How to Plan Execution for App-Specific Chains

Execution planning defines how your appchain processes transactions and updates its state. Unlike general-purpose chains, an app-specific chain allows you to design an execution environment tailored to your application's logic, optimizing for performance, cost, and developer experience. This involves selecting a virtual machine (VM)—like the Ethereum Virtual Machine (EVM), CosmWasm, or a custom VM—and configuring the rules for how transactions are validated, executed, and finalized. The execution layer is responsible for running your smart contracts or application logic and producing a deterministic state transition.

The first step is choosing your execution runtime. The EVM offers maximum compatibility with Ethereum tooling and a vast ecosystem of developers and contracts. CosmWasm, written in Rust and used in the Cosmos ecosystem, provides strong security guarantees through WebAssembly sandboxing. For maximum performance and control, you can implement a native VM using a framework like the Cosmos SDK's x/wasm module or Polygon Edge. Your choice dictates the programming languages available (Solidity, Rust, Go), the gas metering model, and the available precompiles for cryptographic operations.

Next, you must define your state transition function. This is the core business logic that determines how the chain's state—account balances, NFT ownership, game scores—changes in response to a transaction. In an EVM-based chain, this is handled by your deployed smart contracts. In a custom chain, you might build modules (like Cosmos SDK modules) that directly manipulate a key-value store. Planning involves mapping out all possible transaction types, their authorization rules, and their effects on state. Use tools like state machine diagrams and transaction flowcharts to model this logic before implementation.

A critical component of execution planning is gas economics. You must decide how to meter computational work (opcode costs in the EVM, CPU cycles in Wasm) and storage. Set gas prices to prevent spam and allocate block space efficiently. For appchains, you can often set significantly lower gas fees than mainnet L1s because you control the hardware. However, you must still design a fee market or alternative mechanism (like staking for priority) to handle transaction congestion. Consider whether fees are paid in your chain's native token or a stablecoin, and how they are distributed (to validators, burned, or to a treasury).

Finally, plan for execution client implementation and upgrades. You will need to run nodes that execute transactions; these can be forked from existing clients like Geth, Erigon, or Ignite/Cosmos. Establish a clear process for network upgrades (hard forks) to patch vulnerabilities or introduce new features. Use governance mechanisms, often tied to your proof-of-stake validator set, to vote on and deploy upgrades. A well-documented execution plan ensures your chain can scale, remain secure, and evolve without unexpected downtime or consensus failures.

The first prerequisite is a clear value proposition that justifies building a dedicated chain. Ask: does your application require custom execution (e.g., a gaming VM), specific data availability guarantees, or sovereignty over governance and upgrades? If your needs are fully met by deploying a smart contract on an existing L2 like Arbitrum or Optimism, that is often the simpler path. An app-specific chain, or appchain, is warranted when you need control over the entire stack—block time, gas pricing, transaction ordering, and fee token—to optimize for a specific use case like high-frequency trading or privacy.

Next, define your technical requirements. This dictates your stack choice. Key decisions include: the execution environment (EVM, SVM, or a custom VM like the Move VM), the data availability layer (EigenDA, Celestia, Avail, or using Ethereum for security), and the sequencing model (centralized, decentralized, or based on shared sequencing like Espresso). Your choice impacts development complexity, time-to-market, and security. For example, using an EVM rollup stack like the OP Stack or Arbitrum Orbit provides developer familiarity and tooling, while a non-EVM chain offers more flexibility but requires building more infrastructure from scratch.

You must also plan your economic and security model. This involves selecting a settlement layer (typically Ethereum), designing your gas token (using ETH or a native token), and budgeting for ongoing costs like data publishing fees and validator incentives. Security is not automatic; you are responsible for the security of your proposer/sequencer and the economic security of any fraud or validity proof system. Using a shared sequencing network can mitigate some single-operator risks. Tools like the Chainscore Development Dashboard can help model these long-term operational costs and security trade-offs before you commit to a particular architecture.

The state of an app-chain is the complete set of data it tracks, from user balances to smart contract variables. Unlike general-purpose chains where storage is a shared, unpredictable resource, an app-chain lets you design a custom state model optimized for your application's logic. This involves defining your core data structures—whether they are simple key-value stores, Merkle trees for efficient proofs, or complex relational models—and deciding how they are accessed and modified. The choice of state model directly impacts performance, cost, and developer experience.

Effective storage design requires mapping your application's read and write patterns to the underlying database. For high-frequency data like an order book or game state, you might use an in-memory store with periodic snapshots to disk. For less volatile data, a persistent key-value database like RocksDB (commonly used with CometBFT) is standard. Consider separating hot and cold storage: keep frequently accessed data in a structured, indexed format while archiving historical data to a cheaper, slower layer. This separation is crucial for managing chain bloat and ensuring fast node synchronization.

Gas economics for storage must be explicitly defined in your execution environment. You set the costs for SSTORE (writing), SLOAD (reading), and storage refunds. For data-heavy applications, consider implementing state rent or storage staking models where users pay recurring fees to keep data on-chain, preventing indefinite state growth. Alternatively, adopt a stateless or state expiry paradigm, where only state roots are stored permanently, and users provide proofs for the data they interact with, significantly reducing validator overhead.

