Ankur Mandal

GCP Cost Management For Efficient Cloud Infrastructure


Ankur Mandal

5 min read

Google Cloud Platform (GCP) offers a wide range of robust services designed to support business growth and enhance productivity and efficiency. However, as businesses increasingly rely on GCP for data storage, concerns about rising costs and potential financial strain exist.

Understanding GCP's cost structure can be challenging without the right strategies in place. This detailed guide delves into the factors affecting GCP cost, different cost management techniques, and tools available within GCP to help you optimize and control your cloud spending effectively.

Google Cloud Platform (GCP) stands out as a game-changer for businesses of all sizes, thanks to its expansive suite of services and robust infrastructure. Yet, amidst the myriad benefits of cloud computing lies the critical challenge of cost management. Without careful planning and constant oversight, expenditures can quickly escalate, leading to budgetary constraints and financial instability. This calls for effective GCP cost management techniques to be put in place.

Before delving into the various techniques for managing GCP cost, it is important to understand how GCP pricing works and the factors that drive GCP cost. 

GCP Pricing Model

To help you optimize your cloud infrastructure costs, GCP offers a range of pricing modes tailored to different needs. Some are designed to accommodate micro workloads launched on demand, while others are better suited for sustaining long-term production workloads.

  • Free trial: The Free Trial option on GCP is available to new users. It provides a $300 billing credit that remains valid for 90 days. The credit can be used on various GCP resources based on individual requirements. This option is ideal for organizations preparing for cloud migration and students and individuals looking to explore GCP and improve their skills.
  • Free Tier: Unlike the Free Trial, the Free Tier offers complimentary access to GCP resources up to a specified limit. This encompasses over 25 GCP services, including one non-preemptible e2-micro VM instance per month. While the Free Tier serves well for educational purposes, its limitations may make it unsuitable for production environments.
  • On-Demand: The On-Demand mode is the default choice and is widely favored among leading public cloud providers. Users can swiftly launch instances as needed and are billed based on the precise resources consumed over time. This flexible approach operates on a pay-as-you-go (PAYG) basis, requiring no upfront payments or long-term commitments.
  • Spot VMs: Spot VMs leverage unused GCP resources that would otherwise go to waste. These instances are initiated whenever surplus resources become available and are paused, stopped, or terminated when the cloud requires those resources elsewhere. While Spot VMs offer substantial cost savings compared to On-Demand pricing, they are not ideal for workloads requiring strict adherence to service-level agreements (SLAs).
  • Committed Use Discounts: Committed use discounts offer a compelling avenue for cost savings when running workloads over the long term. By committing to a specific usage, measured in $/hour, for at least one year, organizations can enjoy competitive pricing compared to the On-Demand mode. This option is well-suited for production environments with predefined resource requirements, although it's currently limited to just five GCP products.
  • Sole-Tenant Nodes: Sole-tenant nodes present another opportunity for organizations to optimize costs while running long-term workloads on GCP. With Sole-tenant nodes, organizations secure permanent allocation of physical machines, ensuring all resources are exclusively available to them. This option shines in scenarios where access is necessary for compliance or licensing purposes.

Factors Affecting GCP Cost

Let us now discuss different factors on which the GCP cloud cost depends.

Compute: Compute costs in the cloud are determined by the processing power required to run applications. Pricing is influenced by factors such as the selected virtual machine instance type, deployment region, and operating system. Businesses can choose from various instance types with specific specifications and pricing, allowing them to customize their selections to align with their needs.

Storage: Storage costs are a crucial element of cloud pricing models, encompassing fees for storing data in cloud environments. Like computational costs, storage expenses vary depending on the amount and type of storage utilized and the data storage location. 

Cloud services offer a range of storage options with different performance characteristics and pricing structures. For example, block storage is designed for tasks requiring low latency and high IOPS, such as database management and high-throughput applications. On the other hand, object storage is ideal for storing unstructured data like images, videos, and documents.

A study done by Virtana, "State of Hybrid Cloud Storage in January 2023", on over 350 cloud decision-making led to the following discovery:

  • 94% of the respondents said that their cloud storage cost was increasing
  • 54% said the storage cost grew faster than the overall cloud cost bill.

The data above emphasizes the need for cloud cost management tools for storage usage and waste.

Database Pricing: Database pricing significantly shapes cloud pricing models, especially in managed database services. The cost is influenced by factors such as the type of database service utilized (e.g., relational, NoSQL, or in-memory), the capacity and performance of the database instance, and the geographical location of deployment.

