Starknet performance and rollup technology comparisons
Starknet utilizes Cairo-native implementations and decentralized sequencers to enhance performance, while Rollup and Validium modes offer different data availability strategies. Comparison with Scroll and Polygon CDK highlights trade-offs between EVM bytecode equivalence and modularity.
Starknet architecture and Cairo-native upgrades
Starknet relies on the Cairo language to generate mathematical proofs for transaction validity. The sequencer utilizes a Sierra to MLIR compiler to transform declared Cairo classes into native bytecode, which allows the network to bypass the virtual machine and execute native CPU opcodes during the transaction execution process to improve overall system performance for all users. This Cairo-native implementation targets performance. Version 0.14.0, known as Grinta, introduced a decentralized sequencer architecture using three sequencers with Tendermint consensus. Juno works to become a Starknet sequencer by using Nethermind’s Go-based custom Tendermint implementation. This update also introduced pre-confirmations, which reduce perceived execution time to 500 milliseconds. Users select between Rollup and Validium modes via Volition. Rollup mode publishes the ledger directly to Ethereum, whereas Validium mode relies on a Data Availability Committee to secure the ledger off-chain. Validium only stores a single hash of the ledger information on Ethereum. The network also introduced optimistic parallelization in version 0.13.2. Earlier, version 0.13.0 provided fee payment in STRK and a 4x throughput improvement in ERC-20 transfers. StarkWare allocated 75M STRK among chosen validators and staked 30M STRK to its own validator. The Starknet Foundation provides a matching 5M STRK for validators. The sequencer and prover are closed source.
ZK-EVM differences and finality
Rollup technologies vary in how they handle the Ethereum Virtual Machine. Scroll targets bytecode-level equivalence with the EVM, meaning existing Ethereum contracts run identically without recompilation or additional audits. This equivalence allows developers to use standard tools like Foundry and Hardhat without modifications. zkSync Era uses a compiler to transpile Solidity to its own intermediate representation, although its interpreter now supports both standard EVM bytecode and native EraVM bytecode. Polygon CDK enables the creation of custom ZK-powered blockchains that manage specific application requirements.
| Feature | Scroll | zkSync Era | Polygon CDK |
|---|---|---|---|
| EVM Model | Bytecode-equivalent | Compatible | Compatible |
| DA Strategy | Rollup | Rollup | Validium |
| Withdrawal Finality | ~1 hour | Minutes | Fast (ZK) |
The Euclid upgrade migrated Scroll to OpenVM, a general-purpose RISC-V zkVM developed by Axiom. Scroll reaches hard finality in approximately one hour after the ZK proof reaches Ethereum. This speed removes the 7-day challenge window used by optimistic rollups. Developers choose between performance and compatibility. You must evaluate if bytecode equivalence outweighs the benefits of specialized compilers. Scroll uses the halo2 proving system to generate proofs.
Polygon CDK and interoperability
Polygon CDK builds appchains that leverage shared liquidity through the LXLY bridge. These chains communicate with adjacent Polygon chains to prevent liquidity fragmentation. Axelar provides secure cross-chain communication for Polygon Supernets, allowing assets to move between different chains. This integration facilitates complex operations like cross-chain borrow-lending. Polygon CDK is an upgraded version of Supernets. The framework includes a Prover, a virtual machine, data availability, and a sequencer for all deployments. Users can choose an execution environment, a token model, or gas fee customization with this modularity. Zeeve acts as an implementation partner for all Polygon CDK based chain deployments. The Data Availability Committee handles the availability of data for all blocks of application-specific blockchains. This structure reduces the load on Layer 1. How will the competition between these different data availability strategies affect long-term scaling?
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