How to Reduce AWS Cloud Costs: 15 Proven Strategies

Amazon Web Services makes infrastructure incredibly easy to scale. Unfortunately, AWS spending can scale just as quickly.

A developer launches a powerful server for a new application. A staging environment is created for a temporary project. A Kubernetes cluster is deployed. A database receives extra capacity “just to be safe.” Before an upgrade, somebody creates another snapshot.

Six months later, the project is gone. Some of the infrastructure is not. AWS keeps billing.

This is why cloud cost optimization should not start with arbitrary budget cuts. The objective is not to make infrastructure as cheap as possible. It is to identify where an organization is paying for capacity, services or resources that provide little or no additional business value.

In practice, AWS overspending often falls into several categories: resources are oversized, resources are no longer being used, non-production infrastructure runs 24/7, storage accumulates indefinitely, network architectures generate avoidable charges, or predictable workloads continue to be purchased at On-Demand rates.

A structured AWS cost optimization program examines each of these areas. The following 15 strategies provide a practical framework for reducing AWS cloud costs while protecting availability, security and application performance.

Why AWS cloud costs become difficult to control

The cloud removes friction. That is one of its greatest advantages.

A team no longer needs to purchase a physical server, wait for delivery, install hardware and negotiate capacity months in advance. Infrastructure can be created in minutes.

Financially, however, removing friction has a side effect. Creating infrastructure becomes much easier than removing it.

As companies grow, their AWS environments can therefore accumulate abandoned load balancers, obsolete snapshots, forgotten VPC endpoints, unnecessary NAT Gateways and empty Kubernetes clusters.

A second problem is overprovisioning. Engineering teams frequently size infrastructure conservatively when an application is first launched. This is understandable. Nobody wants a production outage because a server was slightly too small.

The mistake is not creating a safety margin. The mistake is never reviewing that safety margin again.

A successful AWS cost optimization program therefore asks four recurring questions:

• Which resources are larger than necessary?
• Which resources are no longer required?
• Which resources really need to run 24 hours a day?
• Which predictable workloads are still being purchased at On-Demand prices?

Those questions form the foundation of the following 15 AWS cost-saving strategies.

1. Right-size EC2 instances

Amazon EC2 is one of the most obvious places to begin an AWS cost optimization program.

A common scenario looks like this: an engineer selects an instance with eight vCPUs and 32 GB of memory because the future workload is uncertain. The application launches successfully. Months later, monitoring shows that CPU utilization rarely approaches the available capacity.

The company is effectively paying for a sports car and driving it at city-center speeds.

AWS Compute Optimizer analyzes resource configuration and utilization metrics and can recommend more appropriate EC2 instance types. A serious assessment should not rely on average CPU alone. Peak CPU, memory, network throughput, disk I/O, latency and recurring workload patterns all matter.

A lookback period of at least roughly 30 days is a useful starting point. Longer periods are appropriate for seasonal or monthly workloads. Maximum utilization is particularly important because reducing CPU capacity can materially change utilization percentages.

An instance with:

• 8 vCPU / 32 GB RAM
• may potentially become 4 vCPU / 32 GB RAM,
• if the application needs the memory but consistently leaves CPU capacity unused.

After every rightsizing change, monitor performance, latency, error rates and peak utilization.

Rightsizing should be treated as a recurring discipline rather than a one-time project. Workloads grow, shrink and change. New instance generations appear with different price-performance characteristics.

For a cost audit, start with EC2 resources flagged as overprovisioned, verify the recommendation against CloudWatch metrics, and then involve the technical owner. The goal is not to find the smallest possible instance. It is to find the lowest-cost instance that reliably satisfies the required performance envelope.

2. Consolidate unnecessary NAT Gateways

NAT Gateways allow resources in private subnets to initiate connections to external destinations while remaining inaccessible directly from the public internet.

In production environments, deploying NAT Gateways across multiple Availability Zones can be an intentional availability decision. The problem occurs when the same architecture is automatically duplicated in development, testing and staging environments.