Data Transfer Charges: Data transfer costs are often overlooked but play a significant role in determining cloud pricing. These expenses cover the costs of data entering and leaving cloud environments. The pricing of data transfer is determined by the amount of data being transferred, and the destination of the data transfer.

GCP Cost Management Techniques

Having covered all the basics of GCP cost, let us move on to different GCP cost management techniques that you can use to make your business cost-efficient without compromising performance. 

1. Use Pricing Calculator

The GCP Pricing Calculator offers robust tools for generating detailed cost estimates, empowering businesses with strategic planning capabilities to optimize cloud utilization. Leveraging resources like the GCP Pricing Calculator enables organizations to forecast expenses accurately for essential services. Moreover, it aids in enhancing operational efficiency by identifying and rectifying underutilized instances. This proactive approach enables businesses to maximize the value of their cloud investment while ensuring cost-effectiveness.

2. Use Preemptible VMs For Non-Critical Workloads

Google Cloud provides preemptible VMs and temporary and budget-friendly virtual machines that are ideal for running workloads and can handle interruptions. These instances are significantly cheaper than regular VMs, making them a great choice for businesses aiming to reduce their computing costs.

Preemptible VMs excel in handling batch processing tasks, video encoding, rendering, and similar non-critical workloads designed with fault tolerance in mind. Leveraging preemptible VMs allows businesses to capitalize on Google Cloud's surplus capacity while reducing overall compute expenditures. Moreover, these instances are well-suited for executing processing-intensive tasks like machine learning training jobs. That’s because, it enables organizations to harness substantial processing power without incurring the full expense of regular VMs.

How To Use Preemptible VMs?

  • Selection: When setting up a new instance, choose the "Preemptible" option to utilize preemptible VMs seamlessly.
  • Automated Scalability: Employ managed instances and groups to automate the scaling of preemptible instances in alignment with demand fluctuations.
  • Time Constraint Awareness: Preemptible instances are capped at a maximum lifespan of 24 hours. Hence, ensure that workloads are structured to accommodate interruptions effectively.

3. Monitor Spending With Budget Alerts

Implementing budget alerts is critical in GCP cloud cost management. It involves setting a budget for cloud usage and setting up notifications for when spending approaches or exceeds the set budget limit. This proactive measure helps prevent unexpected charges, enabling effective monitoring of cloud expenses.

To set up budget alerts, follow the steps mentioned below.

  • Setup Options: Choose from the GCP Console, Cloud SDK, or the Cloud Billing API to establish budget alerts according to your preference and workflow.
  • Granular Budgeting: Set budgets for individual billing accounts, projects, or billing subaccounts to monitor expenditures accurately at various organizational levels.
  • Customized Monitoring: Create multiple budgets to monitor different spending categories or projects within your infrastructure.
  • Automated Notifications: When configuring your budget, GCP automatically sends email notifications when spending approaches or surpasses the thresholds you defined.
  • Threshold Options: Define thresholds based on your preferences, such as reaching a percentage of the budget, exceeding a specific amount, or encountering a notable surge in spending.

4. Get Detailed Cost Analysis Reports With Cost Breakdown Reports

Utilizing cost breakdown reports provides valuable insights into the specific costs linked to individual services and resources in your cloud infrastructure. By analyzing usage patterns over time, these reports support informed decision-making on resource allocation and identify areas for cost-efficiency improvements.

Cost breakdown reports go beyond identifying underutilized resources. They are essential tools for monitoring trends and forecasting future expenses. By analyzing these reports, businesses can have insight into changing spending patterns and predict future costs more accurately.

How To Use Cost Breakdown Reports?

The steps below will help make the most of cost breakdown reports.

  • Enable Billing Export: Initiate the process by enabling billing export to Google Cloud Storage within your GCP account settings.
  • Automated Export: Once activated, GCP automatically exports billing data to a specified bucket within Google Cloud Storage infrastructure.
  • Data Accessibility: Utilize tools such as BigQuery or Data Studio to access and analyze the exported billing reports stored in Google Cloud Storage.
  • Query Capabilities: Leverage BigQuery's querying capabilities to extract granular insights from your billing data and enable detailed analysis.
  • Visualization: Utilize Data Studio to create visually compelling reports and dashboards, facilitating intuitive interpretation of your billing data.

5. Right size Compute Engine To Allocate Optimal Resources

Optimizing resource allocation through right-sizing involves aligning an application's resource requirements with its allocated resources. This entails avoiding both over- and under-provisioning, which can lead to unnecessary costs or performance degradation. By monitoring resource usage and adjusting accordingly, right-sizing ensures efficient resource utilization.

