Data Storage Costs
Data storage costs are the expenses associated with storing and maintaining data in various forms of storage media, such as hard drives, solid-state drives (SSDs), cloud storage, and tape storage. These costs are influenced by many factors, including the size of the data, the type of storage media used, the frequency of data access, and the level of redundancy required. As the amount of unstructured data generated continues to grow, the cost of storing it remains a significant consideration for many organizations. Storage is now one of the largest lines in the IT budget. 55% of enterprise IT leaders spend more than 30% of their IT budget on data storage, and 85% expect storage spending to rise in 2026, up from 59% in 2024. Source: Komprise, 2026 State of Unstructured Data Management Report
Cloud Data Storage Costs
Cloud data storage costs refer to the expenses incurred for storing data on cloud storage platforms provided by companies like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). In the cloud the level of durability and availability required are also factors when it comes to cloud storage costs. Here’s what to consider:
- Cloud data storage providers typically charge based on the amount of data stored per unit of time.
- They may assess additional fees for data retrieval, data transfer, and data processing.
- Many cloud storage providers offer different storage tiers with varying levels of performance and cost, allowing customers to choose the option that best fits their budget and performance needs.
- With the right cloud data management strategy, cloud storage can be more cost-effective than traditional hardware-centric on-premises storage.
How much are memory and flash prices expected to rise in 2026?
Gartner estimates DRAM and NAND flash annual prices will rise 125% and 234% in 2026, driven by AI infrastructure demand, and does not expect meaningful pricing relief until late 2027. Gartner calls this memory price inflation memflation. Source: Gartner, April 8, 2026 The increases have not stopped. TrendForce expects NAND flash contract prices to rise another 15% to 20% quarter over quarter in 4Q26, and enterprise SSD is the only memory category rising faster than in 3Q26. Source: TrendForce, September 30, 2026 Komprise customers also report storage lead times of 40 weeks or more and vendor quotes valid for only 24 to 48 hours. Source: Komprise, The Cheapest Capacity You Own Is Already in Your Data Center
Managing Data Storage Costs
Managing data storage costs involves making informed decisions about how to store, access, and use data in a cost-effective manner. Read the interview with Komprise Field CTO Benjamin Henry: Is there any relief from data storage costs? Here are some strategies for managing data storage costs:
- Data archiving: Archiving infrequently accessed data to lower cost storage options, such as object storage or tape, can help reduce storage costs.
- Data tiering: Using different storage tiers for different types of data based on their access frequency and importance can help optimize costs.
- Compression and deduplication: A well known data storage technique, compressing data and deduplicating redundant data can help reduce the amount of storage needed and lower costs.
- Cloud file storage: Using cloud storage can be more cost-effective than traditional on-premises storage, especially for organizations with large amounts of data and high storage needs.
- Data lifecycle management (aka Information Lifecycle Management): Regularly reviewing and purging unneeded data can help control storage costs over time.
- Cost monitoring and optimization (see cloud cost optimization): Regularly monitoring and analyzing data storage costs and usage patterns can help identify opportunities for cost optimization.
By using a combination of these strategies, organizations can effectively manage their data storage costs and ensure that they are using their data storage resources efficiently. Additionally, organizations can negotiate with data storage providers to secure better pricing and take advantage of cost-saving opportunities like bulk purchasing or long-term contracts. Read: Storage Prices are Doubling: How IT Teams are Responding. 