A non-critical development environment may end up with several NAT Gateways even though high availability is not required. AWS charges for NAT Gateway availability and for the amount of data processed.

Start by using AWS Cost Explorer to identify accounts with significant NAT Gateway spending or running hours. Then review the architecture with the responsible engineering or DevOps team.

Ask one simple question: Does this environment genuinely require Multi-AZ NAT Gateway redundancy?

For some non-production systems, reducing three gateways to one can lower recurring costs without materially changing business risk. Multiple gateways inside the same Availability Zone deserve particular attention because they can add fixed cost without automatically delivering a meaningful resilience benefit.

Risk still matters. Production systems with strict availability requirements should not be converted into single points of failure purely to reduce a bill. Development, testing and temporary staging environments may have a very different risk profile.

A useful FinOps principle is therefore: availability architecture should match workload criticality. Applying production-grade redundancy to every environment is an expensive default.

3. Delete unused NAT Gateways

Consolidating active NAT Gateways is useful. Finding gateways that are not being used at all is even better.

The pattern is common: a project is created, a NAT Gateway is provisioned, the project ends, the servers disappear and the gateway survives. AWS continues charging for the resource.

CloudWatch metrics such as BytesOutToDestination can help determine whether meaningful traffic is still passing through the gateway. If virtually no traffic has occurred over an extended period, the resource deserves investigation.

That does not mean it should immediately be deleted. The gateway could support a monthly process, a rarely used integration or a deliberately maintained standby environment.

The correct workflow is therefore:

• analyze traffic,
• identify the owner,
• confirm the business or technical purpose,
• verify dependencies,
• and only then delete the gateway.

If the NAT Gateway is removed, check whether an associated Elastic IP is also no longer required. A forgotten gateway often points to other resources associated with the same retired project.

Larger AWS estates can automate this process. A recurring report showing NAT Gateways with minimal traffic, missing owner tags or unusually long lifetimes can dramatically reduce the effort required to find waste.

4. Remove obsolete VPC endpoints

VPC endpoints provide private connectivity between workloads inside a VPC and supported AWS services. They can improve both architecture and security.

But an endpoint that once made sense can become unnecessary after the application that used it has disappeared. This is especially relevant for interface endpoints that can generate recurring charges while they exist.

Review AWS → VPC → Endpoints. For every relevant endpoint, determine:

• Which service does it connect to?
• Which application owns it?
• Is traffic still flowing through it?
• Does the original application still exist?

CloudWatch metrics such as BytesProcessed can provide useful evidence.

There is an important limitation: a completely abandoned endpoint may not always produce useful current traffic metrics. Utilization data should therefore be compared with infrastructure inventories, historical data and application ownership information rather than used in isolation.

A practical audit exports all interface endpoints and enriches the inventory with account, region, VPC, service, owner, project, creation date and observed traffic. Endpoints without a clear owner or visible use can then be reviewed with the relevant teams.

Do not delete endpoints blindly. Central security, logging or management services may be less obvious than application-specific endpoints and still be important.

5. Clean up old RDS snapshots

Snapshots are essential for backup and recovery. Forgotten snapshots are not.

Engineering teams routinely create manual Amazon RDS snapshots before upgrades or major database changes. The upgrade succeeds. The snapshot is never used again. Months turn into years.

Manual snapshots can remain indefinitely, which means storage associated with them can continue contributing to AWS costs.

Review AWS → RDS → Snapshots → Manual and sort snapshots by creation date and size. Interesting candidates include snapshots that are several years old, snapshots associated with databases that no longer exist and snapshots for which nobody can identify a recovery requirement.

Before deleting anything, consider regulatory requirements, internal retention policies, disaster recovery, audit requirements and the actual recovery architecture.

The cost should also be validated first. Depending on the RDS configuration, some backup storage may already be included, meaning an old snapshot does not automatically represent incremental cost.

