What is GCP Cost Optimization?

Google Cloud Platform (GCP) cost optimization is a strategic approach designed to manage and minimize the costs associated with using GCP's suite of cloud services. It involves the efficient allocation of resources, proper budgeting, and understanding the pricing models of various GCP services.

• Cost optimization is crucial as it helps aligns your cloud spend with business value.
• As organizations increasingly scale their operations and data processing needs, cost management becomes critical.

This approach includes using tools and best practices that allow businesses to leverage the robustness and flexibility of GCP services without incurring unnecessary costs. The focus is on maximizing the value from each dollar spent on the cloud.

Getting started

This guide is designed to equip you with the necessary knowledge related to controlling GCP costs. We have compiled a list of industry best practices, leading cloud cost management tools, and how to pick the one that’s ideal for you.

The importance of Cloud Cost Optimization

To truly grasp the profound significance of Google Cloud Platform (GCP) cost optimization, let's delve into an actual case study. A thriving company heavily relied on cloud resources for data processing, content delivery, and various other operations.

Initially, they had a manageable cloud budget of $250,000 per month. However, as their business grew and customer demands escalated, their GCP usage grew exponentially. As a result, within a span of six months, their monthly cloud costs soared to a staggering $500,000.

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Understanding the gravity of the situation, they sought the expertise of Economize. Our detailed examination of their cloud usage revealed some eye-opening facts.

• An astonishing 35% of their monthly expenditure was traced back to cloud waste. This waste was majorly constituted of unused or underutilized instances. Their in-house GCP tools failed to provide an overall visibility into the connected costs and couldn't pinpoint the main cost drivers.
• The analysis also revealed a lack of accountability in their expenditure. The engineering teams were rapidly spinning up resources during production without diligently shutting them off or decommissioning them post-use. This led to a drastic escalation in costs.

We laid out a comprehensive FinOps roadmap for them, starting with gaining a holistic visibility into their cloud spending. Ingesting their billing data, we established benchmarks and compared them to the current usage and expenditure, leading to a gap analysis.

This in-depth analysis highlighted the necessary steps to bridge this gap and move towards their ideal state of performance and cost efficiency.

• After meticulously following the prescribed best practices and strategic steps, a remarkable transformation was observed. Within four months, the company was able to pare down their monthly costs to $350,000, resulting in impressive savings of $150,000 each month. These savings could then be channeled into other essential objectives, bolstering the company's financial health.

This case study underscores the critical importance of GCP cost optimization. It not only facilitates substantial cost savings but also enables a financially sustainable and efficient cloud environment.

How Much Does GCP Cost?

To effectively manage GCP costs, it's important to first understand how GCP pricing works. Pricing is based on a consumption-based model, where businesses pay for the resources they use. The pricing structure can be complex, and it's essential to have a clear understanding of the key components to manage costs effectively.

The three primary components of GCP pricing are storage, network, and data.

• Storage: Charges are applied based on the quantity of data stored across various GCP services. The details for each storage class can be found here.
• Network: Pricing depends on the volume of data transferred over the network. This includes both data moving in and out of GCP services.
• Data: Specific GCP services, such as Compute Engine, Cloud SQL, or BigQuery, have their own pricing structures based on the data they handle.

By understanding these key components, you can identify areas where they may be overspending and optimize usage.

Google Cloud Pricing Models

GCP offers three key pricing models to accommodate varying user requirements: Free Tier, On-Demand, and Long-Term.

GCP Free Tier

The Free Tier allows users to explore GCP services without any upfront cost. It offers limited monthly usage for specific GCP services like Compute Engine, Cloud Storage, and others. Here are some key free tier limits:

• Compute Engine: 1 non-preemptible e2-micro VM instance per month
• Cloud Storage: 5 GB-months of regional storage per month
• Google Kubernetes Engine: No management fee for one Autopilot or Zonal cluster per billing account
• Firestore: 1 GB storage per project
• Cloud Logging: Free monthly logging allotment
• BigQuery: 1 TB of querying and 10 GB of storage per month
• App Engine: 28 hours per day of "F" instances

Please note, exceeding these limits will incur charges per GCP's standard rates.

