The cost optimization pillar in the Google Cloud Well-Architected Framework describes principles and recommendations to optimize the cost of your workloads in Google Cloud.
The intended audience includes the following:
- CTOs, CIOs, CFOs, and other executives who are responsible for strategic cost management.
- Architects, developers, administrators, and operators who make decisions that affect cost at all the stages of an organization's cloud journey.
The cost models for on-premises and cloud workloads differ significantly. On-premises IT costs include capital expenditure (CapEx) and operational expenditure (OpEx). On-premises hardware and software assets are acquired and the acquisition costs are depreciated over the operating life of the assets. In the cloud, the costs for most cloud resources are treated as OpEx, where costs are incurred when the cloud resources are consumed. This fundamental difference underscores the importance of the following core principles of cost optimization.
For cost optimization principles and recommendations that are specific to AI and ML workloads, see AI and ML perspective: Cost optimization in the Well-Architected Framework.
Core principles
The recommendations in the cost optimization pillar of the Well-Architected Framework are mapped to the following core principles:
- Align cloud spending with business value: Ensure that your cloud resources deliver measurable business value by aligning IT spending with business objectives.
- Foster a culture of cost awareness: Ensure that people across your organization consider the cost impact of their decisions and activities, and ensure that they have access to the cost information required to make informed decisions.
- Optimize resource usage: Provision only the resources that you need, and pay only for the resources that you consume.
- Optimize continuously: Continuously monitor your cloud resource usage and costs, and proactively make adjustments as needed to optimize your spending. This approach involves identifying and addressing potential cost inefficiencies before they become significant problems.
These principles are closely aligned with the core tenets of cloud FinOps. FinOps is relevant to any organization, regardless of its size or maturity in the cloud. By adopting these principles and following the related recommendations, you can control and optimize costs throughout your journey in the cloud.
Contributors
Author: Nicolas Pintaux | Customer Engineer, Application Modernization Specialist
Other contributors:
- Anuradha Bajpai | Solutions Architect
- Daniel Lees | Cloud Security Architect
- Eric Lam | Head of Google Cloud FinOps
- Fernando Rubbo | Cloud Solutions Architect
- Filipe Gracio, PhD | Customer Engineer
- Gary Harmson | Customer Engineer
- Jose Andrade | Enterprise Infrastructure Customer Engineer
- Kent Hua | Solutions Manager
- Kumar Dhanagopal | Cross-Product Solution Developer
- Marwan Al Shawi | Partner Customer Engineer
- Radhika Kanakam | Senior Program Manager, Cloud GTM
- Steve McGhee | Reliability Advocate
- Sergei Lilichenko | Solutions Architect
- Wade Holmes | Global Solutions Director
- Zach Seils | Networking Specialist
Align cloud spending with business value
This principle in the cost optimization pillar of the Google Cloud Well-Architected Framework provides recommendations to align your use of Google Cloud resources with your organization's business goals.
Principle overview
To effectively manage cloud costs, you need to maximize the business value that the cloud resources provide and minimize the total cost of ownership (TCO). When you evaluate the resource options for your cloud workloads, consider not only the cost of provisioning and using the resources, but also the cost of managing them. For example, virtual machines (VMs) on Compute Engine might be a cost-effective option for hosting applications. However, when you consider the overhead to maintain, patch, and scale the VMs, the TCO can increase. On the other hand, serverless services like Cloud Run can offer greater business value. The lower operational overhead lets your team focus on core activities and helps to increase agility.
To ensure that your cloud resources deliver optimal value, evaluate the following factors:
- Provisioning and usage costs: The expenses incurred when you purchase, provision, or consume resources.
- Management costs: The recurring expenses for operating and maintaining resources, including tasks like patching, monitoring and scaling.
- Indirect costs: The costs that you might incur to manage issues like downtime, data loss, or security breaches.
- Business impact: The potential benefits from the resources, like increased revenue, improved customer satisfaction, and faster time to market.
By aligning cloud spending with business value, you get the following benefits:
- Value-driven decisions: Your teams are encouraged to prioritize solutions that deliver the greatest business value and to consider both short-term and long-term cost implications.
