Mindaugas Čaplinskas is Cofounder and Strategic Advisor of IPRoyal, a leading residential proxy provider.
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Since almost all companies, to some extent, now use and collect data, decisions around data strategy have become more consequential. But controlling the data pipeline is no longer just about your company’s resources; it’s also about building a strategic moat.
Casual users may treat data collection tools as simple commodities, but whether companies need to consider this data a strategic asset depends on a variety of factors. Proxy servers were, at first, considered a commodity just like cloud servers or hosting services. But their place might be shifting and require reevaluation.
Virtualized Computing Resources
Infrastructure-as-a-service (IaaS) is the practice of renting virtualized computing resources rather than owning the physical hardware. It has become the default foundation of data operations for most companies, as providers such as AWS, Google Cloud and Azure have made it widely accessible.
Companies of any size can now store and process data through sophisticated pipelines without the upfront costs of servers or data centers. The IaaS model is a great fit for data pipelines, specifically because data tasks run on rented infrastructure and reside in cloud storage, making them easy to access for all teams.
Many core infrastructure parts are interchangeable between providers. As such, IaaS is treated as a commodity layer driven by supply and demand, or by price-to-performance ratios, rather than by proprietary features. However, proxy infrastructure is an outlier that only looks similar on the surface.
Why Proxy Networks Are Different
While proxy structure is also rented from third-party providers and serves as core infrastructure for data tasks, the resemblance is superficial at scale. Unlike storage, for example, proxy networks are not interchangeable. Data center IP (internet protocol) addresses from any location are not a substitute for a residential IP pool built across millions of real devices in specific geographies.
Websites treat these IPs differently, so data access rates vary, and the overall quality of data collected is impacted. The efficiency of web scrapers, whether custom-built or pre-made ones, is largely dependent on the proxy layer that executes the tasks.
More importantly, the quality of your proxy infrastructure is often the ceiling of your data collection efforts. No matter how sophisticated your tools are, if the proxy IPs are blacklisted, it won’t retrieve the necessary data. As such, this is why proxy servers should take a more important place when compared to IaaS infrastructure.
Treating effective proxy partnerships as a strategic asset rather than an interchangeable commodity becomes more beneficial as your data collection scales and the need for data in company processes increases. From a supplementary function as a research tool or competitor monitoring, it has become crucial for enabling automation.
The change is now accelerated by self-hosted LLMs, agentic AI and other tools. Companies are no longer collecting data just to inform decisions, but as a core part of their products. Such a shift is especially relevant for proxy server infrastructure, as the quality, scale and geographic reach of data collection directly rely on proxies.
When data collection was only a secondary function, it was reasonable to treat it as a commodity similar to cloud storage. Now, if data collection is important for the product’s functioning, and thus the business model, the proxy infrastructure placement might need reevaluation. Traditional asset classification theory is helpful here.
A Framework For Strategic Resources
The principles of how an organization categorizes its resources determine how it governs, funds and procures them. Such principles are defined by asset classification theories, providing us with the vocabulary to express what exactly goes wrong when critical infrastructure components, such as proxy servers, are misclassified.
I think the most useful theoretical tool here is the resource-based view (RBV), developed back in the ‘90s by Jay Barney. It argues that sustainable competitive advantage derives from resources that are valuable, rare, inimitable and non-substitutable. In practice, RBV implies that resources meeting the criteria must be treated as strategic assets.
All other assets that do not meet such criteria should be treated as operational costs, appropriate for cost-cutting and standard procurement. Quality proxy server networks are valuable because they determine data access and collection quality. They are also rare, especially at scale or for highly complex tasks that require specific IPs.
The operational complexity of residential proxy partnerships is difficult to imitate, creating a product moat further strengthened by the company’s own data collection infrastructure. Lastly, in most large data collection scenarios, quality proxy servers are non-substitutable. Residential proxies from a specific location, for example, cannot be easily replaced by any type, quality or location IPs.
It’s safe to conclude that, according to the RBV framework, treating proxy servers as a utility is a mistake. For companies that rely heavily on data, proxy infrastructure is not a cost-saving measure but a strategic asset that impacts long-term competitiveness.
The Commodity Trap
Investments in proxies shouldn’t be evaluated in isolation. Proxies gain value within a full data collection stack, but this does not diminish their value. On the contrary, proxies are the constraint that sets the ceiling for the entire system, even if they might not have value on their own.
Treating such assets as commodities may lead to what’s commonly called a commodity trap. When a critical resource of a larger system is categorized as interchangeable, the company triggers savings logic that, in pursuit of lower prices, degrades quality.
If data is crucial to the product’s positioning in the market, then saving on such infrastructure as proxies risks your product’s moat. What’s even worse is that the commodity trap is self-reinforcing.
Once a strategic asset is treated as a utility, the spending on it is no longer justified by performance or, in this case, data quality metrics. Rather, the measure becomes savings, and it fuels the product moat erosion caused by cost-cutting even further.
Conclusion
With the ever-increasing reliance on data, it’s important to keep your strategic focus. Best data moats are not necessarily the most sophisticated pipelines. A clear asset classification that prioritizes what’s actually important for your product’s data needs may be more beneficial.
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