The correct policy is not “delete old snapshots.” It is: delete snapshots for which the organization can no longer demonstrate a valid retention purpose.

A formal retention policy makes this much easier. Snapshots can be classified as short-term change backups, monthly backups, annual backups or regulatory backups. Every class receives an owner and maximum retention period.

This approach reduces cost while also lowering the risk of deleting backups that are genuinely required.

6. Delete empty EKS and Kubernetes clusters

Amazon Elastic Kubernetes Service is extremely powerful. It is also an environment in which infrastructure can become fragmented quickly.

Imagine a project team creating a dedicated EKS cluster. The project ends. Applications are removed. Nodes are terminated. The cluster itself survives.

During an AWS cost audit, inventory every EKS cluster and examine what is actually running behind it.

Check whether the cluster has:

• managed Node Groups,
• Fargate Profiles,
• self-managed EC2 nodes,
• active workloads,
• a documented owner and business purpose.

A cluster with no meaningful compute infrastructure is a strong signal that the environment may have been abandoned.

Deleting the cluster itself may not be enough. An old Kubernetes environment can leave behind load balancers, IP addresses, Security Groups and storage.

Active clusters also deserve internal cleanup. Old namespaces, persistent volumes and application-specific load balancers can survive long after the original workload is gone.

For organizations with many Kubernetes teams, FinOps should be combined with platform governance: standardized tags or labels, expiration rules for temporary environments and clear ownership for each cluster and namespace.

A discontinued application rarely leaves behind just one billable resource. Audit the full infrastructure stack.

7. Right-size Amazon RDS databases

EC2 is not the only environment in which companies overprovision capacity. Databases are frequently oversized as well.

A business might pay for a relatively powerful RDS instance even though the actual workload uses only a fraction of available CPU and memory. Because database instances can be expensive, rightsizing a small number of RDS resources can produce meaningful savings.

Begin with AWS Cost Explorer. Identify the databases and accounts generating the largest RDS costs. Then review CloudWatch metrics such as CPUUtilization and FreeableMemory.

Database analysis should generally use a longer observation period than a simple development server. Month-end processing, quarterly reporting, scheduled data pipelines or seasonal events may create peaks that do not appear within a short window. Roughly 90 days is often a reasonable starting point.

If CPU remains low even during peak periods and substantial memory remains consistently available, a smaller RDS instance class may be appropriate.

Before changing anything, consider Read Replicas, Multi-AZ configurations, I/O, storage type, connection counts, performance requirements and existing commitments or Reserved Instances.

Theoretical savings are irrelevant if the change simply leaves prepaid capacity unused. Likewise, a cheaper instance is not economical if it creates latency or resilience problems.

A controlled change should define success criteria and compare CPU, memory, read/write latency, database connections and application response time before and after the modification.

8. Remove obsolete Site-to-Site VPN connections

Site-to-Site VPNs commonly connect AWS environments to corporate offices, external networks or private data centers.

Infrastructure changes. Connections do not always disappear with it.

A data center may have been decommissioned two years ago while the corresponding AWS VPN configuration continues to exist. Or a tunnel remains technically available but transfers virtually no useful data.

Review AWS → VPC → Site-to-Site VPN Connections. Then investigate CloudWatch metrics including TunnelState, TunnelDataIn and TunnelDataOut.

A tunnel that has remained down for weeks should be investigated. So should a tunnel that remains technically active but transfers almost no meaningful data.

Before deletion, ask whether the connection is genuinely obsolete or deliberately retained for disaster recovery. A 30-day observation period may be useful, but rarely used emergency connections require additional documentation rather than simple traffic-based decisions.

If the VPN is confirmed as unnecessary, remove it and review associated gateways or networking components.

From a governance perspective, every long-lived connection should have a documented purpose. A disaster-recovery tunnel may have very little traffic, but the organization should still know why it exists, when it was last tested and who owns it.

