Achieving Cost Efficiency on Google Cloud Platform (GCP)

Managing asset costs can be tough with your own servers, but Google Cloud Platform (GCP) makes it easier. They break down storage costs separately, which simplifies monitoring and lets you save money by making changes when needed.

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Discounts

Discounts on Google Cloud Platform (GCP) can help you save on your compute resource costs in various ways:

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Sustained Use Discounts:

When it comes to Compute Engine, each virtual CPU (vCPU) and each gigabyte (GB) of memory are billed separately rather than as part of a bundled machine type. With sustained use discounts, you automatically receive discounts for vCPUs and memory that are consistently utilized. If you use a specific vCPU or GB of memory for more than 25% of a month, you'll receive incremental discounts for every additional hour of usage. These discounts can add up, potentially resulting in up to a 30% net discount on the vCPU and memory costs for instances that run throughout the month.

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Resource-Based Committed Use Discounts (CUDs):

If you commit to using specific resources for a specified period, you can enjoy substantial discounts. When you commit to a 1-year term, you receive a 37% discount over the on-demand prices for the resources you choose. Opting for a 3-year commitment brings even greater savings, with a 70% discount for memory-optimized machine types and a 57% discount for all other machine types compared to on-demand prices.

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Spot Instances:

Spot instances provide significant cost savings, ranging from 60% to 91%, for virtual machines (VMs) and offer additional discounts on GPUs, local SSDs, and external IP addresses. It's important to note that while the discount rate may vary within this range, it consistently offers substantial savings. However, a key drawback is that these instances can be preempted or terminated at any time if the underlying resources are required for other purposes.

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Optimizing Storage and Big Query Costs:

Selecting the appropriate storage class is another critical factor that can significantly impact your storage costs. If your data isn't frequently accessed, transitioning to lower-cost options like nearline, coldline, or archival storage can reduce expenses while maintaining data resiliency. However, it's important to note that continuous access requests can lead to additional costs by violating the minimum storage duration period.

Optimizing BigQuery usage is crucial for cost control. Some best practices include setting query cost limits and enforcing them to prevent oversized queries, choosing batch loading over streaming inserts for data ingestion when real-time updates are unnecessary, and considering flat rate pricing for steady, high workloads. Google also offers flexible slots with short time commitments, allowing you to adjust resource usage as needed.?

Delete Irrelevant Assets

Clearing out obsolete snapshots is another cost-saving measure. While individual snapshots may not be costly, accumulating them can inflate operational costs. Identifying and removing old and unnecessary snapshots through automated scripts can help optimize expenses, or you can consider using cheaper storage options like coldline for older data.

Identifying and terminating zombie assets is another cost-saving strategy. These assets occur when VMs are stopped, but associated resources like IP addresses, load balancers, and disk storage remain active, incurring charges. You can automate asset termination when stopping VMs or use third-party tools to find and eliminate unused resources.

Tools

To effectively manage your cloud expenses, utilizing cost management tools is essential. These tools, such as CloudHealth, Google Cloud's operations suite, Apptio Cloudability, and CloudCheckr, provide greater control over resource costs. They support features like quotas, alarms, and rightsizing, automating tasks to save money and developer time.

Cost Optimization with Resource Scheduling

Implementing resource usage schedules is foundational to cost optimization. GCP offers instance schedulers for both virtual machines (VMs) and compute instances, enabling you to stop idle resources not in production use. This scheduling flexibility can significantly reduce costs, especially for testing and development purposes.