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Cloud data is difficult to move for more reasons than its size. Retrieval and transfer charges, restrictive software licences, proprietary APIs and dependencies between cloud and AI services can all make switching providers costly or operationally risky. Jon Brooks, a Wasabi executive, argues in a September 2026 ITPro article that these frictions are shifting the practical burden of “data gravity” from physical scale toward fees and ecosystem lock-in. That is a useful way to frame a procurement risk, not a settled conclusion that infrastructure no longer matters.
What data gravity means—and what is changing
Data gravity is the tendency for applications, services and workloads to gather around data that is large, important or operationally central. Digital Realty, a data-center operator, defines it as “the attractive force caused by enterprise data creation and exchange, drawing applications, servers, and other data,” attributing the concept to Dave McCrory. The definition captures more than file size: once applications depend on a dataset, moving the data can mean changing those applications and the systems around them.
Brooks’s ITPro argument is that cloud object storage and nearby compute have reduced some obstacles associated with data’s physical scale, while charges, management complexity and reliance on cloud and AI ecosystems can still make data hard to access or move in practice. The article’s claim that infrastructure-created gravity is no longer a primary concern is Brooks’s view; it should not be treated as a universal finding. Physical transfer, network capacity, application dependencies and migration downtime can still matter, depending on the workload.
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A storage price alone does not show what it will cost to use or leave a cloud service. Retrieval charges, data-transfer or egress fees, storage tiers, software licensing and the operational work of migration can contribute to the total. A low storage rate may therefore be a poor guide to the cost of a workload that frequently reads data, moves it between services or needs a complete export at exit.
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Brooks cites Wasabi’s 2026 Cloud Storage Index for three figures: the index reported that fees accounted for 48% of UK businesses’ cloud spend, nearly half of organizations were overspending on cloud, and 84% named fees as a key driver of overspend. These are findings attributed to a Wasabi index, not universal benchmarks. The article does not state the geography for the “nearly half” figure, and the linked report landing page did not provide detailed methodology in the material reviewed. The 48% figure is specifically reported for UK businesses; it should not be generalized to other countries or all cloud customers.
When comparing services, estimate costs over the expected usage pattern and include the cost of exit, not only the cost of storing data. Ask for the applicable retrieval, transfer, tiering and licensing terms, and model what happens if usage changes or a full migration is required.
Rank #2
How software and AI dependencies create lock-in
Switching friction is not limited to a provider’s own storage charges. Software licences may restrict where a product can run or require customers to buy additional licences when moving it to another cloud. In a review of six selected federal agencies, the US Government Accountability Office (GAO) found that five reported restrictive software-licensing impacts. Examples included repurchasing existing licences for cloud use, additional fees to use software on another provider’s infrastructure, and practices that limited provider or architecture choice. GAO cautioned that its sample was not generalizable; reported costs were often not quantified. These cases document possible barriers, not their prevalence across the whole market.
AI services can add another layer. The US Federal Trade Commission’s January 2025 announcement about specific partnerships between major cloud providers and AI developers identified potential risks including partner-cloud spending commitments and higher contractual or technical switching costs for AI developers. The FTC described possible competition concerns, not a finding that every cloud–AI partnership creates lock-in. Its stated findings reflect information available through January 2025.
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The practical concern is cumulative dependence: a dataset may be stored in one cloud, processed through provider-specific services, used by applications built around proprietary APIs, and tied to software licences or AI partnerships. Exporting the raw files would not, by itself, move those dependencies or guarantee that the workload can run elsewhere.
What portability requires in practice
Portability is a combination of technical capability and contractual permission. A provider’s claim that data can be exported is not enough if the export format is unusable, the transfer takes too long, required APIs are proprietary, licences do not travel with the workload, or the costs and deletion terms are unclear.
- Data: Ask which formats are available, whether metadata and permissions are included, and how to export a complete dataset in a usable form.
- Interfaces and applications: Identify proprietary APIs, managed-service dependencies and application changes needed to run the workload elsewhere.
- Time and process: Obtain the export procedure, expected transfer windows, any service limits and the steps required to verify completeness.
- Contracts and licences: Check whether software and AI-related terms permit operation on another provider or in a hybrid environment, and what additional charges apply.
- Exit: Clarify transfer and retrieval charges, support obligations, deletion procedures and how long the provider retains data after migration.
GAO’s separate review of private-sector cloud practices describes lock-in risk as varying by provider and service. Companies cited multi-cloud planning, containers, interoperability, data portability and application compatibility as ways to preserve flexibility and avoid artificially increased costs. These approaches can reduce dependence, but they also require engineering and operational effort; using several providers is not automatically simpler or cheaper.
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The European Commission says the EU Data Act has applied since 12 September 2025. For covered data-processing services, providers must facilitate switching. The Commission’s explainer describes open interfaces and machine-readable exports for SaaS and PaaS, and functional-equivalence measures for comparable IaaS services.
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The transition matters for fees: the complete prohibition on switching charges, including data-egress charges, begins on 12 January 2027. Before that date, providers may still charge costs related to switching and egress. These requirements apply within the Data Act’s EU scope; they are not a worldwide ban on cloud exit fees.
A practical way to compare providers before committing
Compare the full service and exit path rather than treating storage price, AI capability or portability as separate decisions. Use the same workload assumptions for each candidate and have vendors demonstrate how data and dependent services can be moved.
- Model total cost: Estimate storage, retrieval, transfer, storage tiers, software licences and the staff or service effort needed to operate and migrate the workload.
- Map dependencies: List the APIs, managed services, models, licences and contractual commitments that would need to change if the provider changed.
- Define an acceptable exit: Specify the export format, transfer process and timing, required application changes, deletion terms and charges that would be acceptable.
- Request a demonstrated export: Ask the vendor to show a complete export and explain how it would be validated. Treat this as a procurement check, not a guarantee that another provider can run the workload unchanged.
- Choose architecture deliberately: Decide whether containers, interoperability measures, hybrid deployment or more than one provider are justified by the value of added flexibility and the cost of operating them.
Brooks’s ITPro article also says 72% of UK businesses prefer hybrid strategies for AI workloads, but it does not identify the underlying study or methodology. That figure cannot support a verified market-wide conclusion here. Buyers can still evaluate hybrid deployment on its merits without relying on that statistic.
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