# Revenue Sharing #### **Revenue Sharing in $Terra** Terra’s **revenue sharing model** ensures that all participants in the ecosystem—hardware owners, renters, and $TERRA token holders—benefit from the platform’s success. **Hardware contributors** who lend GPUs, VMs, or other computing resources earn a significant portion of the revenue generated from tasks performed on their hardware. These tasks include cryptocurrency mining, AI model training, and cloud computing applications. Payouts are distributed in **real-time** through Terra’s blockchain-powered system, with earnings directly proportional to the performance and utilization of the contributed hardware. In addition to this, **$TERRA token holders** also share in the platform’s success. A portion of the revenue generated by Terra’s ecosystem is allocated to token holders, creating additional incentives for holding $TERRA. This distribution mechanism rewards long-term supporters and aligns the interests of all stakeholders with the platform’s growth. The revenue sharing process is fully automated via **smart contracts**, ensuring transparency, accuracy, and efficiency. Payouts are made in $TERRA tokens, enabling contributors and holders to reinvest in the platform, participate in governance, or trade on the open market. This dual revenue-sharing approach—rewarding both hardware contributors and token holders—fosters a thriving, inclusive ecosystem. It creates a **self-sustaining cycle** where users, contributors, and investors are incentivized to grow Terra together while benefiting from the increasing demand for decentralized and affordable computing power. --- # Agent Instructions: 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: ``` GET https://useterra.gitbook.io/terra/key-info/revenue-sharing.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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.