Experienced Managed Service Providers (MSPs) who have sold to enterprise customers for decades will be familiar with the challenge of data gravity. The larger and more disparate a global organization’s dataset was, the more difficult it was to move, access, and use, therefore raising data storage bills. Companies were unable to scale without bearing the logistical and financial weight of their data, and it was a regular pain point in new business negotiations.

However, the storage industry has evolved exponentially since then. Through the introduction and subsequent advancement of cloud object storage, cloud providers were able to alleviate much of the weight of data gravity by consolidating enormous volumes of data and eliminating the need for physical space. What’s more, compute is now able to run where the data resides inside the cloud, as opposed to needing to be downloaded for processing. Data gravity, as created by infrastructure, is no longer a primary concern.

And yet global businesses are still burdened by their data. The scale of the dataset may no longer be an obstruction, but there are new forces dragging down data management and preventing seamless access.

The weight of cloud cost

The first new “data drag” is cost – not the costs associated with the size of the dataset but the cost of moving and maintaining data. From egress fees to tiered storage penalties and rising infrastructure complexity, the cost of moving and maintaining data is preventing businesses from seamlessly using their own information on demand.

When choosing a storage solution, data management fees can play an influential role in purchase decisions. Our 2026 Cloud Storage Index revealed 48% of UK businesses’ cloud spend was spent on fees rather than storage itself. Then, nearly half of organizations are overspending on cloud and 84% point to fees as a key driver of excess spend. So, it is imperative channel partners understand the financial paradigms of each cloud player to be able to offer a bespoke fit that suits their customers’ budgets and requirements.

Take egress fees as an example of how pricing mechanisms weigh down data autonomy. They are incurred whenever data is withdrawn from a cloud provider’s infrastructure and are particularly problematic as they inhibit customers from accessing and moving their own data. As a result, companies often find themselves fully dependent on their cloud provider, locked in by their data.

The weight of AI lock-in

Fees are not the only mechanism weighing businesses down. As organizations adopt and feed their data into AI platforms such as ChatGPT, Claude and Gemini, a new dependency layer is emerging. These tools risk becoming tightly coupled with specific data environments, making interoperability between platforms increasingly difficult.

We’re seeing cloud providers bundle their tools in a way that demands customers use all their data services exclusively. For example, recent submissions to the UK’s Competition Market Authority strategic market status investigation of Microsoft reveal instances of organizations having to pay for the full suite of products despite only planning to purchase Microsoft Teams, for example. Now, AI appears to be the latest channel for vendor lock-in.

With the cloud hyperscalers entering the AI game, and their historic tendency to capture vendor lock-in by bundling commodity and proprietary services, it would not be surprising if they tied down their AI customers into complex contract mechanisms with cloud, compute, and other services. The cloud industry risks AI reinforcing these walled gardens that prevent the flexibility and autonomy businesses need to innovate and progress.

Customers may then feel pressured to commit to one provider for all their AI and data management needs, but the data shows that 72% of UK businesses prefer hybrid strategies to manage their AI workloads. It is up to their channel partners to help find a hybrid solution of data storage and management providers that allow, by default, easy data export and transfer.

While the enormous scale of enterprise datasets is no longer the ball and chain it once was, both the cost and AI lock-in of contemporary data management have stepped into its place as elements of a new data gravity.

Friction from fee structures and AI ecosystem dependencies are both restricting businesses from accessing and using their own data as they need, thus preventing them from unlocking innovation.

To support their customers, channel partners must understand the nuance of data storage and management in an AI era. Only by doing so will they be able to help guide their customers through purchase decisions to find not only the best solution for their storage needs but also the best-fit storage solutions that support and drive their overall business objectives.