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20 Best Cloud Cost Optimization Strategies in 2025
Amid economic uncertainty, rising resource demands from technologies like GenAI, and increased focus on sustainability, it’s more important than ever to achieve cloud efficiency.
It’s no surprise that according to the FinOps foundation‘s annual survey, reducing waste or unused resources is the number one priority this year for organizations of all sizes.
That’s why we wrote this essential guide to cloud cost optimization. It covers strategies and best practices for getting visibility into your cloud spend, eliminating unnecessary costs, and getting more out of every dollar spent on the cloud.
What is Cloud Cost Optimization?
Cloud Cost Optimization ensures that the most suitable cloud resources are allocated to each workload, optimizing for performance, cost, scalability, and security. The goal is to maximize return on investment and overall business value from cloud expenditures.
Cloud environments are complex and dynamic, with unique and evolving requirements for each workload. By leveraging data, analytics, and automated tools, Cloud Cost Optimization identifies the most advantageous resource configurations and pricing models. The goal is not just to minimize waste, but to enhance the operational excellence and performance of your cloud resources.
What are Cloud Cost Components?
Understanding the primary drivers of your cloud expenses is essential for effective cost optimization.
- Compute (EC2, Lambda, etc.): Charges based on the type, size, and runtime of virtual machines or serverless functions. This is generally the largest cost, often accounting for 50–70% of total cloud spend.
- Storage (S3, EBS, etc.): Costs depend on the volume of stored data and retrieval frequency.
- Data Transfer: Outbound network traffic between services or the internet incurs charges. This can become significant in data-heavy applications with frequent cross-region transfers.
- Databases (RDS, DynamoDB, etc.): Pricing includes storage, queries, and instance runtime for managed database services.
- Licensing and Marketplace Services: Costs for third-party software or specialized tools from the cloud marketplace.
- Management and Monitoring: Expenses for tools like CloudWatch or third-party monitoring solutions. While individually small, these costs can add up across large environments.
- Networking (VPC, Load Balancers): Charges for private networking components and data flow routing.
20 Best Practices for Cloud Cost Optimization
Now let’s dive into some cloud cost optimization best practices and strategies.
Get visibility into costs
To make good decisions, business leaders (whether engineering, product or finance) need to be able to understand what cloud costs are generated and who is generating them. However, the complexities of dynamic cloud usage make it difficult to have a complete understanding of your cloud cost.
AWS provides a monthly billing file called the Cost and Usage Report (CUR) which may have hundreds of thousands, or millions, of rows of granular data on your hourly resource use. In some cases, such as EC2 instances running Linux, billing is tracked on a per-second level. With so much information available, you need a method for translating all of that raw cost data into business value.
Examples include dashboards that surface trends across services and accounts, showback reports that attribute spend to the right teams, cost allocation rules for shared resources, real-time alerts for unexpected changes, and AI-driven forecasts to predict spend before it happens and catch anomalies.
Let’s discuss some best practices for understanding your cloud costs.
#1: Tag & Allocate Cloud Costs
Step one of cloud cost optimization is connecting the functions of your business to what you’re spending in AWS each month. The goal is to fully allocate, analyze, and report cloud costs so that you understand how resources are being used and by whom. How much did an app cost to run? Is the engineering team on track for the monthly budget? Who is responsible for shared costs?
Answering these questions begins with meticulous tagging and allocation of cloud resources. Tags allow you to assign metadata to your cloud services, categorizing them by application, owner, project, team, environment, or another category important to your organization. Showbacks or chargebacks allow you to accurately attribute costs to the appropriate projects, departments, or initiatives.
There are some challenges involved in doing this manually, including untagged or mistagged resources, the difficulty of enforcing a tagging policy consistently across an organization, and the time required to tag all resources. You can also use a cost allocation tool to automatically allocate AWS costs, fix tag misconfigurations, and spread shared costs to multiple teams and business units.
