Top Metrics To Spot Underused Resources In Datadog

Want to save on cloud costs and improve performance? Datadog can help you identify underused resources and optimize your infrastructure. Here's a quick summary of the top metrics you should monitor:

• CPU Utilization: Spot oversized or idle instances by tracking low CPU usage.
• Memory Usage: Identify underutilized RAM and adjust allocations to reduce waste.
• Disk I/O and Storage: Detect over-provisioned storage and unused capacity to cut costs.
• Network Traffic: Monitor data transfer fees and bandwidth to avoid hidden expenses.
• Instance Uptime vs. Activity: Compare uptime with actual workloads to find idle instances.
• Custom Application Metrics: Track app-specific inefficiencies like unused database connections.
• Resource Reservation vs. Utilization: Measure gaps between reserved and used resources, especially in Kubernetes.
• Rightsizing Recommendations: Use Datadog’s insights to resize resources for better efficiency.
• Tag-Based Resource Grouping: Organize resources to easily spot inefficiencies by environment, team, or application.
• Historical Usage Trends: Analyze long-term data to uncover consistent underutilization patterns.

Why it matters: SMBs waste up to 30% of cloud spending on unused resources. Monitoring these metrics can help you save thousands annually while improving system reliability. Start optimizing today with Datadog's tools for better visibility and actionable insights.

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Why Monitoring Underused Resources Matters

Keeping an eye on underused cloud resources is crucial when it comes to managing cloud spending effectively. For small and medium-sized businesses (SMBs), these underutilized resources can quietly drain budgets. A recent study found that around 30% of cloud spending is wasted on average. That’s like paying for services you never actually use - a costly oversight.

The financial toll is especially hard on smaller companies. Research from Flexera shows that 36% of SMBs spend up to $600,000 annually on public cloud services. If a third of that is wasted, that’s a potential $180,000 in savings - money that could be put to much better use.

This issue has gained even more attention recently. Between 2023 and 2024, cloud cost optimization became the top priority for SMBs and midmarket companies, overtaking its second-place ranking from 2021–2022. This shift reflects a growing awareness of the financial and operational benefits of addressing this problem.

Cutting back on underused resources doesn’t just save money - it also improves performance. According to IDC, businesses using cloud services spend 69% less time on routine IT maintenance compared to those relying on traditional, on-premises systems. That means IT teams can focus on bigger-picture projects instead of managing an overstuffed infrastructure.

Real-world examples show how impactful this can be. A SaaS startup managed to cut its monthly cloud bill by 25% by optimizing 40% of its underused resources. Similarly, an e-commerce SMB slashed its compute costs by half using spot instances. Another FinTech startup saved $100,000 annually while also improving system reliability by diversifying its cloud providers to avoid vendor lock-in.

These savings can be redirected into areas like hiring top talent, expanding into new markets, or developing innovative products - investments that drive long-term growth.

The market trends back up this urgency. By 2025, 57% of technical professionals are expected to prioritize cloud cost optimization, and 72% of partners plan to offer cost-saving services to their clients. This widespread focus underscores how essential resource optimization is for staying competitive.

Lastly, efficient cloud usage isn’t just about saving money - it also reduces energy consumption. This aligns with environmentally conscious practices, which are increasingly important to customers and investors alike.

1. CPU Utilization

CPU utilization is a key metric for assessing how efficiently your cloud resources are being used. For small and medium-sized businesses (SMBs) working with limited budgets, idle CPUs translate to wasted money.

Impact on Cost Optimization

Keeping an eye on CPU utilization is essential for managing costs. It's estimated that 27% of cloud spending goes toward underutilized resources. Many organizations overprovision instances to handle peak demand, even if those peaks are rare. This means paying for capacity that often sits unused.

With tools like Datadog, you can consolidate workloads onto fewer instances or containers. This not only reduces the number of active hosts but also lowers per-host monitoring expenses.

Identifying Underutilization

CPU metrics are invaluable for spotting underused resources. If you notice consistently low CPU usage across your infrastructure, it might mean your instances are oversized or your workloads are spread too thin. AWS provides guidelines for CPU utilization thresholds depending on your goals: 40% for availability, 70% for cost efficiency, and 50% for a balance of both. Datadog’s user-friendly interface makes it simple to monitor and interpret these metrics.

Simplified Monitoring with Datadog

Datadog’s Cloud Cost Management feature streamlines the process of tracking CPU usage. It enables you to compare clusters’ CPU performance against their reserved resources, making it easier to identify underutilization trends. Additionally, Datadog generates utilization scores that guide you in rebalancing and optimizing your instances.