On-Demand Pricing

This flexible model lets users pay for resources as and when they use them. Users can scale up or down depending on their needs and only pay for the services consumed. This option is ideal for organizations with unpredictable workloads or who need to quickly adapt their resources.

For the General-Purpose Machine Types (C3):

• Predefined vCPUs: $24.80686 / vCPU month
• Predefined Memory: $3.32515 / GB month
• Spot Pricing for vCPUs: $2.25278 / vCPU month
• Spot Pricing for Memory: $0.30149 / GB month

For the C3 Standard Machine Types:

• c3-standard-4: $0.208808 per hour
• c3-standard-8: $0.417616 per hour
• c3-standard-22: $1.148444 per hour
• c3-standard-44: $2.296888 per hour

Long-Term Pricing

Also known as Committed Use (CUD) and Sustained Use Discounts (SUD), Long-Term Pricing is best suited for users with consistent, long-term resource needs. Users agree to use specific resources for a fixed period (e.g., one or three years) in return for reduced rates.

Committed Use Discounts (CUDs)

Committed Use Discounts (CUDs) allow you to commit to a set amount of resources for a specific period of time, in return for a discounted rate.

For example, if you need to run a virtual machine for a year, you can commit to a certain number of CPU and memory resources, and receive a discount for doing so. Discounts range from 20% to 77% for 1 and 3 year commitments respectively.

License pricing with CUDs:

• SLES: 1-2 vCPUs at 77% (1-year) and 79% (3-year) CUD
• SLES for SAP: Any number of vCPUs at 59% (1-year) and 63% (3-year) CUD
• RHEL and RHEL for SAP: Any number of vCPUs at 20% (1-year) and 24% (3-year) CUD

Sustained Use Discounts (SUD)

Sustained Use Discounts (SUD) on the other hand, are automatically applied when you use a virtual machine for a certain amount of time each day, over a 30-day period.

The longer you use the machine, the more you save, with discounts ranging from 10% to 90%. This pricing model is ideal for businesses that have predictable resource requirements and need to run virtual machines for extended periods of time.

• n1-standard-1 VM: 0%–25% of the month at $0.0475/hour, 25%–50% at $0.0380/hour, 50%–75% at $0.0285/hour, and 75%–100% at $0.0190/hour
• c2-standard-4 VM: 0%–25% of the month at $0.2088/hour, 25%–50% at $0.1811/hour, 50%–75% at $0.1530/hour, and 75%–100% at $0.1252/hour

Understanding these pricing components and models allows businesses to spot areas of potential overspending and optimize their GCP usage

How do I calculate GCP costs?

Forecasting GCP costs is a critical aspect of budgeting and resource planning. We've compiled a variety of resources that can assist you in understanding and predicting your GCP expenditure.

• GCP Pricing Catalog
  ‍This comprehensive catalog allows you to explore and compare the costs associated with different Google Cloud services. It presents complex pricing information in a simple, accessible manner, enabling you to quickly determine the cost of the services you intend to use.
• GCP Pricing Calculator
  ‍This tool is designed to provide a quick estimate of your expected GCP expenditure. Just input your instance type, usage, time duration, and discount type to receive a tailored cost prediction.
• GCP Regions and Zones
  ‍Google Cloud Platform (GCP) has extensive global coverage, supporting 24 regions, 73 zones, and 17 countries. This resource provides a detailed map and list of all Google Cloud Platform regions and zones, enabling you to choose the closest and most suitable region for your operations.
• GCP SKU List
  ‍This extensive inventory lists all Google Cloud services, resource groups, and SKUs along with their corresponding pricing details. It's a valuable resource to understand the scope of what GCP offers and how each service is priced.

To effectively forecast your GCP costs, begin by identifying the most appropriate GCP regions and zones for your needs. Next, delve into our comprehensive pricing catalog and SKU list to find out the services you'll be using.

Finally, use the GCP pricing calculator to estimate your costs based on your chosen instance type, usage, time duration, and discount type. This will provide you with an accurate forecast of your expected GCP expenditure.