#2: Monitor cloud costs (budgets, alerts)
With the rise of cloud computing, many organizations have faced significant challenges or even failure due to spiraling cloud expenses. To avoid this, implementing strict budgets and real-time cost alerts is crucial for maintaining financial health (and avoiding billing horror stories).
AWS-native tools like AWS Budgets allows you to define expected costs and usage boundaries. It also sends notifications when you’re close to or have exceeded these limits.
Eliminate Cloud Waste: Use Less
At a basic level, your cloud cost optimization strategy is to use less, and pay less. Let’s discuss using less first.
Using less means identifying and eliminating resources you don’t need — like idle EC2 instances, overprovisioned EBS volumes, unused load balancers, or Lambda functions that rarely get invoked but still incur baseline charges. It also means tuning what you do use: rightsizing compute, reducing retention periods for S3 data, and scaling down RDS instances during off-hours. Here are the key strategies:
#3: Rightsize Cloud Instances to optimize cloud costs
Amazon EC2 instances are virtual servers in Amazon’s Elastic Compute Cloud (EC2). They provide the compute power you need to run applications on the AWS infrastructure. Each instance is designed for a specific use case, that allows you to configure your infrastructure for your application needs precisely.
However, even if you choose the right EC2 instance initially, applications, environments, and demand are always evolving. Continual rightsizing of your cloud resources helps you to align your infrastructure better with actual usage, so that you don’t pay for cloud resources you aren’t using.
The first step of rightsizing includes using monitoring tools to collect key resource-level metrics on your cloud resource usage.
#4: Schedule Resources to reduce cloud costs
Scheduling resources is another key step in your cloud cost optimization strategy.
According to best practices, resources should ideally be running in the cloud only when the workload is required. Scheduling the time when a cloud environment or resources run saves both cost and environmental impact.
You might not want to ever turn off your production environments — on the other hand, your team is likely not using your pre-production (dev, test, QA) environments 24 hours a day, 7 hours a week. If you stop these environments outside of the core 8-10 hours your team works, you can potentially save 60-66% of these cloud costs.
#5: Implement Auto-Scaling Policies for Workloads
Instead of provisioning EC2 instances for peak traffic and paying for unused capacity during lulls, use Auto Scaling to dynamically match compute to demand. For scaling policies, target tracking is a strong default: scale out when CPU usage exceeds 70%, scale in when it drops below 30%. You can also use step scaling for more nuanced rules or predictive scaling for time-based patterns.
To maximize cost savings and stability:
- Use health checks to detect and replace unhealthy instances.
- Set cooldown periods to prevent rapid scaling in both directions.
- Group instances by workload type and distribute across Availability Zones.
Auto Scaling helps you reduce waste, absorb traffic spikes, and only pay for what you use—if configured correctly. Consider solutions like Compute Copilot, which optimizes instance selection using real-time pricing, commitments, and ML-based Spot interruption forecasts.
#6: Consider Serverless Architecture
Traditional compute models require provisioning resources in advance—even if they sit idle. Serverless architectures, like AWS Lambda, Google Cloud Functions, or Azure Functions, let you run code in response to events without managing servers or paying for idle time. You’re billed only for actual execution time, making it ideal for bursty, short-lived workloads.
Serverless also scales automatically with demand, reducing the need for manual rightsizing or scaling policies. While it’s not a fit for every use case—long-running, stateful, or latency-sensitive workloads may still need EC2 or containers—it’s a powerful way to cut waste for stateless APIs, background jobs, and event-driven processing.
#7: Eliminate idle resources to reduce cloud costs
AWS accounts often accumulate unused EC2 instances over time. In a dynamic cloud environment, it’s not uncommon to spin up EC2 instances and then forget about them (due to workload migrations, auto-scaling misconfigurations, developmental tests, discontinued projects, or other reasons). And cloud providers charge for these idle resources, even if you’re not using them.
The good news is that for every dollar saved on an idle instance, you also save two more dollars in corollary charges such as storage, network and database charges.
Another common culprit of cloud waste is unused EBS volumes — if not regularly identified and deleted, these can quickly accumulate and inflate your cloud costs.
