fine-tuning to customize foundational models.”