Turning Insights into Action

Once you’ve gathered CPU data, it’s time to act. Datadog helps you pinpoint services with complementary usage patterns, allowing you to colocate them on a single host or container. This approach maximizes resource efficiency without sacrificing performance. For clusters with persistent underutilization, scaling down can significantly cut costs. By rebalancing resources based on actual usage, you can turn monitoring insights into measurable savings.

2. Memory Usage

Keeping an eye on memory usage is another essential aspect of managing resources effectively, yet it’s something many small and medium-sized businesses (SMBs) tend to overlook. While CPU metrics focus on processing efficiency, memory metrics reveal whether your allocated RAM is being fully utilized or if you're paying for resources that are sitting idle. Together, these metrics play a key role in managing costs.

Impact on Cost Management

Over-provisioning memory can lead to unnecessary expenses by reserving more resources than you actually need. By tracking memory usage, you can identify these inefficiencies and adjust allocations to cut down on waste.

Datadog provides detailed memory usage metrics that help you fine-tune your AWS instances. With these insights, you can consolidate workloads onto fewer instances or containers, slashing both infrastructure and monitoring costs.

Spotting Underutilization

Memory metrics are also invaluable for identifying when resources are underused. Datadog goes a step further by tracking both memory utilization and allocation time, giving you a complete picture of how your resources are being used.

A critical area to monitor is the gap between memory requests and actual usage. For example, comparing requested memory against capacity metrics can uncover inefficiencies, like applications asking for far more memory than they use. This can help troubleshoot issues with launching or running the right number of pods.

Simplified Monitoring with Datadog

Datadog makes monitoring memory usage straightforward with its built-in tools. The Process Check feature, for instance, lets you track memory usage at the process level, helping you identify which processes are consuming the most resources.

For containerized environments, Datadog provides insights at both the pod and node levels. This kind of monitoring is crucial for understanding cluster performance and ensuring workloads run smoothly. Additionally, Datadog offers a Memory Leaks workflow, which consolidates relevant data and provides a structured approach to investigating and resolving memory leaks.

Turning Insights into Action

Just like CPU monitoring, tracking memory usage can lead to actionable steps for optimizing resources. For example, monitoring pods' actual memory usage against their limits can help you spot risks of them being OOM (Out of Memory) killed. It also highlights opportunities to resize allocations appropriately.

Setting up alerts for high memory usage allows you to make proactive changes, such as revising memory limits or downsizing underutilized instances. On the flip side, consistently low memory usage might indicate that instances can be downsized or workloads can be consolidated.

Monitoring memory allocation time is another useful metric for SMBs. It helps predict costs, identify bottlenecks, and make configuration changes before performance issues arise. By optimizing resource allocation, you can ensure smoother operations and avoid unnecessary expenses.

3. Disk I/O and Storage Usage

After examining CPU and memory, the next piece of the puzzle for resource efficiency is monitoring disk I/O and storage. These metrics often reveal hidden opportunities to cut costs, especially for SMBs. While CPU and memory tend to get the spotlight, storage inefficiencies - like over-provisioned volumes or unused capacity - can quietly drain your budget without drawing much attention.

Impact on Cost Optimization

Storage costs can spiral out of control if left unchecked. 94% of enterprises overspend in the cloud, and storage inefficiencies are a major reason behind this. Even more surprising, 9 out of 10 enterprises fail to measure disk utilization. Without these insights, businesses are essentially guessing when it comes to one of their largest expenses.

By keeping an eye on disk usage, you can avoid over-provisioning, which leads to wasted resources and higher costs. Tracking storage patterns allows you to make smarter decisions about when to add or reduce storage capacity based on actual needs instead of assumptions.

For cloud storage platforms like Amazon S3 and Google Cloud Storage, metrics such as aws.s3.inventory.total_prefix_size and gcp.storage.inventory.total_prefix_size are invaluable. These metrics help you monitor rapid prefix growth and avoid unexpected cost spikes. Tools like Datadog make this level of monitoring much easier.

Relevance to Underutilization Detection

Disk I/O metrics also help pinpoint underutilized resources, providing opportunities to downsize or reallocate storage.

Key metrics to watch include comparing diskspace.provisioned.latest (total available storage) with virtualDisk.actualUsage (the actual storage being used by virtual machines). If virtualDisk.actualUsage consistently falls far below diskspace.provisioned.latest, it’s a clear sign that you’re over-provisioned and could scale down.

Another metric to monitor is %util, which measures the percentage of CPU time spent handling I/O requests. If this value consistently approaches 100%, it signals device saturation. However, very low values can also indicate underutilization. Datadog’s integrations simplify tracking these metrics, making it easier to take action.

Ease of Monitoring Within Datadog

Datadog streamlines storage monitoring through its robust integration capabilities. For example, Datadog’s AWS integration connects with CloudWatch to automatically pull in metrics from services like EBS volumes.