#8: Cost optimization of storage
Storage costs are another key target for optimizing cloud costs. Evaluate your storage needs and make use of different storage types and classes to optimize costs. For example, infrequently accessed data can be moved to cheaper storage solutions like Amazon S3 Glacier, while keeping frequently accessed data on higher-performance (and cost) options. Or, if your usage patterns changes, you can use S3-Intelligent Tiering to automatically track your usage and select the most cost-effective storage tier.
Another way to optimize your storage is to migrate to more cost-effective options, such as from GP2 to GP3. GP2 and GP3 are general-purpose AWS EBS volumes, with GP2 being the older generation and GP3 the newer. GP3 volumes generally cost up to 20% less compared to GP2 volumes with the same storage size.
#9: Reduce Data Transfer fees in your cloud environment
If you’ve used a cloud provider like AWS, you’ve likely incurred data transfer expenses. They are easily overlooked amidst the many other line items on your Cost and Usage Report — but if left unchecked, these costs can accumulate and can be a major hidden cause of high AWS bills.
Many companies unwittingly incur hefty data transfer charges, potentially spending millions of dollars every year. Migrating data to and from a public cloud can be expensive. AWS charges for data transfer based on the following factors:
- Source and destination regions
- Type of data transfer
- Type of service (S3, EC2, RDS, etc.)
- Amount of data transferred
#10: Identify and investigate cost anomalies
You can identify unexpected spikes in cloud spend with a cloud cost intelligence tool like AWS Cost Anomaly Detection, which utilizes Machine Learning to identify unusual spending patterns in a user’s AWS account. The tool leverages emails or Amazon SNS to deliver alerts.
Once you’ve identified cost anomalies, you’ll need hourly visibility into your cloud spend to perform a root cause analysis. For example, say that your networking costs have significantly increased. With daily visibility, the increase is clear, but the reason why is not.
On the other hand, with hourly visibility, the spikes in traffic are clear — making it easy to identify the culprit. A particular process or job is triggering at these specific times to drive unnecessary costs (in this example, by misrouting internal traffic through an external interface).
Leverage AWS Discounts & Credits: Pay Less
Using less cloud resources is just half the equation in your cloud cost optimization efforts.
One key strategy to optimize cloud costs is to effectively leverage your cloud provider’s pricing system, such that you pay less for the exact same cloud resources.
That means using Reserved Instances or Savings Plans for predictable workloads, choosing Spot Instances for fault-tolerant jobs, and taking advantage of free tier usage where applicable. It also includes running on cost-optimized compute options like Graviton instances, shifting from EC2 to managed services like Fargate or Lambda when appropriate, and deploying in regions with lower pricing when performance requirements allow.
#11: Reserved Instances and Savings Plans
AWS offers three pricing models for cloud resources:
- On-Demand: Pay as you go with no commitments; typically the most expensive option.
- Savings Plans (SP): Commit to a specific usage amount for a reduced rate over 1 or 3 years.
- Reserved Instances (RI): Pre-purchase capacity for 1 or 3 years at a lower cost than On-Demand, with options for partial upfront or all upfront payment for even more savings.
Savings Plans and Reserved Instances apply hourly on a use-it-or-lose-it basis. The basic strategy is to use Reserved Instances and Savings Plans to optimize costs for steady, long-running workflows that you can easily predict in advance.
For more aggressive savings and additional flexibility, automated tools for commitment management can use ML and AI to predict optimal commitment purchases and buy back any unused commitments.
#12: Spot Instances
Another more aggressive cloud cost optimization strategy is to make use of Spot instances. AWS Spot Instances are spare AWS capacity that users can purchase at a heavy discount from On-Demand (up to 90% off). However, AWS does not guarantee that you’ll be able to use a Spot instance to the end of your compute needs. When a user willing to pay the full On-Demand price emerges, AWS can terminate these instances with a two-minute warning (known as a Spot Instance Interruption).
These terminations must be handled gracefully to avoid downtime, making Spot usage an advanced-level cloud cost optimization technique (unless you have a tool to help).
