10 Common Cloud Cost Mistakes That Drive Up Your AWS Bill

At Opslyft, the SaaS companies we work with almost all share the same blind spot: their AWS bill grows faster than their revenue, but nobody can pinpoint why. The CFO sees the trend on a quarterly slide. Engineering sees Cost Explorer once a month. Finance sees the AWS invoice. Nobody sees the connection between a code change last sprint and the $18K bump on this month's bill.

For SaaS businesses, this is not just an engineering problem. Cloud spend feeds directly into cost of goods sold, which feeds gross margin, which feeds valuation. We have seen Series B companies lose 8 to 12 points of gross margin to AWS waste, which translates to millions of dollars of valuation impact at typical SaaS multiples.

The frustrating part is that almost all of this waste comes from the same recurring mistakes. None of them are exotic. None of them require a platform engineering team. They just require someone to actually own the problem and act on it. This guide walks through the ten cloud cost mistakes we see most consistently, what they cost, and how to fix them with realistic effort.

1. Not maximizing Savings Plans and Reservations

Savings Plans and Reserved Instances are some of the most powerful levers for controlling AWS compute spend, especially for EC2, but also for other services that support reservations.

Smaller companies often assume they are not big enough to worry about these options. In reality, if you have stable, long-running workloads and are only using On-Demand pricing, you are leaving significant money on the table.

If you do not have the time or expertise to manage reservations, or if you are worried that your compute footprint will change over time, you can use an automation platform such as ProsperOps. In many cases, the savings produced by optimized Savings Plans and Reservations are much higher than the vendor fees you pay.

The key is to treat commitments as an ongoing practice, not a one-time project. Monitor utilization, adjust coverage, and let automation handle the complexity where possible.

2. Ignoring the potential of Spot Instances

Spot Instances can provide around 60 percent savings compared to On-Demand pricing. If you already use auto scaling groups or other elastic compute patterns, you should seriously consider adding Spot capacity into the mix.

You can build the Spot management logic yourself, but it is often easier to rely on specialized providers such as Xosphere or Spot.io. These vendors handle bidding, orchestration, and failover so that you can capture savings without frequent disruptions.

Again, the main idea is the same as with Savings Plans. Even after you factor in vendor fees, the roughly 60 percent reduction in compute costs can produce major net savings.

3. Treating cloud savings as only a financial responsibility

When people hear terms such as savings and cost optimization, they often assume this is the finance team’s job. Finance does play an important role, but it cannot deliver lasting savings alone.

The largest and most sustainable savings usually come from engineering. Engineers understand how your services are built, which components are critical, and where there is real waste.

With a cloud cost intelligence platform such as Opslyft, engineering teams can see the cost impact of their design decisions. They can identify which products, teams, or environments are running efficiently, and which ones are not.

Once engineers have this context, they can look for optimizations that reduce spend while preserving performance and reliability. This is where you get changes such as better instance sizing, more efficient data flows, and smarter storage strategies.

Finance and FinOps teams should guide and support the process, but engineering needs to be at the center of it.

4. Not migrating to GP3 for elastic compute storage

AWS frequently releases improved volume types, and GP3 is a good example. Many organizations still rely on GP2 EBS volumes for EC2, often deployed through infrastructure as code templates that automatically attach volumes to instances.

If you never revisit these templates, your elastic environment will keep replicating older, more expensive patterns.

Migrating to GP3 requires some testing, but it is usually straightforward. Even if the savings on each volume are not huge, at scale, you can avoid a meaningful amount of unnecessary spend.

The mistake is not evaluating GP3 at all. By doing nothing, you accept higher storage costs for no real benefit.

5. Skipping S3 Intelligent Tiering for storage

If you store large amounts of data in S3, you should at least evaluate S3 Intelligent Tiering. S3 pricing depends on several factors, including storage class, object size, retention duration, and access patterns.