Using the Datadog Agent, you can collect system-level metrics, including disk usage, at 15-second intervals. This includes essential metrics like free, used, and total disk space, along with advanced metrics for environments with heavy storage demands.

For cloud storage, Datadog offers detailed bucket- and prefix-level analytics for Amazon S3 and Google Cloud Storage. These insights make it easier to understand storage usage and identify potential issues before they escalate.

Actionability for Resource Optimization

The real power of disk I/O monitoring lies in turning data into actionable steps. SMBs can use Datadog’s insights to fine-tune their storage allocation in the following ways:

• Identify stale prefixes: Spot unused storage that’s racking up unnecessary costs. Keep an eye on sudden increases in prefix size, which could indicate unexpected application behavior or even security concerns.
• Address elevated latencies and errors: Quickly resolve issues in affected buckets to prevent small glitches from snowballing into major problems.
• Monitor data pipeline health: Compare metrics like aws.s3.inventory.prefix_object_count and aws.s3.inventory.total_prefix_size (or their GCP equivalents) to detect delays in data delivery. Setting up anomaly monitors can help flag unusual data accumulation patterns.
• Optimize data organization: Analyze how file types and storage tiers are distributed across prefixes using metrics like aws.s3.inventory.prefix_object_count and gcp.storage.inventory.prefix_object_count.

Case studies highlight the potential savings. For instance, Finout, a financial services company, used Datadog to optimize their cloud spending, cutting costs by 30%. Similarly, Resume Points reduced their cloud expenses by 20% by using Datadog to track costs across multiple providers.

4. Network Traffic

Keeping an eye on network traffic often gets overlooked, but it’s a crucial step in managing cloud costs. Many small and medium-sized businesses tend to focus on CPU and memory usage, while inefficiencies in network usage - like unnecessary data transfers or over-provisioned bandwidth - can quietly rack up expenses. These hidden costs can add up quickly if left unchecked.

Impact on Cost Management

Network traffic plays a direct role in your cloud spending, particularly when it comes to data transfer fees and bandwidth usage. Tools like Datadog's network monitoring provide visibility into which resources are consuming the most data, helping you make smarter decisions about resource allocation. By ensuring bandwidth and capacity match your actual needs, you avoid paying for unused resources.

Spotting Underutilized Resources

Network traffic patterns can also uncover underused resources. For instance, a server with moderate CPU activity but consistently low network traffic might be over-provisioned. Datadog Network Monitoring offers a unified view across multi-cloud, hybrid, and on-premises setups, making it easier to identify such inefficiencies. By analyzing traffic sources, destinations, ports, and protocols, you can pinpoint which assets are underutilized and decide whether to downsize or consolidate them.

Simplified Monitoring with Datadog

Datadog takes the complexity out of network traffic monitoring by integrating data from both physical and virtual network devices into a single platform. It can even link NetFlow data to specific infrastructure components and applications, helping you trace issues from services back to the network level. The tagging system allows for custom alerts and dashboards, so you can quickly identify and respond to abnormal network activity.

Turning Insights into Savings

Network traffic insights can lead to immediate cost reductions, just like CPU and memory optimizations. Datadog helps you spot the biggest contributors to network congestion, identify bottlenecks, and detect underused interfaces. This information is invaluable for fine-tuning auto-scaling groups, ensuring they respond to actual demand rather than theoretical peaks. Additionally, tracking unusual DNS traffic can help secure your network and eliminate unnecessary resource drains. With Datadog's tagging system, troubleshooting becomes more efficient, enabling you to consolidate or adjust resources based on real-world usage patterns.

5. Instance Uptime vs. Activity

When analyzing cloud resource efficiency, tracking instance uptime is a crucial metric. Sure, an instance might be running 24/7, but how much work is it actually doing? Many small and medium-sized businesses (SMBs) find themselves paying for resources that stay online nonstop while barely processing workloads. This gap between uptime and actual activity is a prime area for cutting costs in cloud operations.

Impact on Cost Optimization

Idle instances can quietly drain your budget. For example, with Datadog's Infrastructure Monitoring starting at $15 per host per month (on an annual plan), every underutilized instance adds unnecessary expense. The cost of idle Amazon EC2 instances is a perfect example of waste that can quickly escalate.

Datadog offers a 15-month performance metric history, making it easier to spot seasonal trends and adjust your resource allocation. This long-term visibility helps you identify patterns of waste and take steps to align costs with actual usage.

Detecting Underutilization

By comparing uptime with actual resource usage, you can uncover inefficiencies that might otherwise go unnoticed. For instance, an instance with moderate CPU usage might seem fine at first glance. But if it’s running continuously while only handling significant workloads during business hours, that’s a red flag for underutilization.

