Cloud cost optimization goes beyond rightsizing and reserved instances. The biggest waste hides in backup sprawl and snapshot drift. The same backup data that cuts your bill is what your compliance, analytics, and AI workloads need, which means both problems get solved in one place.
What is cloud cost optimization?
Cloud cost optimization is the practice of reducing cloud spending while maintaining the performance, security, and availability your workloads need. It combines visibility into where money goes, strategies for eliminating waste, and governance to keep costs aligned with business goals over time.
A quick distinction here: Cloud cost management and cloud cost optimization are related but different. Cost management tracks expenses, allocates budgets, and generates reports. You know where the money goes.
Optimization takes that data and acts on it: shutting down idle resources, switching pricing models, and enforcing policies to prevent waste from creeping back in.
One gives you the numbers. The other uses those numbers to drive smarter decisions. You need both, but optimization is where actual savings happen.
Cost management tells you what you are spending; cost optimization acts on that information to reduce waste and align spend with business goals.
Why cloud cost optimization matters in 2026
Cloud spending continues to climb. Gartner expects public cloud services to grow by over 21% in 2026, with the market on track to pass $1.4 trillion by the end of the decade. The growth itself is fine. The waste hiding inside that growth is the real concern.
According to Flexera's State of the Cloud Report, organizations waste roughly 30% of their cloud budgets on average, with that figure rising in environments without formal optimization. For a company spending $500,000 a month, that could represent $150,000 in waste each month, funds that could instead support product innovation.
The State of FinOps 2026 report confirms what we've been seeing firsthand: waste reduction and workload optimization ranked as the top priorities for FinOps teams for the second year in a row.
Every dollar wasted on an oversized instance or a forgotten snapshot is a dollar that could fund engineering hires, product development, or infrastructure improvements. The organizations we work with that get this right treat cloud costs as an engineering discipline that lives alongside architecture decisions and sprint planning.
The biggest cloud cost drivers (and the ones most teams miss)
Before optimizing, identify your largest cost drivers. The following sections cover the compute, storage, networking, database, AI/ML, and backup patterns that typically account for the largest share of cloud spend in enterprise environments.
Compute
Compute is typically the largest single line item. Virtual machines, containers, serverless functions, and managed Kubernetes clusters all contribute.
The waste usually comes from overprovisioning (running instances 50-100% larger than needed) and idle resources (instances that are technically running but doing nothing useful). We've seen organizations running six-node Kubernetes clusters where each worker node sat at 10% utilization. That's paying for 60 units of capacity to use 6.
Storage
Storage costs grow. Object storage (S3, GCS, Azure Blob), block volumes, file systems, and database storage all accumulate.
Backup storage is the worst offender here because most teams perceive it as pure insurance. It's a significant ongoing expense with no tangible ROI until a failure occurs, so nobody spends time optimizing it. That's exactly why the waste compounds.
The biggest offenders we come across are wrong storage tier selection (keeping rarely accessed data on premium tiers), orphaned volumes from terminated instances that nobody deleted, and log files that grow unchecked because nobody implemented rotation policies.
Networking and data transfer
Data transfer fees surprise more teams than almost any other cost category. Moving data between availability zones, across regions, or out of a cloud provider's network (e.g., egress) all incur charges.
We've worked with teams running high-availability database setups with cross-AZ replication who had no idea that replication traffic was generating thousands in monthly transfer fees. If your instances and volumes live in different availability zones, you're paying for every byte that moves between them.
Databases
Managed database services are convenient but expensive. The microservices trend has made this worse: Teams spin up separate databases for every small service because decentralized architecture is the default.
We always recommend auditing whether databases can be shared across services where possible. The savings can be significant.
AI and ML infrastructure
GPU instances, training jobs, vector databases, and inference endpoints are the fastest-growing cost category in the cloud. Teams leave notebook environments running overnight, provision premium GPUs for development work that could run on cheaper options, and accumulate dataset versions that never get cleaned up.
If you leave GPU instances running just a few hours overnight, you could waste $50-$200. That adds up fast, so make it a habit to check the ML infrastructure for idle resources regularly.
Scaling AI effectively starts with treating data as a strategic asset. Historical data for training, fine-tuning, and evaluation lives in your backups.
Teams that can access that data directly, without rehydrating full environments, spend less on both AI infrastructure and backup storage at the same time.
Backup and snapshot costs (The one most teams miss)
Here's the cost driver that almost every optimization guide ignores, and the one we spend the most time helping teams address: backup storage.
