Most cloud teams struggle with rising bills, unpredictable workload behavior patterns, and performance demands that are constantly evolving. To ensure consistent performance, they’ll often overprovision resources to handle unplanned spikes in demand. But over time, this approach can lead to serious cloud waste from underutilized infrastructure.
This doesn’t necessarily reflect poor planning. Most teams overprovision to ensure they can always meet performance and capacity demands despite unknowns. In most cases, they’re making the best decisions they can with the information available to them.
When engineers launch new services or respond to incidents, they often lack access to cost information, so they prioritize performance and stability. Teams typically size cloud environments for peak demands, but rarely revisit those allocations once deployed. As a result, underutilized and idle resources grow and sprawl unnoticed over time, leading to millions in wasted spend.
Organizations need a clear optimization strategy that goes beyond reports and alerts, and actively helps teams manage resource usage in a smarter way. There are two paths to reducing cloud spend: rate optimization and workload optimization.
Rate optimization focuses on paying less for the resources you provision using pricing instruments like Reserved Instances, Savings Plans, or committed use discounts. Workload optimization focuses on using fewer resources by aligning compute, storage, and memory allocation with actual workload demand.
Both are essential. Workload optimization helps control how much you consume, while rate optimization reduces how much you pay for that consumption.
This blog walks you through cloud workload optimization, its basics, benefits, practical strategies and tools.
What Is Cloud Workload Optimization?
Cloud workload optimization is the practice of continuously adjusting cloud resources to match the actual needs of running workloads. The goal is to ensure that applications perform reliably without consuming more infrastructure than necessary.
This involves actions like rightsizing, removing idle and orphaned resources, adjusting autoscaling configurations, and applying schedules during inactive periods. These changes are based on usage data, not assumptions made during deployment.
Workload optimization plays a critical role in controlling cloud costs because it addresses waste at the consumption level. It ensures that teams don’t just pay less through discounts — they also avoid paying for unused capacity. In FinOps, this drives accountability, improves efficiency, and aligns cloud spend with business value.
Why Is Cloud Workload Optimization Needed?
Cloud waste remains a major challenge for organizations. According to the FinOps Foundation’s 2025 State of FinOps Report, nearly 50% of organizations list “waste reduction” as their top priority, and almost one-third of annual cloud spend qualifies as waste.
Real cost efficiency requires more than just lower rates. Workload optimization is essential because it targets the root causes of cloud waste: overprovisioned, underutilized, or unnecessary resources. Without it, businesses pay for capacity they don’t need, inflate their budgets, and misalign cloud investments with business outcomes.
How To Build a Workload Optimization Strategy
Creating a workload optimization strategy is a critical first step to gaining long-term control over cloud spend. Below, we’ll outline key steps to take when configuring cloud environments for optimal efficiency and cost-effectiveness.
1. Assess current workloads and usage patterns
Begin by analyzing how your current workloads are consuming cloud resources across compute, storage, and networking. This step lays the foundation for all optimization efforts. Use native tools provided by your cloud provider to access detailed usage and billing data:
- AWS: Cost Explorer, CloudWatch, Compute Optimizer
- Azure: Cost Management + Billing, Azure Monitor, Azure Advisor
- Google Cloud: Cloud Billing Reports, Cloud Monitoring, Recommender
These tools allow you to view resource-level consumption metrics, utilization trends, and performance baselines. Start by identifying consistently underutilized resources such as compute instances running at low CPU, idle or orphaned persistent disks, or databases with minimal activity.
You can access this data through the cloud console or export it into spreadsheets or dashboards for deeper analysis. Pay attention to usage patterns across time, look for services that remain active during nights or weekends without actual usage, or resources sized far above their typical load. These insights will guide the next steps, including rightsizing, scheduling, or decommissioning unused services.
2. Set clear optimization goals and guardrails
The next step is to establish measurable goals for what your business wants to achieve within your cloud setups. Having specific FinOps KPIs in place (such as reducing idle resource costs, improving utilization rates, or increasing forecast accuracy) gives you a clear optimization target to focus on.
At the same time as creating these goals, you also want to establish certain guardrails that prevent overoptimization. This ensures you can find the right balance between cost-effectiveness and ensuring ongoing performance and reliability.
3. Map KPIs and tradeoffs across teams
With your optimization goals in place, it’s critical to ensure that all your finance, engineering, and operations teams are aligned with them. These teams should all understand how each cloud management KPI relates to certain business outcomes and any acceptable performance tradeoffs for each.
Creating a clear map of KPIs and acceptable tradeoffs ensures that each of your teams base their cloud decisions primarily on the long-term goals of the business.
