reservation

Related topics

• reservation works with Vertex AI From the official documentation: “You can modify the reservation to allow Vertex AI training or prediction jobs to use it.”
• labels configures reservation From the official documentation: “When you add a label to a reservation, it is included in your billing data.”
• Compute Engine works with reservation From the official documentation: “After you attach your reservations, Compute Engine automatically populates the fields in the **Commitment details** section to match the numbers and types of resources from your attached reservations.”
• Google Kubernetes Engine works with reservation From the official documentation: “In the **Google Cloud Services** section, specify whether to share this reservation with other services like Google Kubernetes Engine or Vertex AI.”
• GPU works with reservation From the official documentation: “For all GPUs and most Local SSD disks, the total numbers and types must match between your commitment and attached reservations.”
• reservation is part of Compute Engine From the official documentation: “If Google Cloud approves your request, then Compute Engine creates reservations with your requested resources at your specified date and time.”
• reservation works with REST From the official documentation: “You can create your attached reservations while purchasing your commitment by using the Google Cloud console, the gcloud CLI, or REST.”
• 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: “Sample to load Avro data from Cloud Storage into a new BigQuery table”
• BigQuery works with Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• Compute Engine works with service account From the official documentation: “A common method is to attach a service account to a resource like a Compute Engine instance.”
• BigQuery works with Cloud Run functions From the official documentation: “Learn how to write an HTTP Cloud Run function that submits a query to BigQuery.”
• Cloud Run depends on service account From the official documentation: “The check ensures that the service account that runs the container has permission to access these secrets.”
• service account works with Vertex AI From the official documentation: “The service account can interact with Vertex AI services.”
• Cloud Logging works with Cloud Storage From the official documentation: “Cloud Storage writes various log entries to Cloud Logging in the Google Cloud console.”
• Cloud Run functions works with Cloud Storage From the official documentation: “(GCF translates GCS notifications to HTTP requests internally)”
• Cloud Storage works with Compute Engine From the official documentation: “For example, you can create a service account that your Compute Engine instances use to access objects stored in Cloud Storage buckets.”
• BigQuery works with Vertex AI From the official documentation: “The page provides guidance for getting predictions from Vertex AI, based on retail [data exported into BigQuery](/retail/docs/export-data-into-bq).”
• Cloud Logging works with Compute Engine From the official documentation: “Compute Engine sends you Cloud Logging events for scheduled, started, or completed maintenance.”
• Cloud Run works with Cloud Storage From the official documentation: “Receive events from Cloud Storage in a Cloud Run service”
• Cloud Run works with Google Kubernetes Engine From the official documentation: “Connecting Cloud Run services to Cloud Service Mesh lets you securely route traffic to workloads on GKE.”
• Compute Engine is an alternative to Google Kubernetes Engine From the official documentation: “You can install MongoDB on a Compute Engine instance or you can deploy MongoDB on Google Kubernetes Engine.”
• Vertex AI depends on service account From the official documentation: “To use the Google Vertex AI model endpoints, you must add Vertex AI permissions to the service account that you used while installing AlloyDB Omni.”
• BigQuery depends on Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• Cloud Logging works with Cloud Run functions From the official documentation: “Cloud Run functions is configured to use Cloud Logging automatically.”
• Cloud Logging is an alternative to Cloud Storage From the official documentation: “Instead, they are routed to either Cloud Storage (recommended) or Cloud Logging, depending on your organization's configuration.”
• Cloud Logging works with Google Kubernetes Engine From the official documentation: “By default, GKE clusters send system log data and system metric data to Logging and Monitoring.”
• Cloud Run functions depends on service account From the official documentation: “The Cloud Run function needs a service account with permissions to write to GCS bucket and be invoked by Pub/Sub.”
• Cloud Storage works with service account From the official documentation: “The service account can manage objects in the Cloud Storage bucket.”
• Google Kubernetes Engine depends on BigQuery From the official documentation: “This happens because Google Kubernetes Engine depends on BigQuery and Managed Service for Apache Airflow inherits this dependency from the Kubernetes Engine API.”
• labels is part of BigQuery From the official documentation: “BigQuery supports adding labels to resources.”
• BigQuery works with Cloud Run From the official documentation: “You can then use Cloud Run to transcode data present in the bucket and move it to BigQuery.”
• 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.”
• BigQuery works with Compute Engine From the official documentation: “Compute Engine integrates with Google Cloud technologies such as Cloud Storage, Google Kubernetes Engine, and BigQuery, to extend beyond the basic computational capability to create more complex and sophisticated applications.”