With Intelligent Tiering, AWS automatically shifts objects between frequent and infrequent access tiers based on how often they are used. This allows you to keep data available while reducing storage costs over time.

This is particularly useful when you have mixed or unpredictable access patterns and do not want to manually move data between storage classes.

6. Running storage without lifecycle rules

Deploying S3 buckets or volume-based storage without lifecycle policies is a common and costly mistake. Without lifecycle rules, data simply accumulates. Nothing expires, and costs grow linearly as your usage increases.

Over time, this can become very expensive, especially for logs, backups, and temporary data that only have short-term value.

You should define lifecycle rules for each storage type based on retention needs. For example:

• Move older data to cheaper storage classes
• Delete debug logs after a defined period
• Archive infrequently accessed objects

Lifecycle policies turn storage management into a controlled process instead of an afterthought.

7. Keeping RDS snapshot retention too high by default

By default, RDS snapshots are often retained for 35 days. For many workloads, this is longer than necessary and leads to higher costs than required.

If you leave the default retention, your snapshot storage will grow and may become expensive. In many cases, lowering the retention period to seven or fourteen days is enough to meet recovery needs while significantly reducing RDS storage costs.

The important part is to align snapshot policies with your actual recovery objectives, not just with AWS defaults.

8. Ignoring the value of unit cost metrics

Most traditional cloud cost tools focus on resource-level metrics. For example, they show you that 80 percent of your monthly spend goes to EC2.

At first glance, that sounds like a clear signal. In reality, it is not very actionable. You still do not know which product, feature, environment, or customer is driving that cost.

Without unit cost metrics, you cannot answer questions such as:

• Which product or feature is responsible for most of our EC2 usage
• Whether a single customer is responsible for a large percentage of that spend
• How to reduce EC2 costs without harming important user experiences

Unit cost metrics change that. Opslyft provides a cost modeling framework that maps 100 percent of your cloud spend to units that reflect your business. That could be cost per customer, per team, per transaction, per environment, or any other dimension that matters to you.

Once you have this view, you can compare unit economics across products and customers, identify unprofitable segments, and optimize in a way that supports growth rather than blocking it.

9. Using expensive optimized volumes when you do not need them

It is easy to over-provision storage performance. Many teams default to provisioned or optimized volumes to guarantee throughput, but never validate whether they actually need that level of performance.

Optimized volumes usually cost more than general-purpose volumes. With the improvements in GP3, you now have more configuration options, including burstable performance, at lower price points.

You should make it standard practice to:

• Start with GP3 as the default
• Test workloads to confirm whether higher performance is truly required
• Question any request for more expensive volume types

Monitoring expensive storage and challenging unnecessary upgrades should be part of your basic hygiene as an engineering organization.

10. Paying for security and management services you do not actively use

CloudTrail, GuardDuty, and similar security or management services are valuable when they are integrated into your processes. However, in many organizations, they are enabled simply because the security team requested them, without a clear plan for usage.

These services can become costly over time. If you are paying for them but not actively using their data in operations or incident response, you should revisit the decision.

One effective way to bring clarity is to annualize the cost. For example, a 20,000-dollar monthly bill becomes 260,000 dollars per year. Present these numbers to your information security leadership and ask whether the organization is getting value that justifies that level of spend.

The goal is not to cut security blindly, but to ensure that every ongoing cost has a clear owner and a clear purpose.

Gain complete visibility into your cloud costs with Opslyft

Managing cloud spend can feel overwhelming, especially when you are dealing with containers, multi-tenant architectures, and multiple AWS services. It does not have to be that way.

Opslyft is a cloud cost intelligence platform that gives you clear visibility into your cloud costs and organizes spend into business-relevant views, even when your tags are not perfect. You can track costs by customer, product, team, environment, or feature, including complex setups such as Kubernetes and other container platforms.

By surfacing the right metrics and correlations, Opslyft helps your engineering, finance, and FinOps teams take meaningful action. You can identify waste, protect performance, and make confident decisions about where to invest and where to optimize.