When the cloud costs more than it brings in
The initial promise of the cloud was economical: pay only for what is consumed, without heavy initial investment. For a startup that is launching or a team experimenting, this model is relevant. But as usage establishes itself, regularizes, and workloads become predictable, the bill changes in nature. What was a variable and controlled expense becomes a disguised fixed cost, and often much higher than an own infrastructure.
The problem lies with the model itself. The public cloud charges based on consumption, with layers of managed services, outgoing data transfer, licenses, and support that accumulate silently. A company running stable workloads, a production database, a business application server, a data processing pipeline, pays month after month for resources that it could have amortized in two or three years with its own infrastructure. By migrating these workloads to a datacenter, in colocation or on dedicated servers, the company regains real budget predictability, with fixed-term contracts and a marginal cost close to zero once the infrastructure is in place.
This is not a question of rejecting the cloud, but of economic rationality. Predictable resources belong in the datacenter. Temporary elasticity belongs in the cloud. This simple distinction, if applied correctly, can lead to a substantial reduction in IT expenses without compromising performance.
Performance, where the cloud shows its limits
Cloud architectures rely on a principle of pooling: resources shared among thousands of customers, distributed across geographically distant hyperscalers. For many uses, this pooling is transparent. But for certain critical applications, it becomes a serious operational constraint.
Latency is the first point of friction. A financial application that must respond in a few milliseconds, an industrial system connected to control real-time automation, a recommendation engine that needs to process requests at a very high frequency: all these cases are sensitive to the physical distance between servers and users, and to the inherent variability of shared environments. By hosting these applications in a data center close to the end users or local systems, the company not only ensures lower latency but, more importantly, stable latency, which public cloud cannot always promise.
Intensive processing presents an additional challenge. Artificial intelligence, big data, complex renderings, and scientific simulations are tasks that massively consume CPU, GPU, and RAM over long and predictable durations. Renting these resources hourly on the cloud can incur considerable costs. Having your own dedicated servers in a data center, with guaranteed access to non-shared resources, allows you to perform these processes under optimal conditions, at a much lower cost over time.
Data sovereignty and regulatory requirements
The question of security and compliance has long been an argument in favor of the cloud, which offered reassuring certifications and SLAs. But the regulatory landscape has evolved, and today the requirements are much more specific than what major cloud providers can generically guarantee.
The GDPR imposes strict rules on the location and processing of personal data. Sectors like healthcare, finance, defense, or public services are subject to sector-specific standards, HDS, PCI-DSS, SecNumCloud, which require guarantees that only certified data centers can provide. When the data of a hospital or a banking institution passes through the infrastructure of an American hyperscaler subject to the Cloud Act, the question of sovereignty is no longer theoretical. It is legal.
Hosting these sensitive data in a colocation data center on infrastructures where the company controls access, architecture, and flows fundamentally changes the approach to security. The company can define its own encryption rules, implement custom access policies, and ensure that no one else, neither the provider nor a third-party state, can access its data without its explicit authorization. This level of control is difficult, if not impossible, to achieve in a standard public cloud environment.
The hybrid model: neither going backward, nor compromise
Migration to a data center does not mean abandoning the cloud. It involves using it better. The hybrid model, which involves distributing workloads according to their nature between private infrastructure and public cloud, is currently the strongest strategy for companies that want to combine performance, cost control, and flexibility.
In this model, predictable and critical workloads, databases, business applications, recurring processes, are hosted in the datacenter, where cost and stability are optimal. The public cloud maintains its role as an elastic strike force: absorbing unpredictable load peaks, hosting development environments, testing new architectures, rapidly deploying short-lived services. Innovation remains in the cloud. Solidity is in the datacenter.
This distributed architecture is also an asset for resilience. By no longer relying on a single provider for its entire infrastructure, the company reduces its exposure to outages, unilateral price increases, and strategic decisions by third parties over which it has no control. Diversifying the infrastructure is, as in finance, a way to reduce systemic risk.
Regain control of one's infrastructure
This movement of partial repatriation to data centers is not a nostalgia for the server rooms of the 2000s. It is the recognition that the public cloud, designed for universality, is not always the most suitable tool for every situation. Companies that have reached a certain level of IT maturity now know how to distinguish the workloads that benefit from the cloud from those that suffer from its constraints.
At DC2SCALE, we support this transition with dedicated infrastructures, data centers in France, and an approach that complies with European regulatory requirements. Because regaining control of your infrastructure means regaining control of your IT, and, by extension, your business.