- Informed resource choice: Your teams have the information and knowledge that they need to assess the business value and TCO of various deployment options, so they choose resources that are cost-effective.
- Cross-team alignment: Cross-functional collaboration between business, finance, and technical teams ensures that cloud decisions are aligned with the overall objectives of the organization.
Recommendations
To align cloud spending with business objectives, consider the following recommendations.
Prioritize managed services and serverless products
Whenever possible, choose managed services and serverless products to reduce operational overhead and maintenance costs. This choice lets your teams concentrate on their core business activities. They can accelerate the delivery of new features and functionalities, and help drive innovation and value.
The following are examples of how you can implement this recommendation:
- To run PostgreSQL, MySQL, or Microsoft SQL Server server databases, use Cloud SQL instead of deploying those databases on VMs.
- To run and manage Kubernetes clusters, use Google Kubernetes Engine (GKE) Autopilot instead of deploying containers on VMs.
- For your Apache Hadoop or Apache Spark processing needs, use Dataproc and Dataproc Serverless. Per-second billing can help to achieve significantly lower TCO when compared to on-premises data lakes.
Balance cost efficiency with business agility
Controlling costs and optimizing resource utilization are important goals. However, you must balance these goals with the need for flexible infrastructure that lets you innovate rapidly, respond quickly to changes, and deliver value faster. The following are examples of how you can achieve this balance:
- Adopt DORA metrics for software delivery performance. Metrics like change failure rate (CFR), time to detect (TTD), and time to restore (TTR) can help to identify and fix bottlenecks in your development and deployment processes. By reducing downtime and accelerating delivery, you can achieve both operational efficiency and business agility.
- Follow Site Reliability Engineering (SRE) practices to improve operational reliability. SRE's focus on automation, observability, and incident response can lead to reduced downtime, lower recovery time, and higher customer satisfaction. By minimizing downtime and improving operational reliability, you can prevent revenue loss and avoid the need to overprovision resources as a safety net to handle outages.
Enable self-service optimization
Encourage a culture of experimentation and exploration by providing your teams with self-service cost optimization tools, observability tools, and resource management platforms. Enable them to provision, manage, and optimize their cloud resources autonomously. This approach helps to foster a sense of ownership, accelerate innovation, and ensure that teams can respond quickly to changing needs while being mindful of cost efficiency.
Adopt and implement FinOps
Adopt FinOps to establish a collaborative environment where everyone is empowered to make informed decisions that balance cost and value. FinOps fosters financial accountability and promotes effective cost optimization in the cloud.
Promote a value-driven and TCO-aware mindset
Encourage your team members to adopt a holistic attitude toward cloud spending, with an emphasis on TCO and not just upfront costs. Use techniques like value stream mapping to visualize and analyze the flow of value through your software delivery process and to identify areas for improvement. Implement unit costing for your applications and services to gain a granular understanding of cost drivers and discover opportunities for cost optimization. For more information, see Maximize business value with cloud FinOps.
Foster a culture of cost awareness
This principle in the cost optimization pillar of the Google Cloud Well-Architected Framework provides recommendations to promote cost awareness across your organization and ensure that team members have the cost information that they need to make informed decisions.
Conventionally, the responsibility for cost management might be centralized to a few select stakeholders and primarily focused on initial project architecture decisions. However, team members across all cloud user roles (analyst, architect, developer, or administrator) can help to reduce the cost of your resources in Google Cloud. By sharing cost data appropriately, you can empower team members to make cost-effective decisions throughout their development and deployment processes.
Principle overview
Stakeholders across various roles – product owners, developers, deployment engineers, administrators, and financial analysts – need visibility into relevant cost data and its relationship to business value. When provisioning and managing cloud resources, they need the following data:
- Projected resource costs: Cost estimates at the time of design and deployment.
- Real-time resource usage costs: Up-to-date cost data that can be used for ongoing monitoring and budget validation.
