*updated August 2026
I’ve been hearing more organizations ask whether cloud storage is simply too expensive compared with buying hardware. It’s a reasonable question—especially when cloud pricing is presented as a visible monthly number and hardware is evaluated as a one-time purchase. But that comparison often leaves out the operational costs, lifecycle risk, and capacity-flexibility tradeoffs that determine the real total cost of ownership. This post is written for infrastructure and IT leaders who need to evaluate cloud storage costs against traditional hardware purchases on equivalent terms.
You Pay for What You Control
Cloud storage often looks expensive because it exposes costs that on‑premises environments quietly hide. When you compare total cost of ownership—not just raw capacity—the story changes.
The useful comparison is not cloud capacity versus hardware capacity. It is cloud service cost versus the full cost of delivering storage as a reliable service: capacity, performance, availability, lifecycle management, facilities, labor, and risk.
Let’s start with a simple analogy, taken from a familiar example: pizza. Making pizza at home, picking it up partially prepared, ordering delivery, or dining out all result in the same end product—but the cost structure and responsibility change dramatically. Some options give you maximum control, others trade that control for convenience, consistency, and predictability. Infrastructure works the same way.
Figure 1- https://pragmaticworks.com/blog/this-week-in-data-pizza-and-the-cloud
The cloud does not look expensive because it is inefficient. It looks expensive because it exposes costs that on-premises environments often hide, underestimate, or defer. Hyperscalers also purchase hardware, power, and data center services at volumes individual organizations rarely match, so the comparison needs to account for both bundled operations and buying power. To understand the real economics, we have to look past hardware acquisition and examine what it takes to deliver storage as a service.
ksThe yellow boxes represent the layers the customer controls and pays for directly. That control can be valuable: teams can choose the brand, size, lifecycle, configuration, and cost profile that best fit their environment. But every layer of control also brings ownership responsibility, including planning, patching, monitoring, troubleshooting, support renewals, and refresh cycles.
Now let’s apply this to Azure NetApp Files (ANF). Azure NetApp Files is a PaaS storage service. With ANF, teams no longer manage storage firmware, controller upgrades, hardware refresh cycles, performance tuning under failure scenarios, or capacity planning against physical constraints.
And this model still leaves out several cost categories that are easy to treat as background expense but very real in a storage TCO analysis:
- Electricity (40-60% of the total cost of a data center)
- Cooling – Chillers, HVAC, and/or liquid cooling for high-performance systems
- Labor – Network operations, server operations, facilities maintenance, cleaners
- Network equipment
- Uninterrupted Power Supplies
- Racks, KVMs, cable arms, etc.
- Security Systems and staff
- Generators + Fuel
- Fire suppression systems
- Building rental + Parking
- Building out a data center – raised floors, additional power, etc.
In infrastructure terms: availability engineering, performance headroom, patching, lifecycle management, and incident response need to be considered —they’re either handled explicitly or quietly absorbed by your team.
1yr TCO
For simplicity, this example models a 100TB storage requirement and focuses on first-year cost categories a buyer would commonly evaluate. Actual results will vary based on performance tier, utilization, regional pricing, resiliency requirements, discounting, and how much operational cost is already allocated to the storage environment.
Once you account for those operational realities, the cost comparison becomes less theoretical. Let’s look at what this actually means for a typical 100TB deployment. On-premises, your costs would look something like this:
| Category | Cost Range |
| Hardware (Drives, controllers, shelves) | $42,000–$78,000 |
| Networking gear | $6,000–$18,000 |
| Software licensing | $12,000–$36,000 |
| Annual support (hardware + software) | $18,000–$42,000 |
| Facilities / Power / IT labor | $24,000–$52,000 |
| 8.5K-18.8K/m or 102K-226K/yr |
*Expect 20–35% higher hardware costs and 15–25% higher power/ops costs compared to 2024–2025 baselines.
That is a wide range, but the range is not a weakness of the estimate. It reflects how variable on-premises storage economics become once utilization, growth, support renewals, and failure scenarios are included.
Now compare that with Azure NetApp Files (ANF), which offers more predictable consumption-based pricing. While past pricing behavior does not guarantee future pricing, the buyer can evaluate ANF as an operating-cost model rather than a hardware-refresh commitment.
Total Estimated First‑Year Cost for 100TB ANF, East US
| Component | Estimated Cost |
| ANF Flexible Cool (75% cool) | 7.5K/m or 90K/yr |
| ANF Standard Cool (75% cool) | 8.5K/m or 102K/yr |
| ANF Flexible (no cool) | 11.3K/m or 135.6K/yr |
| ANF Standard (no cool) | 12.4K/m or 148.8K/yr |
5yr TCO
With hardware, you must buy a large enough device to last the lifespan of the hardware. Let’s consider a customer who starts with 100TB, grows 20% a year, and is comparing hardware and ANF to host their storage.
Realistically, many customers find that a large percentage of capacity can be tiered to cool storage because ANF tiers at the block level and restores in seconds. For this analysis, let’s model the numbers with 75% tiered to cool.
The tables above show that ANF can be more expensive in some scenarios, but it will often be the more cost-effective choice when utilization, tiering, operational burden, and growth uncertainty are included. Instead of committing capital up front, teams can pay monthly and adjust capacity as growth rates change.
Conclusion
A practical way to test the comparison is to ask three questions: What performance and availability level must the workload sustain? How much capacity is truly active versus cold or infrequently accessed? And which costs are already visible in the budget versus absorbed by facilities, operations, or another team? Those answers often matter more than the raw price per terabyte.
When people claim that cloud storage is more expensive than hardware, they’re often not wrong in a narrow, line‑item comparison. But that comparison rarely reflects equivalent costs. On‑premises storage absorbs power, cooling, facilities, labor, maintenance, and operational risk in ways that are difficult to quantify—and very easy to overlook. Cloud pricing, by contrast, makes those costs explicit.
When you factor in the full operational footprint, services like Azure NetApp Files stop looking disproportionately expensive and start looking predictable. That predictability—along with reduced operational burden and faster time to value—is what organizations are actually buying. The question, then, isn’t whether cloud storage costs more than hardware. It’s whether organizations are comparing the same thing at all.
For technical teams, this isn’t just a pricing discussion—it’s a decision about where operational complexity should live. Azure NetApp Files makes that complexity explicit, priced, and predictable instead of implicit, fragmented, and risky. Before declaring cloud storage “too expensive,” ask whether your comparison includes the costs you’ve already normalized.
TL;DR: Cloud storage often looks more expensive than hardware when the comparison stops at capacity price. But once power, cooling, facilities, labor, maintenance, lifecycle risk, utilization, and growth uncertainty are included, services like Azure NetApp Files can deliver a more predictable—and often more comparable—total cost of ownership. Add hyperscaler buying power and consumption-based flexibility, and the hardware-versus-cloud comparison becomes less about price per terabyte and more about where you want operational complexity to live.