How to Calculate Data Storage Costs
The purchase price of primary storage is only part of what data costs to keep. A realistic cost model counts every copy and every service the data consumes, expressed per GB per month:
- Primary capacity: the cost of the NAS or flash array, amortized over its service life
- Snapshots and replicas: the extra capacity consumed by snapshot reserves and disaster recovery copies
- Backup: backup software licenses, backup target capacity, and the number of retained copies
- Software and support: storage operating system licenses and annual maintenance
- Power, cooling and floor space for on-premises systems
- Cloud charges: capacity plus retrieval, API request and egress fees
- Staff time to manage, protect and migrate the data
Multiply the per-GB cost by every copy of the data, not just the primary copy. A cold file on primary storage typically pays for primary capacity, snapshots, a disaster recovery replica and several backup copies. Tiering that file to object storage removes it from all of them at once. Hidden costs to include:
- Rehydration: block-level tiering forces tiered data back onto primary storage at the next refresh or vendor change
- Egress: moving data out of a cloud tier often carries per-GB transfer charges
- Refresh timing: buying capacity at peak flash prices because cold data filled the array early
Komprise applies your own cost model to every share and storage system, so you see current cost, cost after tiering, and savings by department or owner.
Controlling Data Storage Costs: Without and With Unstructured Data Management
| Evaluation Criteria | Without Unstructured Data Management | With Komprise |
|---|---|---|
| Cost visibility | Capacity reports per array; cost estimated in spreadsheets | Cost per GB per month applied to every share, owner and storage system across vendors |
| Cold data on primary storage | Stays on flash and is bought again at each refresh | Identified by last access and tiered by policy at the file level |
| Backup and DR copies | Every cold file is snapshotted, replicated and backed up repeatedly | Tiered files leave the primary backup and DR footprint |
| Refresh timing | Arrays fill early and force purchases at 2026 flash prices | Reclaimed capacity defers expansion; savings modeled before data moves |
| Rehydration and egress | Block-level tiering forces data back to primary at refresh | Native-format tiering with no rehydration penalty |
| Duplicate and orphaned data | Found manually, if at all | Potential Duplicates and Orphaned Data reports across vendors |
| Departmental accountability | Storage is a shared overhead line | Showback reports by department or owner, with cost after tiering |
| AI processing cost | Full shares copied into AI pipelines; every file costs tokens | Curated subsets ingested; ROT data confined before ingest |
| Migration cost | Every file moves, hot or cold | Tier cold data first, then migrate hot data 27X faster for NFS and 25X faster for SMB |
Why are data storage costs increasing right now?
Data storage costs are rising due to a global surge in demand for memory and flash storage driven largely by AI infrastructure. Enterprise SSD prices and NAND flash components have increased sharply as data centers compete for limited supply. Gartner estimates NAND flash prices will rise 234% in 2026, and TrendForce expects enterprise SSD contract prices to keep rising through the fourth quarter. As AI workloads expand and hyperscale data centers secure large portions of global memory production, IT is facing ongoing pressure from higher storage costs and longer hardware procurement cycles.
How do rising memory and flash prices impact enterprise storage budgets?
Rising memory prices directly increase the cost of enterprise storage systems such as high-performance NAS and all-flash arrays. In some cases, enterprise SSD costs have surged dramatically compared with traditional disk storage, forcing organizations to rethink storage architectures and budgets. As a result, many IT teams are prioritizing strategies such as data tiering, capacity optimization, and hybrid storage architectures to control infrastructure spending. 
Why is storage efficiency becoming a strategic priority for IT teams?
As storage hardware prices rise, organizations must focus on using expensive primary storage more efficiently. Without visibility into data usage, large volumes of inactive files accumulate on high-performance systems, increasing costs and accelerating storage refresh cycles. Komprise addresses this directly: before any data is moved, the Komprise Flash Stretch Assessment scans the environment and quantifies exactly how much primary storage capacity can be reclaimed by tiering cold unstructured data, and what the savings would be at current market prices. Komprise customers consistently reclaim 70% or more of primary storage capacity, deferring or eliminating planned refresh purchases.
How does unstructured data management help organizations control rising storage costs?
Unstructured data management helps organizations analyze file and object storage environments to identify inactive data, duplicates, and growth trends. With this visibility, IT teams can automatically tier cold data to lower-cost storage and reclaim expensive primary storage capacity. Komprise Intelligent Data Management delivers this through continuous analysis across all NAS and cloud environments, a Global Metadatabase that tracks every file regardless of which tier it occupies, and Intelligent Tiering that moves cold data automatically based on last accessed time and other policy criteria via Transparent Move Technology. Data moved to lower-cost cloud or object storage remains accessible in native format via Dynamic Links, with no disruption to users or applications and no rehydration required. This approach allows organizations to delay costly storage purchases, extend the lifespan of existing infrastructure, and keep high-performance storage focused on active workloads. Read: The Memory ‘Crisis’ Is An Opportunity for a Smarter Unstructured Data Strategy
Why is data tiering especially important during periods of rising storage prices?