- Costs mapped to business metrics: Insights into how cloud spending affects key performance indicators (KPIs), to enable teams to identify cost-effective strategies.
Every individual might not need access to raw cost data. However, promoting cost awareness across all roles is crucial because individual decisions can affect costs.
By promoting cost visibility and ensuring clear ownership of cost management practices, you ensure that everyone is aware of the financial implications of their choices and everyone actively contributes to the organization's cost optimization goals. Whether through a centralized FinOps team or a distributed model, establishing accountability is crucial for effective cost optimization efforts.
Recommendations
To promote cost awareness and ensure that your team members have the cost information that they need to make informed decisions, consider the following recommendations.
Provide organization-wide cost visibility
To achieve organization-wide cost visibility, the teams that are responsible for cost management can take the following actions:
- Standardize cost calculation and budgeting: Use a consistent method to determine the full costs of cloud resources, after factoring in discounts and shared costs. Establish clear and standardized budgeting processes that align with your organization's goals and enable proactive cost management.
- Use standardized cost management and visibility tools: Use appropriate tools that provide real-time insights into cloud spending and generate regular (for example, weekly) cost progression snapshots. These tools enable proactive budgeting, forecasting, and identification of optimization opportunities. The tools could be cloud provider tools (like the Google Cloud Billing dashboard), third-party solutions, or open-source solutions like the Cost Attribution solution.
- Implement a cost allocation system: Allocate a portion of the overall cloud budget to each team or project. Such an allocation gives the teams a sense of ownership over cloud spending and encourages them to make cost-effective decisions within their allocated budget.
- Promote transparency: Encourage teams to discuss cost implications during the design and decision-making processes. Create a safe and supportive environment for sharing ideas and concerns related to cost optimization. Some organizations use positive reinforcement mechanisms like leaderboards or recognition programs. If your organization has restrictions on sharing raw cost data due to business concerns, explore alternative approaches for sharing cost information and insights. For example, consider sharing aggregated metrics (like the total cost for an environment or feature) or relative metrics (like the average cost per transaction or user).
Understand how cloud resources are billed
Pricing for Google Cloud resources might vary across regions. Some resources are billed monthly at a fixed price, and others might be billed based on usage. To understand how Google Cloud resources are billed, use the Google Cloud pricing calculator and product-specific pricing information (for example, Google Kubernetes Engine (GKE) pricing).
Understand resource-based cost optimization options
For each type of cloud resource that you plan to use, explore strategies to optimize utilization and efficiency. The strategies include rightsizing, autoscaling, and adopting serverless technologies where appropriate. The following are examples of cost optimization options for a few Google Cloud products:
- Cloud Run lets you configure always-allocated CPUs to handle predictable traffic loads at a fraction of the price of the default allocation method (that is, CPUs allocated only during request processing).
- You can purchase BigQuery slot commitments to save money on data analysis.
- GKE provides detailed metrics to help you understand cost optimization options.
- Understand how network pricing can affect the cost of data transfers and how you can optimize costs for specific networking services. For example, you can reduce the data transfer costs for external Application Load Balancers by using Cloud CDN or Google Cloud Armor. For more information, see Ways to lower external Application Load Balancer costs.
Understand discount-based cost optimization options
Familiarize yourself with the discount programs that Google Cloud offers, such as the following examples:
- Committed use discounts (CUDs): CUDs are suitable for resources that have predictable and steady usage. CUDs let you get significant reductions in price in exchange for committing to specific resource usage over a period (typically one to three years). You can also use CUD auto-renewal to avoid having to manually repurchase commitments when they expire.
- Sustained use discounts: For certain Google Cloud products like Compute Engine and GKE, you can get automatic discount credits after continuous resource usage beyond specific duration thresholds.
- Spot VMs: For fault-tolerant and flexible workloads, Spot VMs can help to reduce your Compute Engine costs. The cost of Spot VMs is significantly lower than regular VMs. However, Compute Engine might preemptively stop or delete Spot VMs to reclaim capacity. Spot VMs are suitable for batch jobs that can tolerate preemption and don't have high availability requirements.
