Google Cloud Platform (GCP) offers a variety of pricing options for virtual machine instances, commonly known as servers. The pricing for servers on Google Cloud is based on several factors, including:

1. Machine Type: Google Cloud provides predefined machine types with different combinations of virtual CPUs (vCPUs) and memory. Each machine type has its own pricing per hour or per second, depending on the region and availability.

2. Instance Duration: The duration for which you run a virtual machine instance affects the pricing. You are billed per hour or per second for the duration of the instance usage, depending on the selected machine type.

3. Machine Type Customization: Google Cloud also allows you to create custom machine types with specific vCPU and memory configurations. Custom machine types are billed based on the resources allocated.

4. Storage: Persistent Disk storage is billed separately based on the type (standard HDD or SSD), capacity, and usage (provisioned capacity or actual usage).

5. Networking: Network egress traffic (outbound data transfer) from your virtual machine instance to the internet and other GCP regions or zones is subject to data transfer charges.

6. Preemptible Instances: Preemptible virtual machine instances are available at a significantly lower price but can be preempted with short notice. They are suitable for workloads that can tolerate interruptions.

It’s important to note that pricing details can change over time, and the actual cost may vary depending on factors such as the region, usage patterns, and any applicable discounts or committed use contracts. To get accurate and up-to-date pricing information for virtual machine instances on Google Cloud, it’s recommended to visit the official Google Cloud Pricing Calculator or consult the Google Cloud Platform Pricing documentation.

Additionally, Google Cloud offers various pricing programs and options to optimize costs, such as sustained use discounts for long-running instances, committed use contracts for reserved capacity, and preemptible VMs for cost-effective workloads.