9. Delete unused load balancers

Load balancers route incoming traffic to backend targets. They are essential when applications are active. They are unnecessary when the application has disappeared.

Unused load balancers often remain after old services, abandoned development environments, migrations or completed blue-green deployments.

Review AWS → EC2 → Load Balancers. Then ask:

• Does the load balancer have listeners?
• Are Target Groups configured?
• Do those Target Groups contain actual targets?
• Are requests or bytes being processed?
• Is an active DNS record still pointing to the load balancer?

A load balancer without listeners is a clear warning sign. A listener that ultimately points to no active target is another.

In some cases, the infrastructure configuration alone provides enough evidence to flag the resource without requiring months of traffic history. The owner should still validate that the resource is unnecessary.

After removal, review associated Target Groups, Security Groups, certificates and DNS records. Migrations often leave several dependent resources behind.

For larger AWS estates, a monthly report of load balancers without targets, requests or owner tags can be one of the highest-value infrastructure hygiene checks.

10. Right-size ElastiCache

Amazon ElastiCache provides high-speed caching through technologies such as Redis and Memcached.

Applications can use caches to avoid repeatedly querying slower databases or recomputing frequently requested information. Like EC2 and RDS, however, ElastiCache is often provisioned with a large safety margin that remains untouched for years.

Two particularly useful dimensions are memory and CPU. CloudWatch provides metrics such as FreeableMemory. For Redis environments, EngineCPUUtilization can be especially relevant because generic CPU measures do not always represent Redis engine utilization accurately.

If a cache permanently leaves a substantial proportion of memory unused and CPU peaks remain low, investigate whether a smaller node type could support the workload.

After a change, monitor latency, cache hit rate, evictions, connections, network throughput and replication behavior.

For Redis clusters, also consider whether the number and size of shards match actual demand. A cluster can be overprovisioned even when no individual node looks completely idle.

Existing reservations or commitments should be considered before modifying the architecture. The economically best configuration is determined by utilization, performance and purchasing model together.

11. Automatically stop development and test environments

One of the simplest cloud cost questions is also one of the most powerful: Why is the development server running at 3:00 a.m.? And why is it running on Sunday?

Many development, QA, training and internal test environments are only needed during normal working hours. Yet companies frequently leave them online 168 hours per week.

Automated schedules can stop EC2 and RDS resources outside defined usage windows and start them again before the workday begins. An environment needed five days per week for roughly ten hours per day may run only around 50 hours instead of 168.

Not every workload can be scheduled. Global development teams, overnight batch workloads, integration tests and production systems may require continuous operation.

But traditional office-hours development environments are obvious candidates. Resource tagging makes the approach significantly more scalable.

Instead of relying on engineers to remember to shut down infrastructure every evening, the organization turns cost control into an automated platform policy. Developers can still be given a controlled way to extend a schedule for exceptional work.

The major organizational benefit is consistency. Savings no longer depend on individual habits.

12. Use Savings Plans for predictable baseline usage

Once waste has been removed and resources have been right-sized, organizations should examine how they purchase the capacity that remains.

Running predictable workloads entirely at On-Demand rates may be unnecessarily expensive.

AWS Savings Plans provide discounted pricing in exchange for a commitment to a specified level of compute usage for one or three years. Depending on the model and term, discounts compared with On-Demand pricing can be significant.

But sequencing matters. Do not make a long-term commitment to an inefficient environment.

A better order is:

• establish cost visibility,
• remove unused infrastructure,
• right-size resources,
• determine the stable baseline,
• then purchase the appropriate commitment.

AWS Cost Explorer can provide recommendations based on historical usage. Organizations should evaluate multiple scenarios and avoid maximizing coverage mechanically if the business is volatile or major migrations are planned.

Commitments are not a substitute for cost optimization. They are the final purchasing layer on top of an already efficient infrastructure footprint.

13. Optimize S3 with Intelligent-Tiering and lifecycle policies

Amazon S3 storage can appear inexpensive at the individual gigabyte level. At enterprise scale, storage accumulation becomes a different story.