4. Prioritize workloads by impact and effort
To ensure you’re focusing on the right workload optimization initiatives, use an impact-effort matrix to categorize each task based on its priority. This lets your business identify early wins you can introduce that require little effort, but have a high impact on your cloud savings.
This methodical approach helps you avoid wasting too much time and resources trying to optimize configurations that bring minimal value to the business.
5. Align stakeholders early
When implementing your workload optimization strategies, encourage early collaboration with both technical and non-technical stakeholders. This ensures that everyone maintains shared ownership of cloud deployments and remains aligned on how to manage them.
To ensure that each of your stakeholders remains aligned with business objectives, schedule regular meetings with them. Use these opportunities to review your respective team initiatives, address any usage, resource, or architectural changes or new requirements, and opportunities to optimize them.
6. Integrate optimization into delivery workflows
Workload optimization shouldn’t be treated as a one-time project or an afterthought. To make it sustainable, it needs to be built into your everyday engineering processes. This means integrating optimization checks into CI/CD pipelines, infrastructure provisioning, and ongoing workload reviews.
For example, when new services are deployed, teams should evaluate instance sizing, storage class selection, and autoscaling thresholds as part of the delivery process. Use infrastructure as code (IaC) templates with pre-approved resource configurations that reflect your optimization standards.
Tagging policies, environment schedules, and cost anomaly alerts should be applied from day one, not retroactively. Teams should also adopt a regular cadence for reviewing workload performance and resource utilization, especially after major updates or scaling events.
By making optimization part of how infrastructure is delivered and maintained, you reduce waste before it happens and ensure that cost-efficiency becomes a shared responsibility across engineering and FinOps.
7. Monitor progress and continuously improve
Once optimization efforts are in motion, it is important to track results and adjust your strategy over time. Set a regular cadence to review key metrics like resource utilization, cost per workload, and idle spend. Use these reviews to evaluate whether your optimization goals are being met and where improvements can be made.
Involve both technical and financial stakeholders in these check-ins to ensure alignment. Use data from your cloud provider’s billing and monitoring tools to track trends, identify new inefficiencies, and validate the impact of recent changes. If certain KPIs are not improving, revisit your provisioning policies, thresholds, or workflows to find out why.
Continuous improvement ensures your workload optimization strategy stays relevant as your architecture, usage patterns, and business priorities evolve.
Proven Workload Optimization Techniques and Strategies
Once you have a workload optimization strategy in place, you can utilize a range of techniques to minimize cloud waste and improve efficiency. Below are 12 workload optimization techniques you can leverage:
1. Identify and eliminate idle or overprovisioned resources
Start by scanning your environment for resources with consistently low utilization, this includes compute instances running at less than 10% CPU, idle load balancers, unattached volumes, old snapshots, and unused IPs. These often go unnoticed but can make up a large portion of unnecessary spend.
Once identified, remove them by stopping or terminating unused instances, deleting orphaned storage, decommissioning inactive load balancers, and releasing unused IPs. Set up regular audits or automation scripts to prevent idle resources from piling up again.
2. Rightsize compute and database resources
Rightsizing means adjusting the size and type of resources to better match actual workload requirements. Start by analyzing long-term CPU, memory, and IOPS metrics using tools like AWS Compute Optimizer, Azure Advisor, or GCP Recommender. Identify instances or databases that are consistently underutilized or overprovisioned.
Once identified, switch to smaller or more efficient instance types that still meet performance needs. For high-memory or bursty workloads, consider specialized families. Regular rightsizing reviews help prevent waste from static provisioning and keep costs aligned with real demand.
3. Use auto scaling to match demand
Unlike rightsizing, which adjusts the size of a single resource, autoscaling adjusts the number of resources based on real-time demand. It helps you scale out during spikes and scale in when traffic drops, so you’re not paying for idle capacity.
Use native services like AWS Auto Scaling Groups, Azure VM Scale Sets, or Google Cloud Instance Groups to set up policies based on metrics like CPU, memory, or request count. Autoscaling ensures consistent performance while keeping usage aligned with demand.
4. Schedule non-production and inactive workloads
Unless action is taken, cloud compute resources remain running during nights, weekends, or holidays even when there is little to no demand. This is common in non-production environments like dev, test, and QA, but can also apply to production systems with predictable low activity during off-peak periods.
Start by identifying workloads with clear inactive windows. Test environments are usually the easiest to begin with. Once identified, apply schedules to automatically shut them down during inactive hours and restart them when needed. Use tools like AWS Instance Scheduler, custom scripts or automated tools like ProsperOps Scheduler to manage this process. Scheduling helps reduce runtime waste without disrupting core operations.