Snapshots accumulate. Retention policies drift. Teams set backup schedules to "forever" because it feels safer, and two years later, you're paying for hundreds of snapshots nobody dares delete.
Native cloud backup tools (such as AWS Backup, Azure Backup, and Google Cloud snapshots) typically charge for stored copies, retained versions, and, where applicable, data replicated across regions.
The problem compounds because backup costs are buried in your cloud bill, lumped in with general storage charges, spread across dozens of accounts, with no clear owner.
A recent survey of 150+ IT and cloud leaders found that 38% of organizations still rely on basic disaster recovery tools or have no formal backup strategy.
Based on enterprise audits and Eon’s State of Cloud Backup, backup-related costs, including snapshots, replicated copies, over-retained data, recovery compute, and transfer, frequently constitute a material portion of an organization’s cloud storage bill.
These costs often go unaddressed because ownership is fragmented across infrastructure, security, and finance.
Eon's guide on managing backup sprawl and cutting cloud storage costs breaks down the specific patterns that drive backup waste.
Cloud cost optimization strategies that work
These are the strategies we come back to again and again with clients and teams. Start with the ones that address your biggest line items first, then work down the list.
1. Rightsize your compute resources
Rightsizing means matching CPU, memory, and storage to your workloads' actual utilization.
Pull utilization data for the last 60-90 days. If an instance consistently runs at 20-30% utilization, it's a candidate for downsizing.
One thing we always warn teams about: don't rightsize based on averages alone. A workload that averages 15% CPU but spikes to 90% during peak hours requires a different approach than one that sits flat at 15%. Look at peak utilization patterns, not just averages.
Check whether newer instance generations are available too. Cloud providers regularly release updated instance families that deliver the same performance at lower prices. Upgrading from an older generation to the current one is often the easiest cost-win available.
2. Eliminate idle and zombie resources
In our experience, estimates suggest that somewhere around 15-25% of cloud resources are sitting completely idle in a typical environment. Stopped instances still incurring storage charges. Load balancers with no backends. Elastic IPs allocated but unattached.
What we typically find when auditing an environment: developers created volumes or spun up test instances months ago and forgot about them. They're technically "in use" (something might be writing logs to them), but nobody needs the data.
The fix is systematic identification and cleanup:
- Audit unused volumes and snapshots. Use native tools (AWS Trusted Advisor, Azure Advisor) to flag volumes with zero IOPS over the past 30 days.
- Schedule non-production environments. If dev and staging only get used 8-10 hours a day, shut them down during off-hours. That alone can cut development infrastructure costs by 50-70%.
- Require ownership tags. Every resource should have an owner. If nobody claims it, it's a candidate for deletion.
One pattern we see constantly is that multi-account environments guarantee snapshot waste. Native backup tooling runs per account, so a snapshot orphaned in Account 47 doesn't show up in the dashboard of whoever's running cleanup in Account 12.
The only way to catch it is a cross-account view, which native tools don't provide.
3. Use reserved instances and savings plans strategically
Reserved instances and savings plans offer discounts up to 72%, depending on commitment length and payment structure. They only make sense for stable, predictable workloads that run consistently.
Analyze your usage patterns to identify baseline capacity (the resources that run 24/7 regardless of demand). Cover that baseline with reservations. Keep variable, spiky workloads on on-demand or spot pricing.
The mistake we see most often: Teams buy reservations too early, before they understand actual usage, and end up locked into commitments that don't match reality. We always tell teams to give themselves at least 60-90 days of usage data before committing.
Enterprise agreements with cloud providers are another lever worth exploring. You can negotiate discounts on committed spend and, sometimes, specific line items, such as egress traffic costs.
4. Leverage spot instances for fault-tolerant workloads
Spot instances (or preemptible VMs on GCP) offer up to 90% discounts in exchange for the possibility that the provider reclaims them on short notice. They work well for workloads that can handle interruptions:
- CI/CD pipelines (jobs can restart if interrupted)
- Batch processing and data pipelines (stateless, resumable work)
- ML training (with checkpointing every 15-30 minutes)
- Testing and QA environments (non-critical)
For ML training specifically, spot GPU instances can reduce costs by 70% compared to on-demand, depending on the cloud provider and instance type.
5. Optimize storage tiers and lifecycle policies
Not all data needs to live on the fastest, most expensive storage tier. We've seen organizations keep years of rarely accessed data on premium storage classes simply because no one questioned the default.