- Discounts for specific product options: Some managed services like BigQuery offer discounts when you purchase dedicated or autoscaling query processing capacity.
Evaluate and choose the discounts options that align with your workload characteristics and usage patterns.
Incorporate cost estimates into architecture blueprints
Encourage teams to develop architecture blueprints that include cost estimates for different deployment options and configurations. This practice empowers teams to compare costs proactively and make informed decisions that align with both technical and financial objectives.
Use a consistent and standard set of labels for all your resources
You can use labels to track costs and to identify and classify resources. Specifically, you can use labels to allocate costs to different projects, departments, or cost centers. Defining a formal labeling policy that aligns with the needs of the main stakeholders in your organization helps to make costs visible more widely. You can also use labels to filter resource cost and usage data based on target audience.
Use automation tools like Terraform to enforce labeling on every resource that is created. To enhance cost visibility and attribution further, you can use the tools provided by the open-source cost attribution solution.
Share cost reports with team members
By sharing cost reports with your team members, you empower them to take ownership of their cloud spending. This practice enables cost-effective decision making, continuous cost optimization, and systematic improvements to your cost allocation model.
Cost reports can be of several types, including the following:
- Periodic cost reports: Regular reports inform teams about their current cloud spending. Conventionally, these reports might be spreadsheet exports. More effective methods include automated emails and specialized dashboards. To ensure that cost reports provide relevant and actionable information without overwhelming recipients with unnecessary detail, the reports must be tailored to the target audiences. Setting up tailored reports is a foundational step toward more real-time and interactive cost visibility and management.
- Automated notifications: You can configure cost reports to proactively notify relevant stakeholders (for example, through email or chat) about cost anomalies, budget thresholds, or opportunities for cost optimization. By providing timely information directly to those who can act on it, automated alerts encourage prompt action and foster a proactive approach to cost optimization.
- Google Cloud dashboards: You can use the built-in billing dashboards in Google Cloud to get insights into cost breakdowns and to identify opportunities for cost optimization. Google Cloud also provides FinOps hub to help you monitor savings and get recommendations for cost optimization. An AI engine powers the FinOps hub to recommend cost optimization opportunities for all the resources that are currently deployed. To control access to these recommendations, you can implement role-based access control (RBAC).
- Custom dashboards: You can create custom dashboards by exporting cost data to an analytics database, like BigQuery. Use a visualization tool like Looker Studio to connect to the analytics database to build interactive reports and enable fine-grained access control through role-based permissions.
- Multicloud cost reports: For multicloud deployments, you need a unified view of costs across all the cloud providers to ensure comprehensive analysis, budgeting, and optimization. Use tools like BigQuery to centralize and analyze cost data from multiple cloud providers, and use Looker Studio to build team-specific interactive reports.
Optimize resource usage
This principle in the cost optimization pillar of the Google Cloud Well-Architected Framework provides recommendations to help you plan and provision resources to match the requirements and consumption patterns of your cloud workloads.
Principle overview
To optimize the cost of your cloud resources, you need to thoroughly understand your workloads resource requirements and load patterns. This understanding is the basis for a well defined cost model that lets you forecast the total cost of ownership (TCO) and identify cost drivers throughout your cloud adoption journey. By proactively analyzing and forecasting cloud spending, you can make informed choices about resource provisioning, utilization, and cost optimization. This approach lets you control cloud spending, avoid overprovisioning, and ensure that cloud resources are aligned with the dynamic needs of your workloads and environments.
Recommendations
To effectively optimize cloud resource usage, consider the following recommendations.