Introducing GCP Cost Optimization Tools

The process of pricing management and cost optimization is time-consuming and labor-intensive, even for experienced FinOps professionals. It can take several months to understand and adapt to a new cloud environment - a delay which might result in significant lost savings.

In response to this challenge, GCP Cost Optimization Tools have emerged as effective solutions, empowering organizations to manage their cloud costs with more efficiency and accuracy.

The Value of GCP Cost Optimization Tools

Cloud cost optimization tools are engineered by FinOps experts to process your billing and usage data, presenting it through comprehensible visualizations and intuitive dashboards. These tools offer an array of benefits:

1. Visibility and Insight: The tools provide granular insights into your cloud expenditure patterns, enabling better understanding and control over your spending.
2. Benchmarks and Best Practices: By analyzing your data, these tools help establish meaningful benchmarks, fostering cost-effective and goal-oriented decisions.
3. Automation: These tools automate routine tasks, freeing your teams to focus on strategic objectives, and build sustainable financial models.
4. Cost Control: They combat the primary cost drivers in GCP expenditure - idle resources, misconfigurations, hidden costs, and missed opportunities:
5. Idle Resources: Also known as "Zombie" instances, these are underutilized or idle resources that drain finances without providing commensurate value.
6. Misconfigurations: Improper configurations of instances and storage often lead to inflated costs, presenting a substantial challenge to many organizations.
7. Hidden Costs: Unexpected costs like data transfers, licenses, network egress, and operations can accumulate unnoticed, thereby inflating the total expenditure.
8. Missed Opportunities: The inability to identify and utilize cost-saving opportunities could result in missing out on potential savings.

Taking the complexity out of cloud management, GCP Cost Optimization Tools offer organizations a streamlined and effective way to manage their cloud finances.

Instead of investing time and resources into developing an in-house cloud management team, these tools present an economical and efficient alternative.

Types of Cost Optimization Tools

Different organizations have varying requirements when it comes to cloud cost management, depending on their size, the complexity of their cloud environments, and the level of granularity and control desired. Recognizing this, a variety of tools have been developed to address diverse needs.

Native GCP Cost Optimization Tools

Google Cloud Platform itself offers several tools that can help with cost management. These tools are integrated into the GCP console and provide insights specific to GCP services. Here are some noteworthy ones:

• Google Cloud Billing Reports: This feature provides comprehensive charts and tables that summarize your GCP usage costs.
• Google Cloud Pricing Calculator: This tool helps you to estimate the cost of your intended GCP usage.
• Google Cloud Cost Table: It shows a comprehensive list of all GCP products along with their respective pricing.

Leading Third-Party Tools

Third-party tools have emerged in the competitive market to offer multi-cloud and hybrid-cloud cost management solutions with user-friendly interfaces.

• These tools not only provide cost optimization solutions for GCP, but also extend their services across other cloud platforms, including AWS and Azure.
• A curated list of leading third-party tools can be found here.

These tools generally provide a more comprehensive set of features than the native GCP tools, including more advanced analytics, budgeting, and forecasting capabilities.

Free Open Source Tools

Open source tools provide a level of transparency and customizability that's unmatched by other types of tools. A list of industry-leading open source tools can be found here.

• They can be a cost-effective solution for organizations with the skills and resources to utilize them effectively.
• However, they may come with their own set of challenges, including a steeper learning curve, limited formal support, and potential integration issues.

By understanding your specific requirements and conducting thorough research, you can find the right tool that fits your organization's cloud management needs.

How to Choose the Right Cost Management Tool?

When it comes to selecting a cloud management platform (CMP), it's crucial to align your choice with the unique needs and strategic direction of your organization's cloud journey. Here are some key factors to consider:

Cost Tracking & Allocation

The CMP should provide robust cost tracking capabilities to properly attribute cloud expenses to the appropriate business units or departments. This granular cost insight facilitates efficient cost management and informs optimization strategies.