When storage prices increase, inefficient storage architectures become significantly more expensive. Data tiering helps organizations minimize the amount of data that must remain on costly high-performance systems by transparently moving inactive files to lower-cost object or cloud storage. Komprise Intelligent Tiering does this automatically and continuously based on policy, so cold data never accumulates on primary storage indefinitely. Tiering policies are typically based on last accessed time, and for more precise placement decisions, Deep Analytics Actions allows a saved Deep Analytics query to serve as the input to a tiering policy, enabling IT teams to target specific datasets based on any combination of metadata and custom tag criteria. Because Komprise Transparent Move Technology stores tiered data in native format with no rehydration penalty, organizations can tier aggressively without the access trade-offs that vendor-native tiering tools impose.
How can IT organizations address rising enterprise storage costs?
Start by getting visibility into what data you actually have. Most enterprises are storing far more than they use: typically 70% of unstructured data is inactive yet still sits on expensive primary storage. Moving that inactive data to lower-cost tiers, eliminating redundant and obsolete files, and automating data lifecycle policies can reduce storage costs by up to 70% without buying additional hardware. Komprise Intelligent Data Management does this across multi-vendor NAS, object, and cloud storage.
How can I free up flash NAS capacity without buying more storage?
The fastest way is to identify inactive data on the flash-backed NAS system and tier it to lower-cost storage automatically, rather than manually cleaning up files. The intelligent tiering analyzes usage across your NAS environment and typically identifies 70%+ of primary capacity that can be freed up this way, without the rehydration penalty that comes with storage-vendor tiering. Learn about the Flash Stretch Assessment.
Stop Overspending on Data Storage with Komprise
The blog post How Storage Teams Use Komprise Deep Analytics summarizes strategies that storage teams use to deliver greater cost savings and unstructured data value to the business, including:
- Business unit metrics with interactive dashboards
- Business-unit data tiering, retention and deletion
- Identifying and deleting duplicates
- Mobilizing specific data sets for third-party tools
- Using data tags from on-premises sources in the cloud
In the blog post Quantifying the Business Value of Komprise Intelligent Data Management, we review a storage cost savings analysis that saves customers an average 57% of overall data storage costs and over $2.6M+ annually.
Plan Future Data Storage Purchases with Visibility and Insight
With an analytics-first approach, Komprise delivers visibility into how data is growing and being used across a customer’s data storage silos from on-premises to the cloud. Data storage administrators no longer have to make critical storage capacity planning decisions in the dark. They can now understand how much more storage will be needed and how to streamline purchases during planning.
Optimize Data Storage, Backup, and DR Footprint
Komprise reduces the amount of data stored on Tier 1 NAS, as well as the amount of actively managed data, so customers can shrink backups, reduce backup licensing costs, and reduce DR costs.
Faster Cloud Data Migrations
Auto parallelize at every level to maximize performance, minimize network usage to migrate efficiently over WANs, and migrate more than 25 times faster than generic tools across heterogeneous cloud and storage with Elastic Data Migration. 
Reduced Datacenter Footprint
Komprise moves and copies data to secondary storage to help reduce on-premises data center costs, based on customizable data management policies.
Risk Mitigation
Since Komprise works across storage vendors and technologies to provide native access without lock-in, organizations reduce the risk of reliance on any one storage vendor.
Deliver the Right Data to AI
AI outcomes depend on the quality and relevance of the data. Komprise identifies, classifies, and curates the right unstructured datasets across hybrid storage without requiring data migration or disruption. Eliminate the bottlenecks of traditional ETL and moves only the data you need, directly in native format, so you can feed AI/ML pipelines faster and avoid vendor lock-in. Learn more about Smart Data Workflows.