Companies frequently retain years of logs, exports, backups, old application data, temporary uploads and historical object versions. Not all of those objects need to remain in S3 Standard.

AWS provides multiple S3 storage classes for different access patterns. S3 Intelligent-Tiering is particularly useful where access patterns are unknown or change over time because objects can move into lower-cost access tiers based on actual access behavior.

Lifecycle policies can go further by applying explicit transition or deletion rules.

Potential candidates include:

• historical logs,
• expired exports,
• old backups,
• temporary files,
• older object versions,
• completed project data.

Retrieval charges, minimum storage durations and operational requirements still need to be evaluated.

Organizations should also examine versioning and incomplete multipart uploads. Old object versions and abandoned uploads can consume significant storage without being obvious to application owners. Lifecycle policies can clean these up automatically.

Good storage optimization is not about moving everything into the cheapest storage class. It is about matching storage economics and retention to actual business usage.

14. Evaluate AWS Graviton

Rightsizing asks: How much compute capacity does this workload require?

Graviton introduces another question: Which processor architecture should provide that capacity?

AWS Graviton processors are ARM-based processors designed by AWS specifically for cloud workloads. For suitable workloads, Graviton-based instances can provide attractive price-performance compared with equivalent x86 configurations.

However, migrating to Graviton requires technical validation. Applications, container images, dependencies and libraries need to support the ARM architecture.

A sensible migration process therefore starts small:

• select an appropriate workload,
• create an ARM-compatible environment,
• validate application and dependency compatibility,
• benchmark performance,
• measure cost per transaction, request or batch,
• then expand deployment.

For large EC2, Kubernetes or container fleets, percentage improvements that appear modest can translate into significant absolute savings.

Graviton evaluation can also be combined with rightsizing. A migration is an opportunity to move not only from x86 to ARM, but also to a newer generation or a more appropriate instance size.

15. Optimize data transfer and network architecture

One AWS cost category deserves significantly more attention than it usually receives: networking.

An application architecture can be technically correct while still creating unnecessary data-processing and transfer charges.

Consider private workloads accessing Amazon S3 or DynamoDB through a NAT Gateway. Gateway VPC Endpoints can allow relevant traffic to reach those services without using the NAT Gateway.

For workloads transferring substantial amounts of data, the difference can become meaningful.

Cross-Availability-Zone traffic also deserves review. If an application regularly moves large volumes between Availability Zones, placement of compute, databases, caches and NAT Gateways can have measurable financial consequences.

For internet-facing content, Amazon CloudFront may offer another lever. Caching can reduce the number of requests that reach the origin and improve the economics of distributing large volumes of content.

A useful audit builds a network-cost matrix: Which applications generate the highest data-transfer, NAT, inter-AZ or internet-egress costs? Then evaluate whether architecture, endpoints, caching, compression, region placement or data locality can be improved.

An AWS cost audit should therefore never stop at compute and storage. Network topology has financial consequences.

Where should you start when reducing AWS costs?

Trying to optimize every AWS resource simultaneously is rarely efficient.

Start with AWS Cost Explorer and identify the largest cost categories. Ask:

• Which services generate the highest monthly bill?
• Which AWS accounts or business units generate the spending?
• Which categories have grown fastest during the last three to twelve months?

Then prioritize financially significant opportunities.

Saving 30 percent on a service costing $100 per month matters less than saving five percent on infrastructure costing $100,000 per month.

A strong AWS cost optimization process therefore combines two perspectives. Top-down analysis identifies major cost centers. Bottom-up analysis then identifies inefficient individual resources within those areas.

This prevents highly skilled engineers from spending several days optimizing infrastructure that contributes almost nothing to the overall AWS bill.

Each initiative can also be scored across three dimensions: financial potential, technical risk and implementation effort. High-potential, low-risk and low-effort opportunities should move to the top of the roadmap.

