Workload Cost Analysis: Key Metrics to Track

Managing cloud costs is crucial for UK businesses, as enterprise cloud spending is set to exceed $1 trillion by 2026. Without proper oversight, organisations risk wasting up to 40% of their budgets on inefficiencies. Here's what you need to know:

• Key Metrics to Track: Focus on compute costs (CPU/memory utilisation), storage costs (tier pricing and usage), and data transfer costs (egress fees and inter-region charges).
• Cost-Saving Strategies: Use tagging for accurate cost allocation, automate resource scaling to match demand, and adopt data lifecycle policies to optimise storage.
• Tools to Help: Leverage cloud-native tools like AWS Cost Explorer or third-party platforms for multi-cloud environments to simplify tracking and reduce costs.

Quick Tip: Consistent tagging and monitoring of high-cost resources can cut cloud expenses by 25–50%. Start by analysing your top 10 most expensive services to uncover savings opportunities.

This article explains how to track and optimise workload costs, avoid waste, and align spending with business goals.

Unit Economics: Cloud Cost Metrics that Matter to the Business

Key Metrics to Track

Analysing workload costs means keeping an eye on specific metrics that reveal where your cloud budget is going. Think of these metrics as your financial guide, helping you navigate cloud expenses and find areas to trim costs. This becomes especially critical when you realise that over 70% of cloud spending is wasted [2], and cloud budgets are set to account for 14% of total enterprise IT spending in 2024 [2]. Let’s dive into some of the key areas, starting with compute costs - the leading expense on most cloud bills.

Compute Costs

Compute costs usually dominate cloud bills, making them a prime target for cost control. To manage these effectively, track metrics like CPU utilisation, memory utilisation, and CPU time. These figures reveal whether resources are being used efficiently or just burning through your budget.

For example, consistently low CPU usage (below 20%) can signal an opportunity to adjust resources. You might find that a development environment is running on high-cost production-grade instances unnecessarily. Monitoring these metrics can help you rightsize resources - avoiding both over-provisioning, which wastes money, and under-provisioning, which risks performance issues.

Idle resources are another area to watch. Spending on unused assets adds up, so policies for automatic downsizing or scheduling non-production environments to shut down during off-hours can save a lot. Focusing on your top 10 most expensive services often reveals that a small number of items account for the bulk of your compute costs.

Storage Costs

Storage costs can spiral out of control if left unchecked. Surprisingly, only 30% of organisations know where their cloud budget is going [3]. To manage this, track storage volumes across different tiers and understand how pricing varies between options like hot, cool, and archive storage.

For instance, AWS S3 Standard charges approximately £0.02 per GB for the first 50TB each month, while Standard-Infrequent Access costs around £0.01 per GB [4]. Archive storage, such as Azure’s Archive tier, can be as low as £0.002 per GB monthly, but frequent retrievals can drive up expenses [4].

To optimise, use data lifecycle policies to automatically move data between storage tiers based on usage patterns. Techniques like data compression and deduplication can reduce storage needs, and regular reviews of storage utilisation help identify inefficiencies. Don’t overlook backup and snapshot costs - these can balloon if retention policies aren’t carefully managed.

Data Transfer Costs

Data transfer costs can be unpredictable and escalate quickly, especially for UK businesses operating globally or across multiple regions. For example, AWS offers 100GB of free data transfer per month, but beyond that, charges are approximately £0.07 per GB for the first 50TB transferred via the public internet [5].

Inter-region transfers also vary widely. Basic fees start at around £0.02 per GB between AWS regions but can climb to £0.12 per GB in pricier locations like São Paulo [5]. For UK companies, keeping data transfers within European regions often leads to lower costs.

Data egress fees - charges for moving data out of the cloud - are typically the most expensive. Monitoring outbound data flows and applying compression to reduce transfer volumes can help keep these in check. Tools like Amazon CloudFront allow you to cache frequently accessed content closer to users, cutting down on transfer costs. Additionally, keep an eye on cross-zone data movement and set up billing alerts to catch unexpected spikes in usage.