However, the hefty discounts offered by Spot instances mean that if used effectively, they can be a major part of your cloud cost optimization strategy.
#13: Move Workloads to Cheaper Regions
Cloud pricing varies by region—sometimes by as much as 20–40%. Moving non-critical or latency-tolerant workloads to lower-cost regions like us-east-2 (Ohio) or eu-west-1 (Ireland) can yield significant savings with no change in performance.
Ideal candidates include dev/test environments, batch processing jobs, backups, and internal tooling. Just be mindful of added data transfer fees when workloads span regions. A small architectural shift can unlock material cost savings, especially for workloads that don’t need to be near your users or data sources.
#14: Get AWS Credits on your cloud services
AWS credits are automatically applied to bills to help cover costs that are associated with eligible services. Let’s talk about a few common ways to leverage AWS credits in your cloud cost optimization strategies.
1. AWS Migration Acceleration Program (MAP): Offers financial incentives, including credits, to help enterprises reduce the costs of migrating existing workloads to AWS, providing expertise, tools, and training for effective migration.
2. AWS Activate Program: Specifically designed for startups, this program provides AWS credits, technical support, and training to help start and scale their cloud infrastructure. It’s available in different packages depending on the startup’s needs and association with certain incubators, accelerators, or venture capital firms.
3. Well Architected Framework Report: A process where customers can work with AWS to review their workloads and application frameworks. WAFRs can help with cloud cost optimization. And as a plus, AWS may offer credits for implementing WAFR reviews and fixes.
#15: Consider AWS Enterprise Discount Program (EDP) / Private Pricing Agreement (PPA)
If you’re an enterprise cloud user with a demonstrated history of significant AWS cloud usage (typically $1+ million per year), joining AWS EDP might be a valuable way to optimize cloud costs. It offers a discount on total AWS billing, which increases based on total spend and the length of the commitment period, typically 1-5 years).
These discounts are designed to reward long-term, high-volume use of AWS resources and foster enduring partnerships between AWS and its enterprise customers. The biggest advantage of an Enterprise Discount Program is that it allows companies with large-scale AWS use to pay less for AWS cloud services as their usage scales.
FinOps Strategies for Continuous Improvement
FinOps (a term which comes from combining Finance and DevOps) is the set of cloud financial management practices that allow teams to collaborate on managing their cloud costs. Engineering, Finance, Product and Business teams collaborate on FinOps initiatives to gain financial control and visibility, optimize cloud computing costs and resource ROI, and facilitate faster product delivery.
#16: Optimize over time
The FinOps “Crawl, Walk, Run” framework is a phased approach to implementing financial operations best practices in cloud cost management.
During the “Crawl” phase, organizations focus on gaining visibility into cloud spending and usage to establish basic control. As they transition into the “Walk” phase, they implement more sophisticated management and cloud cost optimization strategies.
Finally, in the “Run” phase, organizations optimize their cloud spend in a continuous and proactive manner, using advanced techniques like automation and predictive analytics to maximize cost efficiency and business value.
#17: Establish a Cloud Cost Ownership Culture
Cost optimization isn’t just a tooling problem—it’s a people and process challenge. FinOps maturity begins with cultural buy-in: teams across engineering, finance, and product must understand that cloud cost management is about accelerating business value, not just cutting spend.
To build a culture of ownership, start by giving engineers visibility into the cost of the resources they manage. Make cost metrics as accessible and routine as performance metrics—something teams review during sprint planning and postmortems. Encourage cross-functional rituals between finance and engineering to align on tradeoffs and budget impact. As the culture matures, engineers begin initiating cost discussions, product teams factor infrastructure into pricing, and finance helps drive cost predictability, not just reporting. When everyone feels accountable for cloud spend, cost-efficiency becomes embedded in how you build and scale.