Choose environment-specific resources
Each deployment environment has different requirements for availability, reliability and scalability. For example, developers might prefer an environment that lets them rapidly deploy and run applications for short durations, but might not need high availability. On the other hand, a production environment typically needs high availability. To maximize the utilization of your resources, define environment-specific requirements based on your business needs. The following table lists examples of environment-specific requirements.
| Environment | Requirements |
| Production |
|
| Development and testing |
|
| Other environments (like staging and QA) |
|
Choose workload-specific resources
Each of your cloud workloads might have different requirements for availability, scalability, security, and performance. To optimize costs, you need to align resource choices with the specific requirements of each workload. For example, a stateless application might not require the same level of availability or reliability as a stateful backend. The following table lists more examples of workload-specific requirements.
| Workload type | Workload requirements | Resource options |
| Mission-critical | Continuous availability, robust security, and high performance | Premium resources and managed services like Spanner for high availability and global consistency of data. |
| Non-critical | Cost-efficient and autoscaling infrastructure | Resources with basic features and ephemeral resources like Spot VMs. |
| Event-driven | Dynamic scaling based on the current demand for capacity and performance | Serverless services like Cloud Run and Cloud Run functions. |
| Experimental workloads | Low cost and flexible environment for rapid development, iteration, testing, and innovation | Resources with basic features, ephemeral resources like Spot VMs, and sandbox environments with defined spending limits. |
A benefit of the cloud is the opportunity to take advantage of the most appropriate computing power for a given workload. Some workloads are developed to take advantage of processor instruction sets, and others might not be designed in this way. Benchmark and profile your workloads accordingly. Categorize your workloads and make workload-specific resource choices (for example, choose appropriate machine families for Compute Engine VMs). This practice helps to optimize costs, enable innovation, and maintain the level of availability and performance that your workloads need.
The following are examples of how you can implement this recommendation:
- For mission-critical workloads that serve globally distributed users, consider using Spanner. Spanner removes the need for complex database deployments by ensuring reliability and consistency of data in all regions.
- For workloads with fluctuating load levels, use autoscaling to ensure that you don't incur costs when the load is low and yet maintain sufficient capacity to meet the current load. You can configure autoscaling for many Google Cloud services, including Compute Engine VMs, Google Kubernetes Engine (GKE) clusters, and Cloud Run. When you set up autoscaling, you can configure maximum scaling limits to ensure that costs remain within specified budgets.
Select regions based on cost requirements
For your cloud workloads, carefully evaluate the available Google Cloud regions and choose regions that align with your cost objectives. The region with lowest cost might not offer optimal latency or it might not meet your sustainability requirements. Make informed decisions about where to deploy your workloads to achieve the desired balance. You can use the Google Cloud Region Picker to understand the trade-offs between cost, sustainability, latency, and other factors.
Use built-in cost optimization options
Google Cloud products provide built-in features to help you optimize resource usage and control costs. The following table lists examples of cost optimization features that you can use in some Google Cloud products:
| Product | Cost optimization feature |
| Compute Engine |
|
| GKE |
|
| Cloud Storage |
|
| BigQuery |
|
| Google Cloud VMware Engine |
|
Optimize resource sharing
To maximize the utilization of cloud resources, you can deploy multiple applications or services on the same infrastructure, while still meeting the security and other requirements of the applications. For example, in development and testing environments, you can use the same cloud infrastructure to test all the components of an application. For the production environment, you can deploy each component on a separate set of resources to limit the extent of impact in case of incidents.
The following are examples of how you can implement this recommendation:
- Use a single Cloud SQL instance for multiple non-production environments.
- Enable multiple development teams to share a GKE cluster by using the fleet team management feature in GKE Enterprise with appropriate access controls.
- Use GKE Autopilot to take advantage of cost-optimization techniques like bin packing and autoscaling that GKE implements by default.
- For AI and ML workloads, save GPU costs by using GPU-sharing strategies like multi-instance GPUs, time-sharing GPUs, and NVIDIA MPS.
Develop and maintain reference architectures
Create and maintain a repository of reference architectures that are tailored to meet the requirements of different deployment environments and workload types. To streamline the design and implementation process for individual projects, the blueprints can be centrally managed by a team like a Cloud Center of Excellence (CCoE). Project teams can choose suitable blueprints based on clearly defined criteria, to ensure architectural consistency and adoption of best practices. For requirements that are unique to a project, the project team and the central architecture team should collaborate to design new reference architectures. You can share the reference architectures across the organization to foster knowledge sharing and expand the repository of available solutions. This approach ensures consistency, accelerates development, simplifies decision-making, and promotes efficient resource utilization.