Integration Capabilities

Your tool should seamlessly integrate with your current project management and IT tools, delivering real-time alerts, swift deployment capabilities, and increased cloud environment visibility. Ensure that the chosen tool caters specifically to your cloud platform.

Automated Alerts

The CMP should be capable of setting up automated alerts for rapid cost spikes, untagged infrastructure, permission failures, or exceeded budget limits. These alerts act as an early warning system to help mitigate unexpected cost overruns.

Intelligent Recommendations

A top-tier CMP should provide tailored recommendations based on your unique cloud usage patterns. Suggestions regarding underutilized instances, storage inefficiencies, or compute optimizations can lead to significant cost savings over time.

By evaluating these critical factors, you can make an informed decision that will shape the effectiveness of your cloud cost management strategy.

Best practices for optimizing GCP costs

When it comes to Google Cloud Platform (GCP), there are a myriad of tools and best practices designed to help users optimize their cloud expenditures. By putting these recommended practices into action, you can considerably cut down your cloud expenses, thereby making your resource allocation more efficient.

Bear in mind that achieving cost optimization on the cloud isn't a one-off task but an ongoing journey that demands regular monitoring and tweaking.

Saving with Google Cloud Platform Spot Virtual Machines (VMs)

Google Cloud Platform Spot VMs are a cost-effective option for running workloads that can tolerate interruptions. They are pre-emptible virtual machines (VMs) that are made available at a discounted price compared to on-demand VMs, in exchange for the possibility of being terminated by GCP with a short notice (30 seconds) when the spot instances are needed by other customers with higher priority.

Some examples of workloads that can benefit from using GCP Spot VMs include:

• Batch jobs: These are typically non-interactive workloads that can be stopped and resumed without any negative impact. Examples include data processing, analytics, and rendering.
• Stateless workloads: These are workloads that do not maintain any persistent state, and can easily be replaced by another instance if needed. Examples include web servers and load balancers.
• Fault-tolerant workloads: These are workloads that can continue to function even if one or more instances are terminated. Examples include distributed systems and applications that use auto-scaling.

Identifying cost saving opportunities with GCP Active Assist

GCP Active Assist provides real-time guidance and suggestions to help users troubleshoot and optimize their GCP resources. It can help users identify and resolve issues related to performance, cost, security, reliability, manageability and sustainability, GCP calls it “Recommenders”.

It also provides automated remediation actions to help users quickly resolve problems and improve the overall health of their GCP resources through Recommendations Hub. These Recommendations can also be consumed via Recommender API or exported to BigQuery on a scheduled manner.

VM Machine Type Recommender

The VM Machine Type Recommender is one of the most used features. It looks at how VMs have been used in the past to suggest the best machine type for your workload and usage.

• It takes into account data such as CPU and memory usage, as well as how many vCPUs and how much memory is allocated to the VMs. It also considers the cost of the different machine types so you can get the most cost-effective option.
• The recommender can also detect VMs that are underutilized or idle and suggest shutting them down or resizing them to save money. It can also detect VMs that are overutilized and suggest upgrading them to improve performance.
• It relies on machine learning algorithms to make its recommendations. They are updated in real-time as new usage data becomes available, which lets the recommender adapt to changing usage patterns and make more accurate recommendations.

Measure and Manage your resources with Cloud Asset Inventory

Cloud Asset Inventory helps in optimizing resource allocation. It can provide detailed information about the resources in use, such as the resource type, location, and size.

This information can be used to optimize resource allocation by identifying resources that are over or under-allocated, and adjusting them accordingly.

While Cloud Asset Inventory can be a powerful tool for tracking resource changes, there are some limitations to be aware of:

1. Limited historical data: Cloud Asset Inventory may not retain historical data for an extended period of time, which can make it difficult to track long-term resource changes or trends.
2. Limited visibility into certain resources: Some resources, such as cloud services or applications, may not be fully visible to Cloud Asset Inventory, which can make it difficult to track changes to those resources.
3. Limited integration: Cloud Asset Inventory may not be able to integrate with other tools or systems, which can limit the ability to track resource changes in a centralized way.