Here’s where Datadog shines. With its agent collecting system metrics every 15 seconds, you get incredibly detailed insights. These granular metrics help you separate truly active instances from those just racking up costs without delivering measurable output.

Monitoring Made Simple with Datadog

Datadog’s AWS integration provides a clear view of your EC2 instance performance. Key metrics like CPUUtilization, NetworkIn/NetworkOut, and system health checks give you a complete picture of both uptime and activity. Here’s a quick breakdown of these metrics:

With Datadog’s Synthetic Monitoring, you can combine uptime data with backend performance metrics to troubleshoot faster. This unified view helps pinpoint instances that may pass health checks but aren’t actively handling traffic or workloads.

Turning Insights Into Action

Once you identify instances with high uptime but low activity, Datadog equips you to take immediate action. For example, you can:

• Build dashboards that correlate uptime with CPU, memory, and network usage.
• Set up alerts to notify you of instances with high uptime and low activity, signaling potential inefficiencies.

One of the most effective strategies is resource scheduling - automating the shutdown of non-production workloads during off-hours to cut down on unnecessary costs. For production environments, consider consolidating services by analyzing performance data to combine workloads on fewer hosts or containers.

"Best practices are important, but there's no substitution for real measurement and cost optimization. Datadog Cloud Cost Management helped us attribute spend at a granular level over dozens of accounts to achieve significant savings." - Martin Amps, Stitch Fix

Another powerful option is rightsizing. By analyzing historical usage data, you can identify opportunities to switch to smaller instance types or implement auto-scaling. These adjustments ensure your resources are used efficiently while maintaining performance during peak demand.

6. Custom Application Metrics

System-level metrics like CPU and memory usage are helpful, but custom application metrics dive deeper, giving you a detailed look at how your applications use resources. By tracking things like how your business logic executes or how users interact with your app, custom metrics can shine a light on inefficiencies that might otherwise go unnoticed. Even when your application seems to be running smoothly, it could be wasting resources due to inefficient code or unnecessary background tasks. These insights are key to managing costs wisely.

Impact on Cost Optimization

In Datadog, the cost of custom metrics depends on the number of unique metrics tracked per hour. If not carefully managed, these metrics can drive up monitoring expenses. For example, having too many tags or unique identifiers tied to metrics can cause costs to skyrocket without adding much value. A real-world example? In 2023, Holland & Barrett managed to cut over $75,000 from their Datadog bill by refining their custom metrics strategy. It’s also worth noting that Datadog doesn’t automatically delete old custom metrics, meaning you could be paying for data no one even looks at anymore.

Relevance to Underutilization Detection

Custom metrics are particularly effective at spotting resource waste within your application. For example, tracking database connection pool usage can highlight when applications are holding onto unnecessary connections, using up memory and network resources without doing any real work. Similarly, monitoring queue depths and processing rates can reveal worker processes that are running but barely handling any tasks. These kinds of insights can help you identify and address inefficiencies at the application level.

Ease of Monitoring Within Datadog

Datadog makes it easy to submit custom metrics using methods like Agent Checks, DogStatsD, PowerShell, and API submissions. These metrics integrate seamlessly into Datadog dashboards, giving you a clear view of application-specific resource usage alongside standard infrastructure metrics. For Java applications, you can use JMX to collect metrics, with the max_jmx_metrics option providing flexibility. Starting small and monitoring how these metrics change over time can help you refine your strategy.

Actionability for Resource Optimization

Custom metrics are incredibly useful for fine-tuning resource usage. Metrics like request processing time, database query efficiency, and cache hit rates can help you identify apps that are consuming too many resources for the value they deliver. To get the most out of custom metrics, focus on tracking the essentials and avoid creating redundant or overly detailed measurements. Limiting the number of similar metrics per service can also prevent unnecessary duplication and cost inflation. Use Datadog’s Metric Summary page to regularly review and delete unused metrics that no longer provide actionable insights. A well-thought-out tagging strategy can further simplify data collection and analysis. Together, these practices enhance your overall resource optimization efforts.

7. Resource Reservation vs. Utilization

When there's a noticeable gap between the resources you've reserved and what you're actually using, it often means wasted capacity. Resource reservation refers to the CPU, memory, and storage that are allocated or guaranteed to an application, while utilization is the real-time consumption of those resources. A large difference between the two indicates you're likely paying for resources that aren't being used. This metric builds on earlier discussions about CPU, memory, and storage, giving you a more complete understanding of resource efficiency.

This concept is particularly relevant in containerized environments like Kubernetes. In Kubernetes, you define requests (the minimum resources guaranteed to a container) and limits (the maximum resources a container can use). If your pods are consistently using far less than their requested resources, you're essentially reserving capacity that could be better allocated to meet actual needs.