AI Workloads and Data Storage Costs
AI adds a second bill to unstructured data: the cost to process it. Inferencing is billed by the token, and every irrelevant, duplicate or outdated file that reaches a model or a retrieval pipeline adds tokens, compute and storage. Only 6% of organizations say their enterprise data is fully ready to support AI at scale. Source: Dun and Bradstreet, July 28, 2026
Three problems drive the AI share of storage cost:
- ROT data: redundant, obsolete and trivial files copied into AI pipelines alongside the data that matters
- MCP bloat: AI agents pulling far more file content into the context window than the task needs
- Full-copy ingestion: copying entire shares to cloud AI services instead of the curated subset a use case needs
The fix is data minimization: confine ROT data, curate before ingest, and let agents read metadata first. Metadata enrichment cuts AI compute and storage costs 80% or more. A Komprise healthcare customer cut digital pathology cloud storage costs 96% by curating a rolling 33 TB working set from a 1 PB source. Source: Komprise, Unstructured Data Quality’s Role in AI Inferencing Costs
Data Storage Costs FAQ
What are data storage costs?
Data storage costs are the total expenses of storing, protecting and keeping data accessible: primary storage capacity, snapshots and replicas, backup, software and support, cloud capacity and transfer fees, power, cooling, space and staff time. For unstructured data, the largest cost driver is keeping cold files on expensive primary storage and backing them up repeatedly.
How do you calculate the cost of storing data per GB?
Add the amortized cost of capacity, data protection copies, software and support, facilities and staff, then divide by usable capacity to get a cost per GB per month. Multiply by every copy of the data, including snapshots, replicas and backups, to see the true cost of keeping a file on primary storage.
What hidden costs do storage buyers miss?
The three most common are rehydration at the next refresh when data was tiered at the block level, cloud egress fees to move data out of a tier, and backup costs that scale with the size of primary storage rather than the value of the data.
How does AI affect data storage costs?
AI copies data into new pipelines and bills processing by the token, so every irrelevant or duplicate file costs twice: once to store and again to process. Curating data before ingestion and confining ROT data cuts both.
How long will storage prices stay high?
Gartner does not expect meaningful relief in memory and flash pricing until late 2027. Planning around a near-term price drop is a risky strategy; reclaiming capacity you already own is not.
How can Dell PowerScale customers reduce storage costs during current price increases?
Dell PowerScale environments accumulate large volumes of inactive data over time, and that inactive data drives up the cost of scale-out flash capacity. Tiering cold data off PowerScale to lower-cost object storage frees up expensive flash capacity and reduces the need for a costly node expansion or refresh.
How can NetApp customers cut NAS costs without relying on vendor-native tiering?
NetApp customers using FabricPool or Cloud Tiering are tied to NetApp storage for the tiered data to remain usable. File-level tiering that works independently of the storage vendor moves cold data to object storage in native format, so files stay directly accessible without rehydration and without a NetApp-specific dependency.
How can Pure Storage (EverPure) FlashBlade customers offset rising flash prices?
FlashBlade capacity is priced at a premium because it is all-flash, which makes storing inactive data on it particularly expensive. Identifying and tiering cold files off FlashBlade to lower-cost storage reclaims that premium capacity for active workloads instead of paying flash prices to store data nobody is using.
Does tiering cold data off one NAS vendor create lock-in with another?
No, when tiering works at the file level using standard protocols. Data tiered this way remains in native format and stays accessible as files, so organizations avoid trading one vendor lock-in for another while still cutting costs.
Does the same cost-saving approach work across Dell, NetApp, and Pure Storage NAS systems?
Yes. File-level tiering that operates through standard protocols works the same way regardless of NAS vendor, so organizations running Dell PowerScale, NetApp, or Pure Storage can apply one tiering strategy across their entire storage estate instead of using separate vendor-native tools with separate cost tradeoffs. 
