Data gravity is an analogy describing the phenomenon where large datasets attract applications, services, and additional data. As the volume of data increases, the "mass" of the dataset grows, requiring compute resources to be located in close proximity to minimize latency and maximize throughput. This makes it progressively difficult and expensive to move the data or the associated processing logic to a different environment.
Data gravity is a primary driver of long-term vendor lock-in. It creates significant technical and financial friction during migrations due to the time required for data transfer and the associated egress fees. This leads to a state where an organization's architectural choices are dictated by the location of existing data rather than by performance or cost-efficiency requirements.
We treat data gravity as a structural risk to institutional sovereignty. To maintain operational leverage, organizations must decouple their data from proprietary cloud storage layers. We recommend storing primary, high-volume datasets in carrier-neutral facilities or using multi-cloud data management layers. This strategy lowers the relative gravity of any single cloud provider, ensuring that compute workloads remains portable and that the organization retains the power to migrate its stack without facing prohibitive exit costs.