Impact on Cost Optimization

Understanding and addressing this gap can lead to significant cost savings. A great example comes from Complyt, a fintech company that used Datadog's tools to analyze their resource usage and uncover inefficiencies in their AWS setup. Alexander Tilkin, Cofounder and CTO at Complyt, shared:

"In an hour, we cut our total AWS costs by 40 percent. When you have a tool that's very fast, integrates with your cloud provider, and lets you understand where you spend your money, it's very easy to dig deep into utilization of your compute resources."

Small and medium-sized businesses often over-provision resources as a precaution, not realizing the potential savings from properly sizing their deployments. Datadog's Cloud Cost Management provides detailed cost data, making it easier to spot and act on these opportunities.

Relevance to Underutilization Detection

These metrics also help uncover inefficiencies in your infrastructure. For instance, in Kubernetes, you might see nodes that are oversubscribed - where the total pod limits exceed the node's available memory - even though actual usage stays low. This creates a misleading perception of resource scarcity. Similarly, when pods consistently use far less memory or CPU than requested, it results in wasted money and cluster capacity.

Ease of Monitoring Within Datadog

Datadog simplifies the process of tracking these inefficiencies. Through its Kubernetes integration and infrastructure monitoring tools, the platform automatically collects resource reservation data from Kubernetes manifests and compares it to real-time utilization metrics. This allows you to clearly see the gap between reserved and used capacity across your infrastructure. With over 850 integrations, Datadog consolidates data from AWS, Azure, Google Cloud, and on-premises systems into built-in and customizable dashboards.

Actionability for Resource Optimization

Once you've identified gaps between reservation and utilization, Datadog provides actionable insights to address them. The platform offers automated recommendations for resizing underutilized resources and flagging orphaned assets that can be removed. It also uses tagging to attribute inefficiencies to specific teams or services, helping you pinpoint the root causes. For Kubernetes environments, you can improve pod density and implement tools like Karpenter for autoscaling. Setting up alerts for unexpected usage spikes ensures you can tackle waste before it impacts your budget. Regular reviews of resource allocation help ensure your deployments align with actual demand.

For more tips on optimizing cloud performance, check out our post on Scaling with Datadog for SMBs.

8. Rightsizing Recommendations

Rightsizing recommendations provide precise, data-backed adjustments to streamline resource usage and cut down on unnecessary waste. By leveraging insights from resource reservation, these recommendations offer targeted changes to fine-tune your infrastructure. Datadog plays a key role here, analyzing historical usage patterns and comparing them with current resource allocations to suggest specific optimizations.

The issue of underutilization in modern infrastructure is widespread. For instance, over 65% of Datadog-monitored containers use less than half of their allocated CPU and memory.

Impact on Cost Optimization

These recommendations don’t just improve efficiency - they also open up opportunities for cost savings. Datadog collaborates with AWS Compute Optimizer to combine metrics like CPU, storage, I/O, and memory, enabling memory-aware EC2 recommendations. This partnership ensures users achieve peak performance without overpaying.

For containerized workloads, the potential savings are even greater. In 2020, nearly 50% of containers used less than one-third of their allocated resources. Small and mid-sized businesses (SMBs) can use Datadog's Cloud Cost Management tools to tailor these recommendations to align with their specific business needs.

Relevance to Underutilization Detection

Rightsizing recommendations go beyond basic metrics like CPU and memory usage to fine-tune resource alignment. They identify inefficiencies that manual monitoring might miss. Datadog’s process agent generates detailed CPU and memory percentile aggregations, offering a granular view of underutilization and the exact adjustments required.

Here’s a real-world example: Datadog analyzed a mysql Deployment by tracking the p95 memory usage of all mysqld processes over 36 hours. The memory request for these containers was set at 750 mebibytes. Based on the analysis, Datadog recommended increasing the memory allocation to 821 mebibytes to match the p95 usage peak.

Ease of Monitoring Within Datadog

Datadog simplifies the rightsizing process through automated tools like Kubernetes VPA and its historical data analysis capabilities. Metrics such as kubernetes.cpu.usage.total serve as the foundation for these recommendations, while process-specific metrics like proc.<NAME_OF_PROCESS>.memory.rss and proc.<NAME_OF_PROCESS>.cpu.total_pct provide detailed insights into individual application performance.

For third-party software, Datadog automatically generates metrics such as datadog.process.per_command.memory.rss and datadog.process.per_command.cpu.total_pct, eliminating the need for custom instrumentation.

Actionability for Resource Optimization

Datadog doesn’t stop at identifying inefficiencies - it bridges the gap between analysis and implementation. Tools like the cluster list help spot clusters with idle resources and high costs, while the workload list highlights specific workloads that can benefit from rightsizing. These recommendations can be applied directly within Datadog or exported to GitOps workflows for further action.

