Network Economics & Alignment
The economic framework of Rapid Chain is engineered to ensure long-term sustainability and institutional alignment. By decoupling execution costs from settlement finality, the network provides a predictable and scalable environment for complex financial operations.
4.1. The Dual-Layer Settlement & Fee Model
Rapid Chain introduces a symbiotic economic relationship with its settlement partner, utilizing a dual-layer approach to manage network costs and incentives.
Execution Metering ($RAPID): Computational resources within the EVM and RAda environments are metered using $RAPID. This allows for granular control over gas prices and resource allocation.
Synchronization Settlement ($CC): Global interoperability and legal finality costs are settled in the background via the Canton Network’s native utility component.
Predictable Cost Structures: To meet institutional budgeting requirements, all fees are algorithmically stabilized to maintain a consistent USD-denominated value.
4.2. Comparative Economic Parameters

The following table outlines the distinction between the execution-level utility and the settlement-level security:
Economic Function
Rapid Chain (Execution)
Canton Network (Settlement)
Primary Unit
$RAPID
$CC
Fee Denomination
USD-Stabilized
USD-Stabilized
Utility Role
Computational Gas & Netting
Finality & Synchronization
Governance
Validator Set & Protocol Logic
Network-wide Interoperability
4.3. Logic Implementation: Fee Handling
This pseudocode demonstrates how the Sequencer handles the dual-layer fee logic to ensure the Canton synchronization cost is covered:
4.4. Validator Incentives
The network’s security is maintained by a set of vetted, known entities. These validators are incentivized to maintain high uptime and deterministic performance through a structured reward system.
Stake-Based Participation: Validators must stake $RAPID to participate in the BFT-style consensus, aligning their economic interests with the network's health.
Operational Rewards: Incentives are distributed based on the accuracy and speed of state transitions, rather than speculative drivers.
Last updated