#18: Conduct Regular Cloud Cost Audits
Cloud environments are dynamic—teams ship changes weekly, services scale automatically, and new tools are constantly being adopted. That’s why one-time cost reviews aren’t enough. Regular cloud cost audits are essential to assess how your FinOps practice is maturing, where inefficiencies are creeping in, and which teams or capabilities need deeper support.
A structured audit doesn’t just track spend—it evaluates cost allocation hygiene, commitment utilization, operational processes, and team accountability using lenses like knowledge, adoption, metrics, and automation. Start small: pick a target group (a product team, business unit, or capability) and use a capability lens from the FinOps Framework to baseline maturity. From there, establish a cadence for reassessment and evolve KPIs tied to business outcomes—not just spend trends. The goal isn’t perfection—it’s continuous improvement. By identifying where your organization stands and where it needs to go, audits become a tool for aligning cloud spend with long-term value creation.
#19: Integrate Cloud Costs into CI/CD Pipelines
By integrating cost visibility into CI/CD pipelines, engineering teams can evaluate the financial impact of infrastructure changes before they ship. This helps shift cost optimization left, enabling developers to catch inefficient design decisions early.
Start by embedding cost estimation tools into your infrastructure-as-code workflows. For example, surface projected costs during a pull request, or block deployments that exceed budget thresholds. Tag new resources at deploy time to ensure traceability, and feed cost data back into dashboards engineers already use. The goal is to make cost awareness a natural part of the software development lifecycle, reducing surprises later in production and reinforcing a culture of accountability.
#20: Cloud Cost Optimization Tools
While cloud providers offer tools for monitoring your cloud spending and recommendations for cost saving opportunities, actually implementing these optimizations often requires significant engineering time and resources. That’s why automation tools are a key part of your cost optimization strategy. Let’s dive into the top options.
nOps
To make it easy for engineers to understand and optimize cloud resources, nOps created an all-in-one automated platform for every stage of your cloud cost optimization journey.
At nOps, our mission is to make it fast and easy for engineers to take action on reducing costs. The all-in-one nOps platform includes:
- Business Contexts: understand 100% of your AWS and Kubernetes costs with cost allocation, visibility down to the node or container level, automated tagging, reports & dashboards
- Compute Copilot: intelligent workload provisioner, Spot & Commitment Management for 50% total savings
- Rightsizing: rightsize EC2 instances and Auto Scaling Groups
- Storage Optimization: One-Click EBS volume migration
- Resource Scheduling: automatically schedule and pause idle resources
- Kubernetes Management & Visibility: automated container rightsizing, binpacking, node & container visibility, resource efficiency, workload troubleshooting all in one UI
- Well Architected Review: automate and streamline your AWS Well-Architected Review
Join our customers using nOps to understand your cloud costs and leverage automation with complete confidence by booking a demo today!
AWS Cost Explorer:
A native AWS tool that provides cost tracking, forecasting, and recommendations for managing Reserved Instances and Savings Plans effectively. Best for teams already using AWS-native tools.
Apptio Cloudability:
Focuses on cloud financial management, providing detailed budgeting, forecasting, and FinOps insights. It bridges the gap between financial tracking and engineering recommendations.
Spot by NetApp:
Automates Spot Instance management with machine learning, dynamically scaling and shifting workloads for optimal pricing. Great for leveraging the Spot market without sacrificing reliability.
Densify:
Uses machine learning to analyze workload patterns and optimize cloud resources automatically. Particularly effective for Kubernetes environments and multicloud setups.
Kubecost:
Provides real-time cost monitoring and optimization for Kubernetes, with insights into cost allocation by namespace and deployment. Ideal for teams heavily invested in containerized workloads.
ProsperOps:
Automates commitment management by purchasing and selling Reserved Instances and Savings Plans based on usage. A hands-off approach to maximizing prepaid savings while maintaining flexibility.
Flexera:
Offers visibility and governance for cloud spend, with automated cost-saving recommendations and robust cost policies. Scalable for large, complex organizations needing granular insights.
Check out 20+ Cloud Optimization Tools for the full list.