Resource Usage and Allocation

Understanding how resources are allocated versus how they’re actually used is key to improving efficiency. Compare requested resources with actual usage to identify areas of waste. For example, resources with high peak usage but low average utilisation may be ideal for auto-scaling configurations.

Breaking down costs by service or application can highlight expensive workloads that might benefit from redesigning or further optimisation. Tracking utilisation patterns also helps with capacity planning, ensuring you’re not overspending on underused assets.

Tag-Based Cost Allocation

Tagging resources properly is essential for tracking expenses. Tags let you categorise costs by workload, department, project, or environment, giving you a clear view of where your money is going.

For example, tags can allocate costs to specific business units or projects, enabling accurate chargeback or showback reporting. This not only promotes accountability but also helps teams understand the financial impact of their decisions. Environment-based tags (e.g., production, staging, development) can also shed light on how costs are distributed across the software development lifecycle. Many organisations find that non-production environments consume a significant chunk of their cloud budget.

Regularly reviewing tagged resources ensures that your cost tracking stays accurate as your infrastructure evolves. Untagged resources should be flagged for immediate investigation to maintain full visibility of your expenses.

Methods for Tracking and Allocating Costs

Once you've identified the key metrics to keep an eye on, the next challenge is figuring out how to track and allocate costs effectively across your workloads. Getting this right can make your cloud spending much more transparent and manageable. Below, we’ll explore some methods that build on the earlier metrics to help you track costs accurately.

Tagging and Labelling Resources

Tagging is the cornerstone of accurate cost allocation. Essentially, tags are key–value pairs that categorise resources, making it easier to track where your cloud budget is being spent. Without a clear tagging strategy, it’s nearly impossible to get a proper view of your costs.

Start by creating a tagging policy that includes mandatory tags like CostCenter, Project, and Environment. Consistency is key here. As Kim Weins from Flexera explains:

Absent a tagging policy, it is common for teams or individuals within the same organisation to use variations of the same tag, which makes it extremely difficult to achieve accurate reporting. To effectively use tags for reporting and governance purposes, it is critical to create a policy that defines consistent naming conventions, including spelling, uppercase/lowercase, and spacing. [6]

To ensure consistency, use Infrastructure as Code (IaC) tools to apply tags automatically when resources are created. This approach reduces human error and helps prevent unnecessary spending. Additionally, set up automated checks to flag untagged resources and enforce tagging rules for any new resources.

Using Cloud Provider Tools

Once you’ve established a solid tagging strategy, you can take advantage of the cost management tools provided by cloud platforms. Services like AWS Cost Explorer and Azure Cost Management are designed to work seamlessly with your tagging setup.

These tools provide real-time insights into your spending, help with budgeting, and allow you to set up alerts. They also make it easier to generate automated reports, which can be used for showback or chargeback purposes - giving teams or departments visibility into their specific costs.

Third-Party Cost Management Tools

If your organisation operates in a multi-cloud or hybrid environment, you’ll likely need more advanced solutions. Third-party tools are particularly useful for managing costs across different platforms, where pricing models and billing cycles can vary significantly.

These tools go beyond what native cloud tools offer. They provide unified dashboards for tracking costs, detailed forecasting, and even automated recommendations for cutting unnecessary expenses. Many also integrate with enterprise financial systems, making it easier to align cloud spending with business goals. In fact, organisations that adopted automated cost allocation and real-time tracking reported up to a 40% reduction in unallocated or shadow IT cloud expenses over a year [7].

As your business grows and evolves, so will your cost allocation needs. Regularly reviewing and refining your methods ensures you stay on top of your cloud spending while making informed decisions that align with your organisation's goals.

Analysing Compute, Storage, and Data Transfer Costs

Once you’ve set up effective tracking methods, the next step is analysis. This is where raw cost data gets turned into practical insights, helping to trim down cloud expenses. Compute, storage, and data transfer costs each demand a specific approach to spot inefficiencies and refine usage. By focusing on these areas, you can make informed decisions to improve cost efficiency.