Implement lifecycle policies that automatically transition data based on access patterns:
- Hot tier for frequently accessed, active data
- Warm/Infrequent Access tier for data accessed less than once a month
- Cold/Archive tier (Glacier, Archive Storage) for data kept for compliance or long-term retention
Set up automated expiration rules for temporary data such as build artifacts, test outputs, and log files. Implement log rotation so that applications don't write uncompressed debug logs to expensive volumes indefinitely.
One Glacier warning worth flagging is that the per-GB price looks cheap, but retrieval fees and a 4-hour restore latency mean Glacier only costs less for data you rarely touch. Teams put operational data there to save money, then discover the real cost the first time they need it back.
For S3 specifically, watch out for hidden costs from versioning (every overwrite creates a new version), abandoned multipart uploads, and Object Lock behaviors that prevent cleanup.
Versioning is often confused with a backup strategy, but it isn't. It protects against accidental deletions inside a bucket. It does not protect against regional outages, account-level compromises, or ransomware that targets the AWS organization itself.
Cross-region replication has its own trap: it requires versioning on both sides. Teams turn on replication without lifecycle policies and end up with 2x, 3x, sometimes 4x the storage growing forever. Eon's guide on S3 backup cost optimization covers these traps in detail.
6. Cut backup and snapshot costs
This is the strategy most optimization guides skip entirely, and it's one of the areas where we see the highest-impact savings for the least effort.
Backup cost optimization shouldn't require trading off access speed, retrieval fees, and API charges against storage price, and yet that's exactly what most native tooling forces teams to do.
Here's what we recommend:
- Audit your snapshot inventory. Identify snapshots older than your compliance requirements. If your retention policy requires 90 days but you have snapshots from 18 months ago, that's pure waste. We've seen customers save $50K+ per month from a single snapshot audit.
- Eliminate redundant copies. If you're backing up the same resource using native snapshots, a third-party tool, and manual copies, consolidate them into a single backup.
- Right-size retention windows. Development environments don't need 365-day retention. Match retention to actual business and compliance requirements.
- Use deduplication and compression. Native cloud snapshots store incrementally within a service but typically don't deduplicate or compress across workloads or accounts. Solutions that apply cross-workload deduplication and incremental storage can reduce backup storage costs by 30-50%.
- Get visibility into actual spend. Most cloud bills bury backup costs across dozens of line items. Without a dedicated view of backup spend, you can't optimize what you can't see.
NETGEAR is the clearest example of what this looks like in practice: a 35 percent reduction in backup storage costs, plus real-time visibility by instance, application, and team through Eon's Cost Explorer, which made internal chargeback possible for the first time.
Innago cut AWS backup costs by 40 percent using the same approach: consolidating snapshots, enforcing retention, and gaining visibility they didn't have before.
7. Reduce data transfer and egress fees
Architecture decisions made early on can lock you into expensive data transfer patterns. This is one of those areas where a small change in how you design things can save thousands per month. Ways to reduce transfer and egress costs:
- Keep related services in the same region and availability zone whenever possible. Cross-AZ transfer fees are low per GB, but they compound quickly with high-throughput workloads such as database replication or Kafka.
- Use CDNs to cache content closer to users, rather than serving everything from the origin.
- Use VPC endpoints to avoid NAT gateway charges for traffic between your services and cloud provider APIs.
- Audit cross-region replication. Teams often configure replication once and forget about it, even after the original use case no longer exists.
We've found that sometimes paying slightly more for compute in the right region can save significantly on data transfer. Factor transfer costs into architecture decisions, not just compute prices.
8. Implement autoscaling
Autoscaling adjusts resource capacity based on actual demand instead of provisioning for peak traffic and paying for idle capacity during off-hours.
Set up scaling policies that match your workload patterns. Use more aggressive scale-down settings for development and staging environments.
For production, configure scaling based on metrics that indicate application load (request latency, queue depth, or custom metrics), rather than relying solely on CPU utilization. For Kubernetes environments, tools like Karpenter can automatically select optimal instance types based on pod requirements.
9. Adopt FinOps practices
FinOps is the organizational practice that enables sustainable cost optimization. Without it, optimization efforts fade as teams move on to other priorities.
We've seen this happen many times. A team runs a cleanup, and six months later, the waste returns because no governance was put in place to sustain the improvements.
The core idea: Bring engineering, finance, and operations together with shared visibility and shared accountability for cloud costs.