To maximize effectiveness, it’s best to avoid setting CPU limits, which can hinder usage, while ensuring memory limits are high enough to prevent out-of-memory errors. After making adjustments, Datadog APM can monitor application performance to ensure that rightsizing doesn’t negatively impact user experience. Additionally, Datadog’s Kubernetes Autoscaling combines key scaling events with node efficiency metrics to provide deeper insights into cluster performance.

9. Tag-Based Resource Grouping

Tag-based grouping organizes your infrastructure in a way that highlights underused resources. Instead of evaluating resources one by one, tags provide a broader view of usage patterns across environments, teams, and applications. This makes it much easier to identify resources that aren't being fully utilized.

Relevance to Underutilization Detection

Tags add an extra layer of context to your resource metrics, making it simpler to spot underutilization trends. For example, grouping resources with tags like environment:staging and environment:production allows you to compare their usage. You might discover that a staging environment is consuming more resources than it should. Datadog simplifies this process by automatically importing tags from platforms like AWS and Kubernetes. You can also add custom tags, such as cost_center:internal-processing-01 or customer_region:eu, to include more business-specific details.

By categorizing application services with tags like service:web-store, site:web-store.staging, and role:webserver, you can easily identify underused parts of your stack. These tags integrate seamlessly with other Datadog metrics, providing a clearer picture of your resource utilization.

Impact on Cost Optimization

Tag-based grouping isn't just about visibility - it plays a crucial role in cost optimization. For instance, Holland & Barrett saved over $60,000 on their Datadog expenses in 2025 by leveraging effective tagging. Beyond tracking costs, tags can uncover broader inefficiencies. For example, resources tagged with business_unit:internal-processing might reveal a pattern of over-provisioning within a specific department.

Ease of Monitoring Within Datadog

Datadog's unified tagging system streamlines monitoring by automatically pulling metadata from your infrastructure providers and supporting custom tags through the Agent configuration. This unified system allows you to connect metrics, logs, and traces without juggling separate tagging strategies. Service tags, in particular, make it easy to view all services tied to a specific environment using Datadog's service map, helping you quickly identify clusters of underutilized resources.

Actionability for Resource Optimization

Effective tagging takes resource management to the next level. A consistent tagging schema across all resources and services is key. Including metadata like application version, deployment ID, and owner information not only aids in troubleshooting but also supports targeted optimizations. Regularly auditing your tagging practices - resolving inconsistencies and removing unused tags - ensures a complete and accurate view of your infrastructure.

Tag variables make monitoring even more actionable by enabling dynamic alerts. These alerts automatically include detailed context about the resource or service when triggered, reducing the time it takes to address underutilization issues. This approach streamlines the path from detection to resolution, improving overall resource efficiency.

10. Historical Usage Trends

Looking at historical data over weeks, months, or even years can reveal patterns that help you understand how resources are used over time. This broader perspective is essential for making smart decisions, as it helps separate temporary spikes in activity from prolonged periods of underutilization. These long-term insights work hand-in-hand with real-time data to create a roadmap for ongoing resource efficiency.

Relevance to Underutilization Detection

By turning scattered data points into meaningful patterns, historical trends make it easier to spot underutilized resources. For example, Datadog retains process metrics for 15 months, allowing you to trace issues back to their origin, even if they started weeks ago. Additionally, the Live Processes feature provides 36 hours of detailed process-level data.

This kind of historical analysis is key to identifying resources that consistently operate below capacity. A server that shows low usage over several months is a clear candidate for optimization. On the other hand, short-term dips in usage might just be normal variations and don’t necessarily indicate a problem.

Impact on Cost Optimization

Examining historical usage trends can have a direct financial impact by uncovering long-standing inefficiencies. Datadog's usage-based pricing model means that costs can add up quickly, especially if resources are underutilized for extended periods. By analyzing months of data, you can pinpoint resources that are costing money without delivering enough value.

For example, custom metrics in Datadog are priced based on unique metric series stored per hour. If a resource generates metrics but sees minimal activity, it’s essentially draining your budget without contributing significantly. Historical trends help you calculate these inefficiencies and focus on areas with the most potential savings.

The same applies to log costs, which are billed per GB of data ingested and retained. By tracking log volumes over time, you can identify resources that generate excessive logs compared to their actual usage. This insight can guide decisions like filtering logs or consolidating resources to save money.

Ease of Monitoring Within Datadog

Datadog makes it straightforward to monitor historical trends with its integrated dashboards and visualization tools. You can create graphs for process metrics and break them down by command to determine whether unusual spikes in resource usage are caused by internal processes or third-party services. This level of detail helps uncover patterns that might indicate underutilization.