Review the reference architectures provided by Google for various use cases and technologies. These reference architectures incorporate best practices for resource selection, sizing, configuration, and deployment. By using these reference architectures, you can accelerate your development process and achieve cost savings from the start.
Enforce cost discipline by using organization policies
Consider using organization policies to limit the available Google Cloud locations and products that team members can use. These policies help to ensure that teams adhere to cost-effective solutions and provision resources in locations that are aligned with your cost optimization goals.
Estimate realistic budgets and set financial boundaries
Develop detailed budgets for each project, workload, and deployment environment. Make sure that the budgets cover all aspects of cloud operations, including infrastructure costs, software licenses, personnel, and anticipated growth. To prevent overspending and ensure alignment with your financial goals, establish clear spending limits or thresholds for projects, services, or specific resources. Monitor cloud spending regularly against these limits. You can use proactive quota alerts to identify potential cost overruns early and take timely corrective action.
In addition to setting budgets, you can use quotas and limits to help enforce cost discipline and prevent unexpected spikes in spending. You can exercise granular control over resource consumption by setting quotas at various levels, including projects, services, and even specific resource types.
The following are examples of how you can implement this recommendation:
- Project-level quotas: Set spending limits or resource quotas at the project level to establish overall financial boundaries and control resource consumption across all the services within the project.
- Service-specific quotas: Configure quotas for specific Google Cloud services like Compute Engine or BigQuery to limit the number of instances, CPUs, or storage capacity that can be provisioned.
- Resource type-specific quotas: Apply quotas to individual resource types like Compute Engine VMs, Cloud Storage buckets, Cloud Run instances, or GKE nodes to restrict their usage and prevent unexpected cost overruns.
- Quota alerts: Get notifications when your quota usage (at the project level) reaches a percentage of the maximum value.
By using quotas and limits in conjunction with budgeting and monitoring, you can create a proactive and multi-layered approach to cost control. This approach helps to ensure that your cloud spending remains within defined boundaries and aligns with your business objectives. Remember, these cost controls are not permanent or rigid. To ensure that the cost controls remain aligned with current industry standards and reflect your evolving business needs, you must review the controls regularly and adjust them to include new technologies and best practices.
Optimize continuously
This principle in the cost optimization pillar of the Google Cloud Well-Architected Framework provides recommendations to help you optimize the cost of your cloud deployments based on constantly changing and evolving business goals.
As your business grows and evolves, your cloud workloads need to adapt to changes in resource requirements and usage patterns. To derive maximum value from your cloud spending, you must maintain cost-efficiency while continuing to support business objectives. This requires a proactive and adaptive approach that focuses on continuous improvement and optimization.
Principle overview
To optimize cost continuously, you must proactively monitor and analyze your cloud environment and make suitable adjustments to meet current requirements. Focus your monitoring efforts on key performance indicators (KPIs) that directly affect your end users' experience, align with your business goals, and provide insights for continuous improvement. This approach lets you identify and address inefficiencies, adapt to changing needs, and continuously align cloud spending with strategic business goals. To balance comprehensive observability with cost effectiveness, understand the costs and benefits of monitoring resource usage and use appropriate process-improvement and optimization strategies.
Recommendations
To effectively monitor your Google Cloud environment and optimize cost continuously, consider the following recommendations.
Focus on business-relevant metrics
Effective monitoring starts with identifying the metrics that are most important for your business and customers. These metrics include the following:
- User experience metrics: Latency, error rates, throughput, and customer satisfaction metrics are useful for understanding your end users' experience when using your applications.
- Business outcome metrics: Revenue, customer growth, and engagement can be correlated with resource usage to identify opportunities for cost optimization.
- DevOps Research & Assessment (DORA) metrics: Metrics like deployment frequency, lead time for changes, change failure rate, and time to restore provide insights into the efficiency and reliability of your software delivery process. By improving these metrics, you can increase productivity, reduce downtime, and optimize cost.