5. Consolidate workloads using containers or serverless architectures
Whenever possible, consolidate modernizing your VM-based deployments by using containerization services like Docker or Kubernetes. This allows you to run multiple workloads on a single VM deployment while reducing your total cloud overhead.
You can also leverage serverless functions, such as AWS Lambda, for additional cost efficiency. By being able to run code without managing servers, you only pay for the compute time you actually consume.
6. Implement storage lifecycle policies for infrequently accessed data
Not all data needs to sit in high-performance storage. Shift older or infrequently accessed data to colder, lower-cost tiers using lifecycle policies. For example, move S3 objects to Infrequent Access or Glacier, or transition Azure Blob storage from Hot to Cool or Archive tiers. This helps retain important data at a fraction of the cost without manual intervention.
7. Optimize data transfer and network traffic
Data transfer across regions, availability zones, or the public internet can silently drive up cloud costs. Review traffic patterns to identify unnecessary cross-region movement, overly chatty services, or public-facing data flows that could be internalized.
Whenever possible, keep traffic within the same region or zone, use private endpoints, and batch data transfers to reduce frequent small requests. Even minor changes in architecture can lead to significant savings over time.
8. Migrate to ARM Microarchitecture where possible
Cloud providers continuously improve cloud services, and introducing more efficient hardware is one area of improvement. The ARM microarchitecture has become more common and desirable in datacenter and cloud computing due to its improved price performance.
Compared to traditional x86 microarchitecture, ARM compute cores are less costly to run, and for some workloads, performance is also superior. Review your compute usage and migrate eligible workloads to more efficient families like AWS Graviton, Azure Dplsv5-series, or GCP Tau VMs. These options often provide better performance at lower cost, especially for scale-out or containerized applications. Compute savings as a result of this change range from 10-20%.
9. Review and manage load balancer configurations
Load balancers are critical for distributing traffic and ensuring high availability, but they come with ongoing hourly charges, even when idle. Many teams forget to decommission load balancers that are no longer connected to active services, especially after temporary environments or architecture changes.
Regularly audit your environment to identify unused ELBs, ALBs, or GCP/Azure equivalents, and remove them if they are no longer routing traffic. This avoids silent cost leaks while keeping your network configuration clean and efficient.
10. Move workloads to lower-cost regions
Depending on the region where your cloud deployments run, pricing can vary significantly. It’s important to audit your workloads to look for opportunities to reposition them to lower-cost regions.
If any of the services you’re running aren’t overly dependent on network latency, move them to other regions to take advantage of the price difference without affecting user experience.
11. Automate cost and usage anomaly detection
Sudden cost spikes or unexpected usage patterns often signal misconfigurations, forgotten resources, or inefficient deployments. Instead of relying on manual reviews, set up automated anomaly detection using tools like AWS Cost Anomaly Detection, Azure Cost Management alerts, or Budget Alerts. These tools can notify teams in real time when costs deviate from normal patterns, helping you catch issues early and respond before they cause significant waste.
12. Take advantage of Spot pricing
Spot instance pricing blurs the line between rate and usage optimization. While it offers a discounted pricing model, actually benefiting from it requires engineering effort. Applications must be architected to tolerate interruptions and variable availability, which makes Spot both a financial and technical consideration.
Since Spot instances can be reclaimed by the cloud provider with very short notice, they’re not typically used for mission-critical workloads. However, for stateless, clustered applications, batch processing jobs, or any architecture tolerant of node interruption, running on Spot instances can save up to 90% compared to undiscounted rates.
Native Tools for Cloud Workload Optimization
When implementing cloud workload optimization strategies, a wide range of native tools and services available through cloud service providers such as AWS, Azure, and Google Cloud can simplify the process. Below is a list of top native platforms you can consider:
AWS native tools
- AWS Compute Optimizer:
AWS Compute Optimizer uses machine learning to analyze historical utilization data for EC2, EBS, Lambda, and container workloads. It identifies opportunities to improve performance and reduce costs by recommending more suitable instance types, sizes, and families. Beyond suggestions, it provides performance risk scores, utilization graphs, and configuration insights that help teams confidently take action without guesswork.
- AWS Cost Explorer:
AWS Cost Explorer is a visualization and analysis tool that lets you track, filter, and break down cloud costs by service, linked accounts, tags, or usage type. While it includes rightsizing recommendations, its real value lies in trend analysis, daily usage granularity, and the ability to correlate cost spikes with specific workloads or resources. It is a foundational tool for cost transparency, budget tracking, and forecasting.
- AWS Trusted Advisor:
AWS Trusted Advisor offers best-practice recommendations to users on how to optimize their cloud resources. Trusted Advisor continuously runs in the background, flagging users across six different best practice pillars (Security, Performance, Cost Optimization, Fault Tolerance, Service Limits, and Operational Excellence), while offering them step-by-step instructions on how to implement suggested improvements.