Implementing the right sizing allows for streamlining resource usage, ensuring that businesses only pay for necessary resources. GCP provides tools like Instance Right Sizing recommendations within Compute Engine, which analyze VM usage and propose more suitable machine types for improved performance and cost-effectiveness.

6. Identify idle/unused and overprovisioned resources

Idle or unused resources in the cloud refer to computing instances, storage volumes, networking components, or other cloud services that are provisioned but not actively utilized by applications or users. These resources may remain idle for various reasons, such as over-provisioning, temporary workload fluctuations, or changes in application demand.

 The cost-related impacts of idle or unused resources in the cloud include:

  • Wasted Spending: Idle resources consume computing resources and storage capacity without delivering value to the organization. This results in wasted spending, as businesses continue to incur costs for resources that are not actively utilized.
  • Underutilized Capacity: Idle resources in the cloud environment signify an underutilization of capacity, preventing computing resources from being allocated to critical workloads or applications.
  • Increased Total Cost of Ownership (TCO): Idle resources lead to a hike in cloud infrastructure's total cost of ownership (TCO). Organizations end up paying for unproductive resources, ultimately inflating overall operational costs.

While there is no shortage of GCP cost optimization tools that can help identify idle/unused and overprovisioned computing resources, organizations overlook the optimization of storage resources. 

While rightsizing can be an effective instrument in ensuring optimal resource allocation, the leaders in this industry provide this service for computing resources and overlook storage resources.

As mentioned above, storage costs are increasing rapidly and must be monitored. We also conducted an independent study to understand the impact of storage resources on the overall cloud bill in a better way and found that.

  • Block Storage, aka GCP's persistent disk, significantly contributed to the overall cloud costs. 
  • The disk utilization for root volumes, applications disks, and self-hosted databases is significantly low.
  • The organization was overestimating the growth and hence overprovisioning storage resources.
  • Despite overprovisioning, there was at least one downtime per quarter. 

After conducting further investigation, we also discovered that enhancing the buffer to ensure the system remains responsive and operates optimally during periods of heightened or unpredictable demand necessitates the following steps:

  • Implementing three manual touchpoints, including monitoring, development, and alerting, requires the DevOps team to navigate through three different tools to manage block storage manually, leading to a significant investment of their time.
  • Allowing a minimum downtime of 4 hours to shrink 1 TB of disk space for specific cloud providers, with a 3-hour downtime required for disk upgrades.
  • Anticipating a wait time of at least 6 hours for the next scaling process.

Despite organizations' challenges, they prioritize overprovisioning storage resources rather than optimizing storage. This decision is often seen as a necessary compromise due to Cloud Service Providers (CSPs) limitations.

  • Due to the CSP limitations, the need for a custom tool arises, but creating such a tool can be a complex and time-consuming task that requires significant DevOps efforts. 
  • Relying solely on CSP tools can result in inefficient and resource-intensive processes, making ongoing storage optimization impractical for day-to-day operations.
  • Cloud service providers like AWS, Azure, and GCP lack a live shrinkage process. While it is possible to achieve shrinkage manually, it is labor-intensive and prone to errors and misconfigurations. Moreover, this manual process requires stopping the instance, taking snapshots, and mounting new volumes, leading to downtime.
  • It would require implementing a tool across the entire cloud infrastructure, which has more than 1000s of instances running. This can be costly, so organizations only implement tools in the production environment, leading to limited visibility.

The reasons above compel the organization to overprovision the storage resources instead of optimizing them. However, overprovisioned signals resource inefficiency and leads to increased cloud bills. The escalating cloud bill is because cloud service providers charge you based on provisioned resources, regardless of whether you use them. In case of overprovision, you will end up paying for the resources you are not using. 

This necessitates implementing cloud cost automation to identify idle/unused and overprovisioned resources. 

Why automation?

Manual discovery or reliance on monitoring tools can pose challenges due to the labor-intensive efforts of DevOps teams or the added expenses associated with deployment. As storage environments grow increasingly complex, managing them manually can lead to spiraling complexities and potential inefficiencies.

This is where Lucidity Storage Audit comes into the picture.

Lucidity Storage Audit revolutionizes the management of your digital infrastructure. It automates auditing by leveraging a user-friendly executable tool, eliminating complexities and streamlining operations. Easily gain deep insights into your persistent disk health and utilization, empowering you to optimize expenditures and proactively mitigate downtime risks.