Compute Cost Analysis

Compute costs often dominate cloud bills, making them the first place to look for savings. Start by analysing CPU and memory usage across your instances. Many organisations find their instances are oversized and underused, leading to as much as 30% of cloud spending being wasted on idle resources [10].

Check utilisation rates to avoid paying for unused capacity. It’s also worth reviewing your pricing model. Reserved instances can cut costs by 50% to 70% for steady workloads [1], and spot instances can slash expenses by up to 90% compared to on-demand options [9][1]. Studying seasonal and usage trends can help you automate processes, like setting up autoscaling to adjust to traffic patterns, creating a system that adapts without manual intervention [1].

By combining historical data, real-time monitoring, and automated alerts for anomalies, you can identify inefficiencies early and act before they inflate your costs [8].

Storage Cost Analysis

Storage costs can spiral out of control if left unchecked, especially as data accumulates faster than it’s managed. According to the 2025 Wasabi Cloud Storage Index, 62% of companies exceed their cloud storage budgets [12].

Understanding how your data is accessed is key. While most organisations classify only 18% of their data as cold, research shows that 60% to 80% of data hasn’t been accessed in over a year [12][13]. This gap represents a major opportunity to reduce costs.

Start with a storage tier review. Compare the costs of premium storage tiers to more economical options. For example, AWS S3 Standard costs £18.40 per TB per month, whereas AWS S3 Glacier Deep Archive is just £0.79 per TB per month [12]. Moving cold data to lower-cost tiers can reduce storage expenses by 70% to 80% [13].

Don’t overlook retrieval fees. For instance, AWS S3 PUT fees are £0.004 per 1,000 requests in S3 Standard but jump to £0.04 in S3 Glacier Deep Archive [12]. Location-specific pricing and data residency rules can also influence costs [11].

Audit your storage for unused snapshots, duplicate files, and idle volumes. Automating policies to delete outdated snapshots and compress rarely accessed data can further optimise storage and cut costs.

Data Transfer Cost Analysis

After tackling compute and storage, it’s time to address data transfer costs. These are often overlooked but can significantly impact your budget, especially in multi-cloud or hybrid setups. Kevin Bogusch, Senior Competitive Intelligence Analyst at Oracle, notes:

Cloud data egress costs depend on application architecture. By designing systems with data egress in mind and taking advantage of certain technologies, organizations can reduce costs. [15]

Focus on analysing high-cost data flows. Monitor outbound network traffic and set alerts for unusual spikes, which could signal inefficient data movement.

Cross-region and cross-cloud transfers tend to be the most expensive. For example, AWS EC2 charges around £0.072 per GB for the first 10 TB of data transferred per month, while Google Cloud charges approximately £0.068 per GB for transfers between 200 GB and 10 TB from Northern Virginia [14].

Reassess your application architecture to find ways to reduce these transfers. Keeping data on the same platform, service, and in the same region as your applications can significantly cut egress costs [14]. Using content delivery networks (CDNs) and caching strategies can also reduce latency and expenses [14].

For large-scale transfers, dedicated network connections can offer predictable pricing [15]. Additionally, applying lossless compression to data in cloud lakes or lakehouses can lower transfer costs by up to 60% for analytics, AI, or machine learning workloads [14].

Common Challenges and Solutions

Even with the best intentions, analysing workload costs can run into hurdles that disrupt efficiency. These challenges are common across organisations, regardless of size, but there are proven ways to tackle them. Addressing these issues effectively can save both time and money.

Poor Tagging Practices

Inconsistent or missing tags can make it nearly impossible to perform accurate cost analysis. Without proper tags, allocating costs to specific teams, projects, or applications becomes a guessing game. This not only clouds reporting but can also lead to unchecked spending, unclear resource ownership, and even security risks.

