> For the complete documentation index, see [llms.txt](https://docs.cortensor.network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cortensor.network/community-and-ecosystem/community-testing/testnet-and-mainnet-strategy.md).

# Testnet & Mainnet Strategy

Cortensor's network architecture is designed to balance scalability, performance, and operational cost through a dual-track deployment model. This involves two parallel environments for both testnets and mainnets, each serving distinct use cases and cost profiles.

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### Overview

Cortensor utilizes a **dual-network strategy**:

* **Testnet-0 / Mainnet-Lite**: Built on Arbitrum Sepolia and Arbitrum Mainnet respectively.
* **Testnet-1 / Mainnet**: Built on a custom L3 AppChain Rollup using $COR as the native gas token.

This approach allows Cortensor to:

* Minimize onboarding friction during early development
* Reduce operational complexity for short- and mid-term testing
* Build toward a long-term, cost-efficient, and fully composable decentralized AI execution network

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### Testnet Strategy

#### Testnet-0 – Arbitrum Sepolia

* **Purpose**: Lightweight test environment for rapid iteration
* **Infrastructure**: Shared public L2 chain
* **Cost**: Minimal infrastructure maintenance, but higher per-transaction gas cost (ETH)
* **Use Cases**:
  * Session flow validation
  * User task pipeline
  * Node onboarding and interaction
  * Staking and session payment flows

#### Testnet-1 – L3 Rollup (COR as Gas)

* **Purpose**: Long-term AppChain testing for scalability and sovereignty
* **Infrastructure**: L3 Rollup maintained by Cortensor with ORACLE/MINER nodes and COR-native execution
* **Cost**: Monthly maintenance cost for rollup and related services (bridge, explorer, RPC), but near-zero per-use cost
* **Use Cases**:
  * App-specific chain logic and slashing
  * Validator feedback loop
  * Metadata rollup
  * Multi-session routing and recursive task flow
  * COR-based payments and economic tracking

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### Mainnet Strategy

#### Mainnet-Lite – Arbitrum Mainnet (ETH as Gas)

* **Launch Stage**: Mid-term production-ready setup
* **Purpose**: Serves users where EVM compatibility and gas UX are mature
* **Benefits**:
  * Lower infra costs
  * Leverages robust Arbitrum infra
* **Trade-offs**:
  * Higher per-use gas cost for AI inferencing
  * Limited app-level customization

#### Mainnet – L3 Rollup (COR as Gas)

* **Launch Stage**: Long-term, full-featured mainnet
* **Purpose**: Fully sovereign appchain for AI and agent execution
* **Benefits**:
  * Fully composable architecture
  * COR-native microeconomy for rewards/payments
  * Optimized for AI workload batching and low latency routing
* **Trade-offs**:
  * Requires monthly maintenance of rollup infra
  * Slightly more complex onboarding vs. L2

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### Transition Strategy

* Early user-facing flows and testing occur on Testnet-0 to minimize costs and friction
* Phase #6 onward focuses heavily on Testnet-1 (L3 Rollup) to test long-term architecture and prepare for mainnet
* Production begins with Mainnet-Lite on Arbitrum for early adoption and cost efficiency
* Full decentralization and custom logic (slashing, app-specific governance, long-term scaling) will move to Mainnet (L3 COR Rollup)

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### Benefits of Dual-Track Approach

* **Cost Flexibility**: Teams can balance short-term and long-term usage based on cost structure
* **Transition Readiness**: Ability to migrate from shared infra to sovereign appchain without disruption
* **Progressive Decentralization**: L2 and L3 testnets help harden the architecture before full production
* **Ecosystem Growth**: Different developer and user profiles can onboard via the environment that suits them best

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### References

* L3 Rollup Bridge: [https://cortensor-testnet-1-32ph80a02h-01acc12814e9cea3.testnets.rollbridge.app](https://cortensor-testnet-1-32ph80a02h-01acc12814e9cea3.testnets.rollbridge.app/)
* L3 Explorer: [https://testnet1.explorer.cortensor.network](https://testnet1.explorer.cortensor.network/)

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This dual-environment design reflects Cortensor’s philosophy: start lean, scale smart, and grow decentralized. The infrastructure must evolve with the AI workloads it serves—this strategy makes that possible.