AWS cost optimization must become a continuous process

There is one limitation to every cloud cost reduction project: waste grows back.

A perfectly optimized AWS environment today can become inefficient again within twelve months. New projects launch. Developers provision infrastructure. Traffic patterns change. Applications migrate. Employees leave. Old systems lose their owners.

For this reason, cloud cost management needs to become operational discipline rather than a one-time exercise.

Useful mechanisms include regular cost reviews, clear resource ownership, consistent tagging, budget alerts, anomaly detection and recurring rightsizing reviews.

One particularly useful governance rule is simple: Every material AWS resource should have an identifiable owner and business purpose.

If nobody can explain why a resource exists, that does not automatically mean it should be deleted. It does mean the resource deserves investigation.

FinOps works best when Finance, Engineering and management share the same data. Engineering understands technical criticality, Finance sees the cost drivers, and management can define priorities and acceptable risk.

This turns cloud cost optimization into a joint management discipline rather than a cost-cutting exercise imposed on IT.

Frequently asked questions about reducing AWS costs

What is the fastest way to reduce AWS costs?

Start with unused infrastructure and non-production environments. Deleting genuinely abandoned resources produces immediate savings, while scheduling development and testing infrastructure can reduce runtime without redesigning an application. The next step is usually rightsizing expensive EC2, RDS and ElastiCache resources.

How do I identify oversized EC2 instances?

Use AWS Compute Optimizer together with CloudWatch metrics. Avoid relying exclusively on average CPU utilization. Examine peaks, memory, network utilization, disk performance and an observation period that represents the real workload.

Are AWS Savings Plans always worth it?

No. Savings Plans are most effective for predictable baseline usage. If usage is uncertain or rapidly declining, a large commitment can reduce flexibility. Infrastructure should ideally be cleaned up and right-sized before the organization commits to long-term capacity.

Should unused AWS resources be deleted automatically?

Automation can help, but automatic deletion requires strong safeguards. A resource with little traffic may still support disaster recovery, compliance or an infrequent process. Ownership, dependencies and purpose should be understood before destructive actions occur.

Which AWS services typically offer the greatest cost-saving opportunities?

The answer depends on the architecture, but EC2, RDS, EKS, ElastiCache, S3, NAT Gateways, load balancers, backup storage and data transfer frequently deserve close attention. Large organizations should prioritize categories according to actual billing data rather than assumptions.

How often should AWS costs be reviewed?

Cloud cost management should be continuous. At minimum, larger environments benefit from monthly cost reviews and recurring deeper audits. Fast-growing environments may require weekly monitoring of significant changes and anomalies.

Conclusion: Reducing AWS costs requires continuous infrastructure discipline

There is no single setting that permanently solves AWS overspending.

Meaningful savings usually come from combining multiple strategies.

EC2 and RDS resources are matched to real utilization. Unused NAT Gateways, VPC endpoints, snapshots, Kubernetes clusters, VPN connections and load balancers are removed. Development environments only run when somebody actually needs them. S3 data moves into appropriate storage classes. Suitable workloads migrate to Graviton. Predictable baseline capacity is covered by Savings Plans or other appropriate commitments. Network and data-transfer costs receive the same scrutiny as compute.

The underlying principle is straightforward: Do not pay for cloud capacity simply because it exists. Pay for infrastructure that creates measurable technical or business value.

Organizations that continuously challenge the gap between the resources they purchase and the resources they actually need can turn AWS cost optimization from an occasional cost-cutting exercise into a permanent competitive advantage.

Sources and technical basis

Technical basis: the user-provided “Cloud.docx” document containing the original ten audit measures. Additional AWS references:

• AWS Compute Optimizer – EC2 rightsizing
• AWS NAT Gateway pricing
• Instance Scheduler on AWS
• AWS Savings Plans
• Amazon S3 Intelligent-Tiering
• AWS Graviton
• VPC Gateway Endpoints
• Amazon CloudFront