In practice, this looks like:
- Cost allocation by team and project. Every dollar should be traceable to a team, product, or business function.
- Regular cost reviews. Monthly reviews catch trends and anomalies. Quarterly deep dives identify structural optimization opportunities.
- Showback or chargeback models. When teams see their own spending, they naturally make more cost-conscious decisions. Chargeback is ideal but complex; showback is easier and still effective.
The hard part is attribution. Most native tooling can't break backup costs down by team, application, or resource owner, which is why NETGEAR only got real chargeback working after Eon's Cost Explorer gave them per-instance and per-team visibility.
- Incentivize financial responsibility. Recognize engineers who design with cost efficiency in mind. When optimization is rewarded, it becomes part of the culture.
10. Tag everything and enforce cost governance
Without consistent resource tagging, you can't allocate costs, identify waste owners, or enforce policies.
Establish a mandatory tagging standard that covers environment (prod, staging, dev), team/owner, project, and cost center. Cloud providers offer native policy engines (AWS Service Control Policies, Azure Policy, GCP Organization Policies) that can block the creation of untagged resources.
Automate governance wherever possible. We can't stress this enough. Manual tagging gets forgotten. Manual audits happen once and fall off the priority list.
This is the problem we built Cloud Backup Posture Management (CBPM) to solve.
Eon invented CBPM to autonomously classify cloud resources, assign the right backup policies without manual tagging, and enforce them across accounts and clouds. Tagging hygiene stops depending on someone remembering to add the right label.
11. Gain full cost visibility across clouds
If your organization runs workloads across AWS, Azure, and GCP, you're dealing with three different billing models, three dashboards, and three ways to categorize spend.
Native cost tools (AWS Cost Explorer, Azure Cost Management, GCP Billing) are useful for single-cloud analysis but don't provide a cross-cloud view. Multi-cloud visibility requires either a third-party FinOps platform or significant internal tooling.
Pay particular attention to costs that span clouds but aren't visible in any single dashboard.
Backup spend is a prime example. If you're protecting resources across multiple providers, the total backup cost is fragmented across multiple bills, and no native tool provides a combined view. That's one of the problems we built Eon's Cost Explorer to solve.
Cloud cost optimization vs. FinOps: What's the difference?
Cloud cost optimization focuses on the specific tactics you use to cut waste, while FinOps is the organizational framework that makes those tactics stick.
On the optimization side, that means rightsizing resources, eliminating idle instances, optimizing pricing models and storage tiers, and controlling backup and snapshot sprawl. All with the goal of reducing cloud spending while preserving performance, security, and availability.
FinOps is an operational framework and cultural practice that maximizes business value from cloud spending by bringing together technology, finance, and business teams to make cost-aware decisions, define accountability, and enforce governance.
You can optimize costs without FinOps. Run a one-time cleanup, delete some unused resources, and downsize a few instances. But without the FinOps framework (accountability, governance, continuous review), costs creep back within months. We've watched it happen repeatedly.
The FinOps Foundation describes maturity in three levels:
- Crawl: Basic visibility, ad-hoc optimization
- Walk: Established processes, moderate automation
- Run: Full automation, proactive governance
Most organizations we work with sit somewhere between Crawl and Walk.
How to build a cloud cost optimization framework
Effective cloud optimization follows a repeatable cycle. Here's the framework you can use for success:
- Phase 1: Visibility. Consolidate spending data across all accounts, regions, and providers. Tag resources. Identify who owns what.
- Phase 2: Analysis. Identify the biggest cost drivers and focus effort where the money is. If 60% of your compute spend is in Kubernetes and 25% of your storage spend is backup snapshots, those are your starting points.
- Phase 3: Action. Implement strategies that align with your cost profile. Rightsize compute. Clean up idle resources. Optimize storage tiers. Consolidate backup policies.
- Phase 4: Governance. Set budgets, alerts, and policies that prevent waste from returning. Automate enforcement. Assign cost ownership to teams.
- Phase 5: Continuous review. Cloud environments change constantly. Monthly reviews and quarterly deep-dives keep optimization current.
The teams that sustain real cost reductions invest in Phases 4 and 5. We keep saying this because it's the most common failure mode we see. The cleanup is easy, but keeping it clean is the hard part.
Cloud cost optimization tools to know
The tooling landscape breaks down into a few categories, and we want to be straightforward about where each tool fits.
