6 Unexpected Places Companies Found Cloud Cost Waste and How They Identified It
Cloud cost waste hides in surprising corners of infrastructure that many teams overlook during standard optimization reviews. This article examines six uncommon sources of cloud overspending, drawing on real-world examples and recommendations from industry experts who help organizations reduce unnecessary expenses. Learn how companies discovered waste in areas ranging from access policies to forgotten sandbox environments, and the specific methods they used to identify these hidden costs.
Harden Multi-Cloud Access Policies
One unexpected area of cloud cost waste we discovered was misconfiguration in our multi-cloud identity and access controls. We identified it during our regular architecture assessments, which always include a security review phase. That review showed inconsistent policy management across providers, so we validated findings with cloud-native tools like AWS Security Hub and Azure Defender and with third-party code and security audits. We then moved to centralize policy management and tighten identity controls to address the issue.
Rightsize QuickSight SPICE Reservations
The cost waste we didn't see coming involved AWS QuickSight SPICE capacity. It's worth understanding the mechanics on this because a lot of teams make the same mistake we did.
SPICE is QuickSight's in-memory data engine. You purchase capacity upfront based on the volume of data you plan to analyze. When we first set it up, we provisioned capacity based on the size of our datasets at the time, which was the right call.
Over the following months, we did a significant optimization of how we stored and structured our underlying data, reducing the footprint considerably. But we never went back to revisit the SPICE capacity reservation, so we were still paying for the original allocation.
The fix was simple: reduce the capacity to match the actual data size. The savings were immediate. We only caught this during a check on our QuickSight configuration. Nothing in the standard cost report flags it as a problem.
The broader lesson: service-level capacity reservations deserve the same review attention you give things like Reserved Instances and Savings Plans. QuickSight SPICE, OpenSearch reserved nodes, and similar commitments are easy to forget once configured, and that's exactly when they become waste.
Remove Orphaned Snapshots and Volumes
We have come across two areas that produce cloud waste, which had both been historically accumulated for an extended period (images of orphaned snapshots and unattached storage volumes). The DevOps team is conscientious regarding spinning down virtual machines to prevent billing; however, the costs for storage can grow considerably over time as they remain in your account after the parent instance has been decommissioned. Therefore, your cloud environment can become a long-term, digital storage unit for historical data, having little value to your organisation.
This was identified due to a noticeable rise in our storage costs on our monthly cloud bill, even when there was no change to our active compute footprint. It was an illustrative example of resource fragmentation. We conducted a deep dive audit of our infrastructure and found hundreds of snapshots that were still stored in our storage buckets, dating back several years, and as such, we were incurring ongoing costs for this data that had no users, and therefore there was no one aware of the data either.
Storage is generally thought to cost less than compute resources, and as such, people generally do not feel the same level of urgency to maintain their storage hygiene than they do with compute resources. As part of our new policy to mitigate this, we have put into place a tag policy and associated all resources with a project ID (associated with a business justification) and a lifecycle expiration tag. In addition, we now run automated weekly scripts that identify and compare existing snapshots to active instances. If a snapshot is orphaned or exceeds the retention policy, it will automatically be flagged and deleted. This change turned a previously unaccounted for growing cost into an easily identifiable and manageable portion of our IT budget.
Terminate Idle GPU Sessions
The unexpected area of cloud cost waste I discovered was idle GPU instances that were technically in use but not performing any computation.
At GpuPerHour, I monitor GPU utilization across our provider network to ensure customers are getting value from their rentals. When I started analyzing utilization data more granularly, I found that roughly 12 percent of active reservations had sustained GPU utilization below 3 percent for more than two hours. The instances were running, the meters were ticking, and customers were being billed, but no meaningful work was happening.
I identified this by building a simple utilization dashboard that flagged any session where GPU compute utilization stayed below 5 percent for 60 consecutive minutes. The pattern was consistent: a customer would start a training run, the job would finish or crash, and the instance would sit idle because nobody remembered to terminate it. In some cases, engineers had started a job on Friday and the instance ran idle through the entire weekend.
The fix on my platform side was straightforward. I added an automated notification that alerts customers when their GPU utilization drops below 5 percent for more than 30 minutes, with an option to auto-terminate after 60 minutes of inactivity. That feature reduced wasted compute hours by about 40 percent across the platform within the first month.
The broader insight is that the most expensive cloud resources are not the ones with the highest hourly rate. They are the ones that run unnoticed. GPUs at $3.50 per hour sitting idle for a weekend cost more than a month of over-provisioned storage. The waste hides in plain sight because nobody is watching utilization in real time.
Faiz Ahmed
Founder, GpuPerHour
Shut Down Dormant Non-Prod Environments
One place we found surprising waste was in non-production environments that were technically "inactive" but still fully provisioned.
These weren't obvious at first because each individual resource looked small and justified—dev databases, staging clusters, temporary test environments. But when you looked at them collectively, a lot of them were running 24/7 despite only being used a few hours a week.
We identified it by shifting from service-level cost tracking to time-based utilization analysis. Instead of just asking "what costs the most," we looked at when resources were actually being used. That exposed a clear mismatch—high uptime, low activity.
Once we saw that pattern, the fix was straightforward: auto-scheduling and expiration policies for non-prod resources. Environments either shut down outside working hours or required explicit extension if they needed to stay up.
The interesting part is that this wasn't a single big inefficiency—it was many small ones adding up. Without looking at usage patterns over time, it would've stayed hidden.
Delete Untagged Debug Sandboxes
One of the most unexpected areas of cloud cost waste I've uncovered in my organization comes from an unlikely source: our own troubleshooting workflows.
As a cloud consultant, my team regularly replicates customer environments and solutions to test configurations, reproduce issues, and validate fixes. It's a critical part of delivering quality service, but it also creates a blind spot for resource management. After a long day of debugging, it's surprisingly easy to forget to tear down the resources we spun up. Over time, these orphaned environments accumulate, inflating our monthly bill and, more concerningly, introducing potential security risks if they aren't properly configured or monitored.
To tackle this, we implemented a tag-driven governance system backed by automation. Every resource created must carry standardized tags identifying its owner and purpose. An AWS Lambda function runs on a daily schedule, scanning resources across a defined list of AWS services to verify compliance. If a resource is missing the required tags — or doesn't carry an explicit exemption tag such as "auto-remove:no" — the function sends a notification to the account owner, giving them a chance to either tag the resource or clean it up.
If the same untagged resource is still present on the Lambda function's next run the following day, it's automatically deleted. This two-strike approach balances safety with accountability: no one is caught off guard, but nothing lingers indefinitely.
Routine audits of resource ownership and tagging are the key to keeping our cloud bill in check -- and the outcome has been surprisingly positive. Since rolling out this automated tag-enforcement approach, we've saved thousands of dollars in cloud spend every month while simultaneously tightening our security posture.