- Site Reliability Engineering (SRE) metrics: Error budgets help teams to quantify and manage the acceptable level of service disruption. By establishing clear expectations for reliability, error budgets empower teams to innovate and deploy changes more confidently, knowing their safety margin. This proactive approach promotes a balance between innovation and stability, helping prevent excessive operational costs associated with major outages or prolonged downtime.
Use observability for resource optimization
The following are recommendations to use observability to identify resource bottlenecks and underutilized resources in your cloud deployments:
- Monitor resource utilization: Use resource utilization metrics to identify Google Cloud resources that are underutilized. For example, use metrics like CPU and memory utilization to identify idle VM resources. For Google Kubernetes Engine (GKE), you can view a detailed breakdown of costs and cost-related optimization metrics. For Google Cloud VMware Engine, review resource utilization to optimize CUDs, storage consumption, and ESXi right-sizing.
- Use cloud recommendations: Active Assist is a portfolio of intelligent tools that help you optimize your cloud operations. These tools provide actionable recommendations to reduce costs, increase performance, improve security and even make sustainability-focused decisions. For example, VM rightsizing insights can help to optimize resource allocation and avoid unnecessary spending.
- Correlate resource utilization with performance: Analyze the relationship between resource utilization and application performance to determine whether you can downgrade to less expensive resources without affecting the user experience.
Balance troubleshooting needs with cost
Detailed observability data can help with diagnosing and troubleshooting issues. However, storing excessive amounts of observability data or exporting unnecessary data to external monitoring tools can lead to unnecessary costs. For efficient troubleshooting, consider the following recommendations:
- Collect sufficient data for troubleshooting: Ensure that your monitoring solution captures enough data to efficiently diagnose and resolve issues when they arise. This data might include logs, traces, and metrics at various levels of granularity.
- Use sampling and aggregation: Balance the need for detailed data with cost considerations by using sampling and aggregation techniques. This approach lets you collect representative data without incurring excessive storage costs.
- Understand the pricing models of your monitoring tools and services: Evaluate different monitoring solutions and choose options that align with your project's specific needs, budget, and usage patterns. Consider factors like data volume, retention requirements, and the required features when making your selection.
- Regularly review your monitoring configuration: Avoid collecting excessive data by removing unnecessary metrics or logs.
Tailor data collection to roles and set role-specific retention policies
Consider the specific data needs of different roles. For example, developers might primarily need access to traces and application-level logs, whereas IT administrators might focus on system logs and infrastructure metrics. By tailoring data collection, you can reduce unnecessary storage costs and avoid overwhelming users with irrelevant information.
Additionally, you can define retention policies based on the needs of each role and any regulatory requirements. For example, developers might need access to detailed logs for a shorter period, while financial analysts might require longer-term data.
Consider regulatory and compliance requirements
In certain industries, regulatory requirements mandate data retention. To avoid legal and financial risks, you need to ensure that your monitoring and data retention practices help you adhere to relevant regulations. At the same time, you need to maintain cost efficiency. Consider the following recommendations:
- Determine the specific data retention requirements for your industry or region, and ensure that your monitoring strategy meets the requirements of those requirements.
- Implement appropriate data archival and retrieval mechanisms to meet audit and compliance needs while minimizing storage costs.
Implement smart alerting
Alerting helps to detect and resolve issues in a timely manner. However, a balance is necessary between an approach that keeps you informed, and one that overwhelms you with notifications. By designing intelligent alerting systems, you can prioritize critical issues that have higher business impact. Consider the following recommendations:
- Prioritize issues that affect customers: Design alerts that trigger rapidly for issues that directly affect the customer experience, like website outages, slow response times, or transaction failures.
- Tune for temporary problems: Use appropriate thresholds and delay mechanisms to avoid unnecessary alerts for temporary problems or self-healing system issues that don't affect customers.
- Customize alert severity: Ensure that the most urgent issues receive immediate attention by differentiating between critical and noncritical alerts.
- Use notification channels wisely: Choose appropriate channels for alert notifications (email, SMS, or paging) based on the severity and urgency of the alerts.