- AWS Auto Scaling:
AWS Auto Scaling allows cloud users to dynamically adjust their resource capacity in response to real-time usage demands. The tool enables users to create their own custom business policies on how each of their EC2 instances or tasks should scale and can run continuously without any manual intervention.
- AWS Instance Scheduler:
AWS Instance Scheduler allows you to define time-based schedules for automatically starting and stopping EC2 and RDS instances. It is particularly useful for managing non-production environments or workloads with predictable usage patterns. Instance schedules can be applied based on tags and can significantly reduce runtime costs without affecting availability during business hours.
- Amazon CloudWatch:
Amazon CloudWatch is a monitoring service that cloud users can utilize to collect valuable metrics associated with their cloud environments. By leveraging the tool’s “Alarms” feature, users can create notifications that trigger when certain metrics exceed pre-assigned thresholds, alerting teams to new opportunities for cost optimization.
Azure native tools
- Azure Advisor:
Azure Advisor is a built-in recommendation engine that scans your active Azure resources and configurations to identify potential cost, performance, security, and availability optimizations. It helps detect overprovisioned VMs, unattached disks, and underutilized resources, and provides actionable guidance aligned with your current workloads. It’s most effective when used in combination with tagging and consistent resource governance practices.
- Azure Cost Management:
Azure Cost Management is a comprehensive suite that helps users monitor, allocate, and control cloud spend. It includes usage reports, budget alerts, and forecasting capabilities across subscriptions, resource groups, and tags. It integrates with Advisor to provide actionable insights like committing to Reservations, managing unused resources, and analyzing historical trends to inform cost-saving strategies.
- Azure Automation:
Azure Automation allows teams to reduce manual cloud management tasks by using runbooks and process automation. For workload optimization, it enables features like scheduled shutdowns of VMs, cleanup of unused resources, and automated patching. This helps enforce governance policies, control costs, and maintain operational consistency across environments.
- Azure Budgets
Azure Budgets enables teams to set spending thresholds across subscriptions, resource groups, or services and track usage against those limits in real time. You can define monthly, quarterly, or custom budget scopes, and configure automated alerts when usage or forecast spend approaches the defined threshold. It’s especially useful for enforcing cost controls, avoiding surprise overages, and driving accountability across teams using shared cloud resources.
Google Cloud native tools
- Google Cloud Recommender:
Google Cloud Recommender is an AI-powered cloud service that generates optimization recommendations for services like Compute Engine and Cloud SQL. By monitoring real-time Google Cloud data, Cloud Recommender provides helpful suggestions, such as rightsizing virtual machines or switching to a more cost-effective billing model, to help reduce costs and enhance cloud performance.
- Google Cloud Cost Management:
Google Cloud Cost Management offers a suite of tools to help users optimize their applications and services. Using custom billing reports, intelligent budgeting alerts, and data visualization dashboards, users access all the information they need to make smarter cloud decisions, all from one unified platform.
- Google Cloud FinOps Hub:
The FinOps Hub is a centralized dashboard that brings together cost-saving recommendations from across your Google Cloud environment. It helps FinOps teams quickly identify unused or underutilized resources, spot rightsizing opportunities, and act on optimization insights at scale. With organization-wide visibility, FinOps Hub simplifies collaboration between engineering and finance teams to drive more informed, efficient cloud decisions.
Improve Your Cloud Cost Optimization Efforts With ProsperOps
Managing cloud costs manually is complex, time-consuming, and prone to inefficiencies. While CSPs provide native cost management tools, they often require constant monitoring, manual intervention, and deep expertise to extract maximum savings.
ProsperOps automates cloud cost optimization by adapting to your usage in real time, eliminating waste, maximizing savings, and ensuring every cloud dollar is spent effectively.
ProsperOps delivers cloud savings-as-a-service, automatically blending discount instruments to maximize your savings while lowering Commitment Lock-in Risk. Using our Autonomous Discount Management platform, we optimize the hyperscaler’s native discount instruments to reduce your cloud spend and help you achieve 45% ESR or more, placing you in the top 5% of FinOps teams.
In addition to autonomous rate optimization, ProsperOps now supports usage optimization through its resource scheduling product, ProsperOps Scheduler. Our customers using Autonomous Discount Management™ (ADM) can now automate resource state changes and integrate seamlessly with ProsperOps Scheduler to reduce waste and lower cloud spend.
Make the most of your cloud spend across AWS, Azure, and Google Cloud with ProsperOps. Schedule your free demo today!