Powered by the cloud service provider's internal services, Lucidity Storage Audit securely collects storage metadata, including storage utilization percentages and persistent disk sizes, ensuring comprehensive oversight without compromising customer privacy or sensitive data. Rest assured, Lucidity Storage Audit operates seamlessly within your cloud environment, safeguarding resources and preserving operational continuity.

With just a few clicks, Lucidity provides the following information:

  • Overall disk spend: Analyze persistent disk expenditures to determine the optimal billing scenario, aiming for a 70% cost reduction. Identify areas for optimization and implement strategies to achieve substantial savings.
  • Disk wastage: Identify the underlying causes of waste, such as idle volumes and over-provisioning, and devise strategies to mitigate them effectively
  • Disk Downtime Risks: Prevent potential downtimes, minimizing financial losses and reputational damage.

Lucidity Storage Audit offers the following benefits.

  • Streamlined Process: Lucidity Storage Audit automates tasks, eliminating manual efforts and complex monitoring tools, streamlining the auditing process.
  • Comprehensive Insights: Gain a thorough understanding of persistent disk health and utilization. Lucidity Storage Audit provides valuable insights for optimizing spending and preventing downtime, offering clear visibility into your storage environment.
  • Optimized Utilization: Analyze storage utilization percentages and disk sizes with Lucidity Storage Audit for informed decision-making, improving resource allocation, and maximizing efficiency.

7. Auto-Scale Resources

Auto-scaling is one of the most effective  GCP cost optimization techniques. It refers to automatically adjusting resources based on current workload demands. This capability allows cloud services to dynamically scale resources up or down in response to fluctuations in demand without manual intervention. 

Why to automate the scaling process?

Traditional methods of scaling storage resources often result in overprovisioning, wasting valuable resources, or underprovisioning, leading to performance bottlenecks.

This is where Lucidity's Block Storage Auto-Scaler can help reduce the hidden cloud costs associated with storage wastage. The industry's first storage autonomous orchestration solution, Lucidity Block Storage Auto-Scaler, shrinks and expands the block storage according to changing requirements. Block Storage Auto-Scaler has the following features.

  • Effortless Deployment: Onboard the Lucidity Block Storage Auto-Scaler with just three clicks, and your storage management process will be revolutionized.
  • Storage Optimization: Instantly increase storage capacity and guarantee an optimal 70-80% utilization rate. This high-efficiency level will lead to substantial cost savings, making your storage management process more economical.
  • Highly Responsive: React promptly to sudden traffic or workload spikes with the Block Storage Auto-Scaler, which expands or shrinks storage capacity within minutes. Ensure seamless operations during surges and efficiently handle demand fluctuations.
  • Efficient Performance: Experience minimal impact on instance resources with the highly optimized Lucidity agent, consuming less than 2% CPU and RAM usage.

Lucidity Block Storage Auto-Scaler offers the following benefits.

benefits of using Lucidity for block storage management
  • Experience automated expansion and shrinkage: It effortlessly adjusts storage resources within a concise 90-second timeframe. Simplify the management of large data volumes with ease. Overcome the limitations of traditional block storage volumes, which max out at around 8GB per minute (equivalent to 125MB/sec) with Standard block storage. Our Auto-Scaler maintains a robust buffer to handle sudden data surges smoothly without exceeding block storage throughput limits.
  • Achieve up to 70% Reduction in Storage Costs: Maximizes savings with Lucidity Block Storage Auto-Scaler, reducing storage expenses by eliminating overprovisioning risks.
  • Estimate Your Savings with the ROI Calculator: Use our ROI Calculator to get personalized estimates. Select a preferred cloud provider (Azure or AWS) and input monthly or annual spending, disk utilization, and growth rate details.
Lucidity ROI calculator to find out how much you can save in your cloud spend
  • Zero Downtime: Lucidity Block Storage Auto-Scaler ensures swift adjustment to changing storage needs, eliminating downtime. Use the "Create Policy" feature to tailor policies to specific scenarios, seamlessly expanding storage resources based on defined policies.
Lucidity custom policy feature to ensure zero downtime

Build A Cost-Efficient GCP Infrastructure

We hope our blog has given you sufficient information to ensure that your Google Cloud is optimized without sacrificing performance. If you struggle with escalated cloud costs but can not pinpoint the reason, your storage usage is a strong possibility. Reach out to Lucidity for a demo, and we will show you how automation can prove instrumental in lowering storage costs and creating a cost-efficient cloud infrastructure.

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