Future of Cloud Cost Optimizations
1. Sustainability as a Core Metric
Cloud providers and enterprises alike are prioritizing sustainability in cloud cost strategies. Optimizing workloads for energy-efficient infrastructure, choosing regions with lower carbon footprints, and leveraging providers’ sustainability tools are becoming critical. For example, AWS offers sustainability dashboards to track carbon emissions from cloud usage, helping organizations align financial savings with environmental goals.2. Kubernetes Dominance
Kubernetes has now been adopted by the most However, managing costs in Kubernetes environments requires tools and strategies that understand the intricacies of containerized workloads. Features like resource quotas, horizontal and vertical pod autoscaling, and efficient node scaling are increasingly becoming essential to avoid overspending while maintaining performance.3. Next-Generation Autoscaling Tools
Traditional autoscaling is being augmented by more intelligent and granular solutions. Multi-Dimensional Pod Autoscaling has been proposed as a way to enhance cluster efficiency by scaling multiple workloads collectively, while Karpenter is emerging as a sophisticated tool to dynamically provision nodes based on workload requirements. These solutions represent a shift toward more efficient resource utilization that addresses both cost and performance.4. Increased Spending Driven by GenAI and Machine Learning
Generative AI and advanced ML workloads are pushing cloud spending to unprecedented levels due to their compute and storage demands. Managing these costs requires a nuanced approach, including Spot instance utilization, rightsizing GPU clusters, and balancing On-Demand versus Reserved commitments. And absolutely key is choosing the right model provider, as different providers may offer similar performance for very different cost levels. Organizations are increasingly turning to optimization strategies tailored to these workloads to reduce costs.5. AI and ML in Cost Optimization
The rise of AI/ML also extends to cost optimization itself. Tools like nOps leverage machine learning to identify idle resources, predict optimal scaling patterns, manage Spot instances, and more. These innovations go beyond simple cost reporting, enabling proactive cost control that adapts dynamically to changes in workloads and business priorities.
nOps was recently ranked #1 with five stars in G2’s cloud cost management category, and we optimize $2 billion in cloud spend for our customers. Join our customers using nOps to understand your cloud costs and leverage automation with complete confidence by booking a demo today!
Frequently Asked Questions (FAQ)
How can cloud computing reduce cost?
Cloud computing reduces cost by eliminating the need for large upfront hardware purchases and letting you pay only for what you use. Instead of maintaining idle infrastructure, you can scale resources up and down as needed. Managed services also reduce operational overhead, saving on staffing and maintenance costs. However, savings are not automatic — cost control requires using the right resource types, scaling correctly, and shutting down idle workloads.
How to reduce Cloud Run cost?
To reduce Google Cloud Run costs, start by right-sizing CPU and memory allocations — overprovisioning drives up charges fast. Use concurrency settings to process multiple requests per container instance when possible. Set idle timeouts aggressively to shut down instances quickly when not in use. Take advantage of committed use discounts if you have steady traffic. Use regional endpoints that match your users’ locations to avoid unnecessary networking charges. Finally, monitor request patterns closely — inefficient app designs, excessive cold starts, or unnecessary background tasks can all inflate Cloud Run costs.
Why is Cloud Run so expensive?
Cloud Run can be expensive because you’re billed for vCPU, memory, and request volume — including idle time between requests if concurrency isn’t optimized. Overprovisioning resources, running too many low-traffic instances, and inefficient cold start behavior all stack up costs quietly. Also, traffic spikes without autoscaling limits can unexpectedly drive up bills.
How to optimize cost in cloud?
Cost cost savings start with visibility — you can’t control what you can’t see. Tag resources for better tracking, monitor usage patterns, and set budgets with alerts. Rightsize instances, use autoscaling, and shift workloads to cheaper options like Spot Instances or serverless where appropriate. Take advantage of long-term pricing discounts like Reserved Instances or Committed Use Discounts when workloads are predictable. Delete unused resources aggressively — idle storage, unattached IPs, and zombie VMs are common sources of waste. Automation tools like nOps can help continuously track and optimize spend.