The platform’s unified service tagging system further enhances monitoring by letting you apply version tags and filter metrics to validate deployments. This capability allows you to assess how resource utilization changes after updates, migrations, or other infrastructure modifications, making it easier to connect trends to specific events.

Actionability for Resource Optimization

Historical trends don’t just provide insights - they also enable concrete actions. For instance, you can use process metrics to analyze resource consumption at the service level, helping you identify specific services that need optimization. This targeted approach ensures that you can prioritize efforts where they’ll have the most impact.

Datadog also supports documentation through notebooks, allowing you to compare recent infrastructure issues with historical data and record successful solutions. This practice turns one-time discoveries into long-term knowledge that can be referenced in the future.

Additionally, tracking your system’s performance during stress and load tests over time helps you measure whether optimization efforts are paying off. This continuous evaluation prevents regression and ensures that resources remain efficient.

Historical metrics also play a role in outage recovery and data integrity. If network issues or system errors cause data gaps, historical ingestion allows you to backfill accurate timestamps. This ensures your trend analysis stays reliable, even when unexpected problems arise.

How to View and Act on These Metrics in Datadog

Once you've identified key metrics, Datadog makes it easier to keep track of them and take action. Its platform transforms raw data into clear, actionable insights through dashboards and alerts, helping businesses of all sizes spot underused resources quickly and efficiently.

Setting Up Custom Dashboards for Resource Monitoring

Datadog provides two types of dashboards: Screenboards and Timeboards. Screenboards are great for visual storytelling, using elements like images, graphs, and logs. Timeboards, on the other hand, are designed for digging into data and troubleshooting specific issues.

You can customize these dashboards with a variety of widgets, such as:

• Timeseries charts
• Heat maps
• Query values
• Top lists
• Host maps

These tools help you visualize trends, pinpoint anomalies, and rank infrastructure usage. To make your dashboards even more effective, group widgets using filters, variables, or labels for better clarity.

Creating Proactive Alerts for Resource Waste

Datadog’s alert system lets you take a proactive approach to managing underutilized resources. You can set up metric monitors with specific alert conditions that reflect your environment's needs. For instance, to monitor unused storage, you might use the system.disk.in_use metric from the Disk integration and set thresholds based on historical data.

To avoid false alarms, you can enable "Delay Evaluation" settings in No Data Alerts. Composite monitors are another powerful tool - they allow you to combine multiple conditions, such as low CPU usage, minimal memory consumption, and limited network activity, to trigger alerts only when all factors align.

Leveraging Analytics for Historical Insights

Datadog’s analytics features go beyond real-time monitoring by offering historical data that helps identify patterns of inefficiency. The Metric Summary page is particularly useful for spotting and removing custom metrics that aren’t being used. Additionally, Notebooks let teams document infrastructure issues and successful solutions, creating a repository of knowledge for future reference .

Taking Action on Identified Inefficiencies

Once you've pinpointed underused resources, Datadog provides several strategies to address inefficiencies and streamline operations:

• Filter out low-value logs using log retention settings.
• Exclude development and test logs with exclusion filters.
• Automate the shutdown of non-production workloads during off-hours.
• In containerized setups, adjust pod density and implement autoscalers like Karpenter.
• Limit metric collection for non-critical namespaces, workloads, or services.

Best Practices for SMBs Using Datadog for Resource Optimization

Small and medium-sized businesses (SMBs) often have limited resources and smaller teams, making it essential to maximize every tool at their disposal. By using Datadog effectively, SMBs can unlock cost savings and improve system performance. Building on the metrics we've already discussed, these best practices will help you turn insights into actionable steps for continuous resource optimization.

Set Clear Alert Priorities

Not all alerts are created equal. To avoid overwhelming your team and ensure critical issues get the attention they deserve, organize alerts by their impact on your business. Here's an example of how to structure alert priorities:

This approach helps SMBs focus on what truly matters, ensuring reliability while making the most of limited resources.

Leverage Predictive Analytics for Proactive Optimization

Datadog’s predictive analytics feature can forecast future trends by analyzing historical data and accounting for seasonal fluctuations. This allows SMBs to address potential issues before they escalate. For instance, companies like Resume Points used predictive analytics to anticipate load changes, while Finout adjusted resources ahead of demand spikes.

To make the most of this feature:

• Add forecasts to your dashboards to combine historical data with forward-looking insights.
• Set up forecast alerts to receive early warnings about potential issues.

Build a Culture of Continuous Monitoring

A "monitoring-first" mindset is key to staying ahead of performance issues. Start by identifying critical endpoints and tracking traffic volume for each resource. Train your team to regularly review metrics, so they understand what normal performance looks like for your workloads. Continuous monitoring leads to faster issue resolution and helps your team detect anomalies early.

