Earning Model
Providing a transparent system for contributors to earn value from their resources.
Last updated
Providing a transparent system for contributors to earn value from their resources.
Last updated
Earning Model is structured to sustain the ecosystemʼs development while providing incentives for all participants, including GPU providers, users and the core development team. By leveraging a decentralized infrastructure, Skyops balances affordability for users with profitability for contributors.
A percentage fee is charged for transactions and services within the ecosystem. This ensures continued development and maintenance of the platform.
Example Calculation for Task Fee: If a user submits a task costing 100 SKYOPS tokens, the task execution fee is:
A small percentage of fees collected in SKYOPS tokens are periodically burned to reduce token supply, thereby increasing scarcity and supporting token value.
Example Token Burn: If the total monthly fees collected amount to 1,000,000 SKYOPS tokens, the platform burns:
Skyops offers premium features and services for power users and enterprises, generating additional revenue streams.
Example: An enterprise requiring guaranteed GPU availability for a 10-hour training task may opt for SLA-backed compute, paying an additional 10% premium for guaranteed resources.
Revenue is generated through partnerships with organizations and developers who integrate Skyops services into their workflows.
Revenue is also reinvested from ecosystem growth activities, including token staking and reward distributions.
GPU contributors are rewarded proportionally based on their compute power, uptime and task completion rates. This ensures a consistent supply of high-quality resources.
Users benefit from transparent pricing, real-time analytics and the option to optimize costs through staking or pre-loading SKYOPS Tokens.
The Skyops Revenue Model is designed to scale with network growth, ensuring sustainability for all participants. Revenue streams adapt to changing demands, incentivizing long-term ecosystem participation while maintaining operational efficiency.
