> For the complete documentation index, see [llms.txt](https://docs.eseye.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.eseye.com/api-reference/connectivity-metrics/data-volume-and-commercial-model.md). # Data volume and commercial model Connectivity Metrics is a commercial service. The charge reflects the cost of generating, processing and delivering network data continuously across the estate. ### What drives volume Data volumes depend on the size of your estate and how active it is. A large or busy deployment can produce significant volumes, particularly on the NetFlow side, where every flow within every session generates a record. Because of that, pricing depends on the number of devices and the level of traffic rather than on a flat rate. ### Planning for it Volume is worth estimating early, since it affects more than the commercial conversation. It also determines your retrieval cadence, your storage footprint and the cost of querying the data once it lands in your platform. Teams that treat the feed as an ordinary warehouse ingestion problem tend to have a much easier time than those that treat it as an occasional export. ### Starting with a trial A short trial period is a common starting point. It lets you ingest real data from your own estate, confirm the volumes you will actually see, and validate the value of the datasets before committing to a broader rollout. For more pricing information, contact . ### Related * [How the data feed works (Delivery flow)](/api-reference/connectivity-metrics/how-the-data-feed-works-delivery-flow.md) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.eseye.com/api-reference/connectivity-metrics/data-volume-and-commercial-model.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.