Execution Shards vs Data Shards

Execution Shards excel at horizontal scaling of transaction processing by partitioning the network into multiple, parallel chains (shards) that each process their own transactions and smart contracts. This approach, pioneered by networks like NEAR Protocol and Zilliqa, can theoretically achieve thousands of transactions per second (TPS) by distributing computational load. For example, NEAR's Nightshade sharding design targets 100,000 TPS, a significant leap from Ethereum's ~15 TPS base layer. The primary trade-off is increased complexity in cross-shard communication and state management, which can introduce latency for applications requiring atomic composability across shards.

Data Shards take a different approach by scaling data availability and storage, not computation. In this model, a single execution layer (like Ethereum's Beacon Chain) processes all transactions, but the associated data is distributed across specialized data availability layers like Celestia or EigenDA. This separation, central to rollup-centric scaling, allows for massive throughput of data (e.g., Celestia can handle ~100 MB per block) while keeping execution simple and secure. The trade-off is that application logic remains bottlenecked by the single execution layer's capacity, making it ideal for rollups and sovereign chains that batch transactions.

The key trade-off: If your priority is native, high-throughput execution for complex, interconnected dApps and you can manage cross-shard complexity, consider an execution-sharded chain like NEAR. If you prioritize building a secure, modular rollup or appchain and want to leverage Ethereum's security for settlement while scaling data independently, choose a data sharding solution like the Ethereum Danksharding roadmap with Celestia or EigenDA as your data availability layer.

Execution Shards, as implemented by networks like NEAR Protocol and Zilliqa, excel at horizontal scaling for transaction processing by partitioning the state and computation. This directly increases network throughput, with NEAR achieving over 100,000 TPS in theoretical sharded design. The primary benefit is low-latency finality for end-users and dApps requiring high-speed interactions, such as gaming or high-frequency DeFi. However, this model maintains higher node hardware requirements and increases cross-shard communication complexity.

Data Shards (Data Availability Layers), championed by EigenDA, Celestia, and Avail, take a fundamentally different approach by decoupling data publication from execution. This modular strategy allows rollups and L2s to post transaction data cheaply and verifiably, with costs as low as $0.10 per MB on Celestia, while relying on separate execution environments like Arbitrum or Optimism. The trade-off is that the execution layer's performance and security become separate concerns, introducing a multi-layer dependency but enabling unparalleled scalability for data-intensive applications like on-chain gaming or social graphs.

The key architectural trade-off is between integrated performance and modular flexibility. Execution shards provide a unified, high-performance environment ideal for monolithic applications demanding consistent low latency and atomic composability across all components. Data shards offer a pluggable, cost-optimized foundation for developers who prioritize minimal data costs, the ability to choose their own execution VM (EVM, SVM, Move), and are building applications where execution can be batched or settled elsewhere.

Strategic Recommendation: Choose Execution Shards if your protocol requires ultra-fast, atomic cross-contract interactions within a single security domain—think a complex decentralized exchange or a massively multiplayer on-chain game. Choose Data Shards if you are launching an L2 or app-specific rollup where data availability is the primary cost bottleneck, and you value the ability to independently scale execution or switch settlement layers without a hard fork.