Native cloud provider tools like AWS Cost Explorer, AWS Trusted Advisor, Azure Advisor, Azure Cost Management, and GCP Billing Reports. These are free (or included) and useful for single-cloud environments, but limited in multi-cloud scenarios.
Third-party FinOps platforms like CloudHealth, Cloudability, Spot.io, Kubecost (for Kubernetes-specific optimization), and Ternary. These provide multi-cloud visibility, cost allocation, anomaly detection, and optimization recommendations across providers.
Cloud-native backup optimization platforms that specifically target backup and storage costs: visibility into backup spend, automated retention management, deduplication, and policy enforcement across cloud environments. This is the category that addresses the backup cost blind spot discussed earlier in this guide, and it's where Eon fits.
Infrastructure-as-Code and policy tools like Terraform, Pulumi, and Open Policy Agent that let you codify cost-efficient configurations and enforce governance at the provisioning layer.
No single tool covers everything. The right combination depends on your environment's complexity, your multi-cloud footprint, and the cost drivers that matter most to your organization.
Backup cost is an architecture problem
Every cost driver on this list has an obvious fix except one: backup.
The reason backup costs stay out of control is that native tooling treats each cost driver separately (storage prices, API fees, restore fees, retrieval fees, cross-account visibility), and teams end up optimizing one while the others continue to grow.
Cost Explorer can show you where it hurts. Architecture is what removes the sources.
That's the architecture we built Eon around. Backups are stored in open, queryable formats like Apache Iceberg and Parquet, so teams can run SQL, full-text search, and AI workloads directly on backup data without hydrating a restore. Compliance audits pull specific records in seconds. Analytics teams query historical data without spinning up staging environments.
Most organizations treat backup data as pure insurance: pay for it, hope you never need it, forget it until something breaks.
The shift we're pushing, and the one that goes beyond traditional cost optimization, is that the same data can be your largest cost line and your most valuable unused asset. Cutting the bill is the easy half.
The harder and more valuable half is making the data useful to the business that's already paying to store it.
What to do next
Cloud cost optimization works best when you treat it as an ongoing discipline. Start with visibility, focus effort on the biggest cost drivers, and build governance that keeps waste from creeping back.
Don't sleep on backup costs either. They're one of the largest and least-managed line items on most cloud bills. If you take one thing from this guide, look at your cloud bill this week and find your backup line items. I'd bet you'll be surprised by what's hiding there.
See what your backups are costing you
If this guide piqued your curiosity about your backup spend, Eon gives you full visibility into backup costs across AWS, Azure, and Google Cloud in one place. No spreadsheets, no digging through thousands of billing line items.
You can see exactly which accounts, resources, and clouds are driving your backup costs, spot anomalies, and identify where retention drift or snapshot sprawl is inflating your bill.
Try a demo to find out where your backup budget is going.
Frequently asked questions
How much can cloud cost optimization save?
Cloud cost optimization can typically produce 40% savings from an initial cleanup, with smaller but steady reductions as governance matures. The largest immediate wins come from removing idle resources, rightsizing compute, fixing backup and snapshot sprawl, and optimizing pricing.
What is the difference between cloud cost optimization and cloud cost management?
Cloud cost management tracks, allocates, and reports on cloud expenses, while cloud cost optimization acts on that visibility to eliminate waste, right-size resources, and enforce policies that prevent overspending.
How often should you review cloud costs?
Monthly is the bare minimum. That catches most budget anomalies and forgotten resources. If your environment scales frequently or teams deploy daily, shift to weekly reviews. The real goal is making cost checks part of your operating rhythm, not a calendar reminder you skip.
What are the biggest cloud cost optimization mistakes?
The most common mistakes are cutting costs at the expense of performance, running one-time cleanups without governance, committing to reservations too early, inconsistent tagging, and ignoring backup spend. Establishing FinOps governance and auditing backup retention regularly prevents most of these.
How do backup costs affect overall cloud spend?
Backup costs create recurring charges for storage, replication, data transfer, and recovery compute that add up quietly. Because that spend is fragmented across accounts and providers with retention policies that drift over time, it's easy to miss until the bill spikes.
What is FinOps, and how does it relate to cloud cost optimization?
FinOps is an operational framework that maximizes business value from cloud spending by aligning technology, finance, and business teams to make cost-aware decisions. It enables cloud cost optimization by supplying the visibility, ownership, and governance needed to prioritize rightsizing, pricing, and backup hygiene.
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