• BigQuery works with labels From the official documentation: “Labels are exported to BigQuery with the corresponding Google Cloud resources and their usage.”
• BigQuery works with service account From the official documentation: “The service account can be used by services, such as Cloud Run, to read and modify data in the BigQuery dataset.”
• Cloud Logging works with Cloud Run From the official documentation: “Cloud Logging captures, stores, and analyzes Cloud Run logs.”
• Cloud Logging works with labels From the official documentation: “[Labels](/resource-manager/docs/creating-managing-labels) can track your resource spend in Cloud Billing, and provide extra insights in Cloud Logging.”
• Cloud Run is an alternative to Cloud Run functions From the official documentation: “The blueprint supports the following use cases: - Deploying a serverless architecture using Cloud Run functions (this document) - [Deploying a serverless architecture using Cloud Run](/architecture/blueprints/serverless-blueprint)”
• Cloud Run works with Compute Engine From the official documentation: “This lets Cloud Run clients use this backend service route to send requests directly to Compute Engine instances.”
• Cloud Run is an alternative to Google Kubernetes Engine From the official documentation: “**Agent and tool runtimes**: hosting and executing agents or MCP servers on runtimes such as Agent Runtime on Gemini Enterprise Agent Platform, Cloud Run, or Google Kubernetes Engine (GKE) incurs standard compute and runtime charges for those services.”
• Cloud Run works with service account From the official documentation: “Map a custom Service Account to a Cloud Run service”
• Cloud Run works with Vertex AI From the official documentation: “The Cloud Run service can interact with Vertex AI services.”
• Cloud Run functions is part of Cloud Run From the official documentation: “| Cloud Run functions | Standalone functions on Cloud Run. |”
• Cloud Run functions depends on Cloud Storage From the official documentation: “Cloud Run functions uses the config in Cloud Storage to determine what actions to take for each stateful MIG.”
• Cloud Run functions is an alternative to Compute Engine From the official documentation: “Cloud Run functions and App Engine don't offer a two-way configurable firewall, so you must either create compensating controls or deploy the tokenization service on Compute Engine or Google Kubernetes Engine.”
• Cloud Run functions is an alternative to Google Kubernetes Engine From the official documentation: “Cloud Run functions and App Engine don't offer a two-way configurable firewall, so you must either create compensating controls or deploy the tokenization service on Compute Engine or Google Kubernetes Engine.”
• Cloud Run functions works with Vertex AI From the official documentation: “- **Vertex AI custom model** node: Use Cloud Run functions to post-process prediction results from the original Vertex AI custom model.”
• Cloud Storage works with Google Kubernetes Engine From the official documentation: “Often, media-related data residing in Cloud Storage is processed by applications running in Compute Engine or Google Kubernetes Engine, and the output of this process is written back to Cloud Storage.”
• Cloud Storage works with Vertex AI From the official documentation: “Access Cloud Storage content through your current project's Vertex AI service agent.”
• Compute Engine works with Google Kubernetes Engine From the official documentation: “Compute Engine integrates with Google Cloud technologies such as Cloud Storage, Google Kubernetes Engine, and BigQuery, to extend beyond the basic computational capability to create more complex and sophisticated applications.”
• Compute Engine depends on service account From the official documentation: “The Service Account is used by Compute Engine to access other Google APIs.”
• Google Kubernetes Engine is an alternative to Vertex AI From the official documentation: “| **Small ML Models** | Requires specific hardware (e.g., GPU). | Vertex AI / Google Kubernetes Engine (GKE) | Specialized hardware; more complex setup. |”
• GPU is part of Cloud Run From the official documentation: “The GPU feature offers on-demand availability with no reservations needed, similar to the way on-demand [CPU](/run/docs/configuring/jobs/cpu) and on-demand [memory](/run/docs/configuring/jobs/memory-limits) work in Cloud Run.”
• GPU is part of Compute Engine From the official documentation: “Compute Engine offers the option of adding one or more GPUs to your virtual machine instances.”
• labels configures Compute Engine From the official documentation: “Labels that are applied to the workstation and propagated to the underlying Compute Engine resources.”
• service account depends on BigQuery From the official documentation: “Each feature view uses a service account to access the source data in BigQuery during sync.”
• service account depends on Cloud Run From the official documentation: “The service account that you created in the previous step needs permission to use Cloud Run so that the BigQuery remote function can use the Cloud Run functions function.”