Implement Regular Threshold Reviews

As your business grows and workloads evolve, your resource thresholds should adapt too. Regularly reviewing and updating CPU and memory thresholds ensures your system remains efficient. Here's how to stay on top of this:

• Schedule monthly reviews of alert thresholds.
• Use Datadog’s cloud cost management tools to set budgets, track spending, and access optimization recommendations.
• Define custom tags to track and analyze cloud activity based on your specific operational needs.

Automate Where Possible

Automation is a game-changer for SMBs looking to save time and reduce costs. For example, automating the scaling down of non-production environments during off-hours can cut operational expenses by up to 30%. Additionally, integrate Datadog alerts with tools like Slack or Teams to streamline communication and response times.

Focus on High-Impact Optimizations First

When optimizing resources, start with the changes that will deliver the biggest cost or performance improvements. For example, focus on:

• Rightsizing oversized instances.
• Eliminating unused storage volumes.
• Optimizing database queries that consume excessive CPU.

Resource optimization is an ongoing process. Regularly monitor your systems and adjust your strategies as your business evolves. By continuously fine-tuning your cloud infrastructure with Datadog, SMBs can not only reduce costs but also position themselves for scalable growth.

For more expert tips and insights, check out Scaling with Datadog for SMBs.

Conclusion

Keeping an eye on underused resources is key to building an infrastructure that's both efficient and scalable. The ten metrics we've covered here provide SMBs with a solid framework to identify waste and improve performance using Datadog's monitoring tools.

Take this example: a startup spending $5,000 a month on cloud servers managed to cut costs by $2,000 - an impressive 40% reduction - just by pinpointing and adjusting underutilized servers. Once you've identified these underused resources, it's time to act. Rebalance instances, set targeted alerts, and leverage Datadog's Resource Policies to fine-tune your infrastructure. Even better, Datadog simplifies the process by allowing developers to kick off remediation workflows directly, saving time and effort.

With real-time visibility, you can make data-driven decisions that boost both performance and cost savings. Tracking these metrics consistently helps uncover usage patterns, identify overloaded hosts, and make smarter rightsizing decisions. Over time, what starts as short-term insights can evolve into long-term strategic advantages.

As your business grows, adopting a monitoring-first approach turns these metrics into valuable business intelligence. Datadog's unified dashboard brings everything together - your infrastructure, applications, and logs - in real time. This gives SMBs the tools they need to shift from reactive monitoring to proactive, strategic success. Begin with the metrics that align with your workloads and expand from there to optimize your infrastructure and budget.

To learn more, visit Scaling with Datadog for SMBs.

FAQs

How does Datadog's tag-based resource grouping help identify underutilized resources?

Datadog’s tag-based resource grouping streamlines the process of identifying underused resources by letting you organize and filter your infrastructure using custom tags. With this feature, you can easily group related resources, evaluate their performance side by side, and pinpoint inefficiencies across your cloud setup.

By analyzing data aggregated through these tags, you can quickly spot trends like low CPU usage or underutilized memory. This lets you act swiftly to optimize your systems, ensuring smarter resource allocation and boosting overall cloud performance. The result? Lower costs and a more efficient cloud environment.

What steps can I take after identifying underutilized resources with Datadog metrics?

To make the most of your cloud environment after spotting underutilized resources with Datadog metrics, there are a few practical steps you can take.

Start by rightsizing your workloads. This means adjusting resource allocations to align more closely with actual usage, so you're not stuck paying for resources you don’t need - or struggling with insufficient capacity.

Another smart move is to implement automatic scaling policies. These allow your resources to scale up or down automatically based on demand, ensuring smooth performance without racking up unnecessary costs.

Lastly, establish resource monitoring policies. These policies help you stay on top of compliance and make sure your configurations remain efficient as your infrastructure grows or changes.

By following these steps, you can fine-tune performance, cut down on waste, and keep your cloud costs in check.

How can tracking historical usage trends in Datadog help SMBs save costs and improve resource efficiency?

Tracking historical usage trends in Datadog allows small and medium-sized businesses (SMBs) to identify patterns in how their resources are being used. This insight helps them make informed adjustments to avoid over-provisioning, which can cut down on unnecessary spending. By analyzing these trends, companies can strike a balance between boosting cloud performance and keeping costs under control.

Additionally, this method improves the accuracy of forecasting future resource requirements. With better predictions, businesses can plan budgets more effectively and allocate resources where they’re needed most. Over time, this not only enhances system performance but also leads to meaningful cost savings.

Related posts

• 5 Key Metrics to Track with Datadog
• A Beginner's Guide to Using Datadog Effectively
• Capacity Planning with Datadog: Key Insights