For instance, many organisations have found that consistent tagging plays a key role in avoiding misallocated costs [16].

To overcome this, it's essential to establish a clear tagging policy. This policy should outline which resources need tagging, specify mandatory tags, and identify optional ones [17]. Automation tools like CloudFormation templates or Lambda functions can ensure tags are applied automatically when resources are created, reducing human error and improving compliance [16]. Without a robust tagging strategy, managing workloads becomes even more challenging.

Unpredictable Usage Patterns

Sudden spikes in workload demand can create unexpected cost surges, making it hard to stick to budgets [19]. Traditional capacity planning often struggles to handle scenarios like seasonal demand, viral events, or rapid business growth. Adopting autoscaling helps address this by dynamically adjusting resources based on real-time demand, ensuring you only pay for what you use [19].

Real-time alerts, combined with autoscaling and varied instance types, can help keep resource usage in line with demand [18][19]. For example, reserved instances can cover steady workloads, while spot instances handle temporary spikes at a much lower cost [18].

Budget controls add another layer of protection. Setting cloud budgets and enabling alerts ensures you're notified as spending approaches limits. This helps prevent runaway costs and keeps spending predictable [20][19]. Additionally, including buffer periods in your planning can account for unexpected events, reducing the risk of performance issues or unplanned expenses [20].

Managing usage variability becomes even more complex when dealing with multiple cloud providers, which introduces its own set of challenges.

Multi-Cloud Cost Management Complexity

Handling costs across multiple cloud providers can be tricky due to the differences in pricing models, billing formats, and discount structures. This complexity makes it difficult to get a unified view of spending. With 98% of enterprises now using multi-cloud setups [21] and 31% investing over £19 million annually in public cloud services, cost management has become a top priority for more than half of organisations [22].

Each provider handles billing differently - AWS uses CSV files, Google Cloud integrates with BigQuery, and Azure relies on APIs. These diverse methods require tailored approaches for data extraction and analysis [21]. Without a standardised method, fragmented data and inconsistent pricing can lead to overspending [22].

To simplify multi-cloud cost management, standardisation is key. Consistent tagging strategies across platforms, aligned cost categories, and a unified data model can make comparisons easier. Tools like the FinOps Open Cost and Usage Specification (FOCUS) can help standardise cost data across providers [22]. Additionally, centralised cloud spend management is essential to keep multi-cloud expenses under control [21].

For organisations struggling with multi-cloud challenges, turning to experts like Hokstad Consulting can make a difference. Their experience in cloud cost engineering and strategic migration helps establish governance frameworks and implement automation to streamline multi-cloud management.

Automation can handle critical tasks like budget alerts, autoscaling, discount management, and tagging enforcement across platforms. This reduces manual work while ensuring consistent control over costs.

Conclusion

Keeping cloud costs under control requires clear metrics, precise monitoring, and a proactive strategy.

It’s estimated that organisations waste 30–40% of their cloud budgets on poorly managed resources, but with better oversight, spending can be slashed by 25–50% [24]. Take the example of a digital media company that brought its AWS costs down from £120,000 to £42,000 by employing strategies like right-sizing, using spot instances, and optimising storage [24]. These impressive savings highlight just how important it is to adopt a structured approach.

Key practices such as consistent tagging, automated scaling, and unified multi-cloud management are essential. Not only do they ensure accurate cost tracking, but they can also help avoid up to 30% of unnecessary cloud expenses [26].

Cost optimisation is not just about saving money. It's also about making sure that your workloads are aligned with your business goals. - John Walicki, Principal Architect, AWS [27]

For UK businesses aiming to make the most of their cloud investments, expert advice from Hokstad Consulting can help align IT resources with broader business goals [23]. This kind of guidance is invaluable for building secure, efficient, and cost-effective cloud applications [27].

Creating a culture that prioritises cost awareness can lead to both innovation and reduced expenses [25]. By implementing these strategies, organisations won’t just save money - they’ll improve performance, reinforcing the principles shared throughout this guide.

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