Interacting with state happens through execution clients like EVM, CosmWasm, or a native SDK. Your runtime must define precise access patterns. For example, a DeFi chain might optimize for batch transaction processing with shared state access, while a gaming chain may require isolated state per user to enable parallel execution. Use iterators for range queries and indexers for complex filtering. Always benchmark these patterns against your chosen database backend to identify bottlenecks before mainnet launch.

Plan for state sync and archive nodes from day one. New validators must be able to bootstrap quickly. Implement snapshot protocols or light client verification for fast sync. For historical data access, design a separate indexing service or archive RPC endpoint that queries a full historical database, keeping your execution layer lean for consensus-critical operations. Tools like The Graph for subgraphs or custom indexers using Apache Kafka can serve this off-chain data layer.

Finally, document your state schema and access patterns clearly. Use protocol buffers or JSON Schema to define data structures, ensuring consistency across clients and indexers. A well-designed state layer is invisible to users but forms the bedrock of your chain's scalability, cost-efficiency, and long-term maintainability. Test under load with realistic transaction volumes to validate your design choices.

For an app-specific chain, the primary goal of a fee market is to secure the network while ensuring predictable costs for end-users. Unlike general-purpose Layer 1s where demand is unpredictable, your application's transaction patterns are more knowable. This allows you to tailor the gas model to your specific operations. You can define custom gas costs for your chain's unique operations, like a gaming action or a DeFi swap, rather than relying on the EVM's default pricing. This predictability is a key user experience advantage, allowing you to abstract gas fees or offer clear, stable pricing.

The core mechanism is the fee market algorithm, which determines how users bid for block space. The simplest model is a first-price auction, where users specify a max fee and pay what they bid. More sophisticated chains implement an EIP-1559-style model with a base fee that adjusts per block and a priority tip for validators. For an app-chain, you must decide which model aligns with your traffic patterns. A stable, predictable application might benefit from a fixed-fee model, while one with variable demand spikes may need a dynamic model to prevent congestion and ensure timely processing.

To plan execution, you must parameterize your gas model. This involves setting key variables: the GasPerSecond target (throughput), the MinGasPrice (base security floor), and the adjustment mechanism for the base fee. For example, the Cosmos SDK's x/feemarket module allows you to set an ElasticityMultiplier to control how aggressively fees rise with congestion. You can simulate different parameter sets against your expected transaction load to find the optimal balance between low user cost and sufficient validator incentives. Tools like Cosmos SDK's simulation framework can help model this.

Validator incentives are inextricably linked to fee economics. The total fees (base fee + tips) must be attractive enough to compensate validators for their hardware and operational costs, securing the chain. If fees are too low, you risk validator attrition and reduced security. A common strategy is to burn the base fee (like Ethereum) to create deflationary pressure, while the tips go directly to validators. Alternatively, you can direct a portion of fees to a community pool to fund development. Your tokenomics must account for this fee distribution to ensure long-term validator participation.

Finally, consider the end-user experience. Will users pay fees in your native token, a stablecoin, or via a gas abstraction layer? Solutions like Cosmos' FeeGrant module allow other accounts to pay fees, enabling sponsored transactions. You can also implement gasless transactions through meta-transactions, where a relayer pays the fee. The choice impacts your chain's accessibility and business model. Thoroughly testing your fee market on a testnet with simulated user activity is essential before mainnet launch to avoid unexpected economic behavior.

Begin by finalizing your execution environment. If you are building with a modular stack like the Cosmos SDK, you will implement your business logic in modules. For an Ethereum-aligned chain using an OP Stack or Arbitrum Orbit, you will deploy your L2OutputOracle and BatchInbox contracts. This stage requires setting concrete parameters: the block_time, gas_limit per block, and the economic model for transaction fees and sequencer/prover incentives. Tools like ignite for Cosmos or foundry/hardhat for EVM chains are essential for local development and initial contract deployment.

Establish a structured testing pipeline. Unit tests should cover all smart contract functions and state transitions. Integration tests must validate cross-module interactions and the correct posting of data to any parent chain or data availability layer. For rollups, this includes testing the fault proof or validity proof mechanism. Finally, run load tests using frameworks like ghz or custom scripts to simulate high transaction volume, measuring metrics like transactions per second (TPS), block propagation time, and state growth. This identifies bottlenecks in your mempool or execution client.

Deploy your chain to a public testnet. For Cosmos chains, this means joining a testnet coalition or using a service like Replicated Security. For rollups, deploy your contracts to a testnet like Sepolia or Holesky. This public phase is critical for testing network gossip, validator/sequencer coordination, and external tooling compatibility (wallets, explorers, indexers). Monitor your chain with a node monitoring stack (Prometheus, Grafana) to track consensus health, peer count, and resource usage.

Plan a incentivized testnet or a closed beta with real users. This "battle-testing" phase goes beyond technical validation to assess user experience, economic security, and the effectiveness of your governance processes (if applicable). Use bug bounty platforms like Immunefi to crowdsource security reviews. The goal is to gather data on real-world usage, finalize gas schedules, and ensure all upgrade pathways are well-documented and tested before committing to a mainnet genesis.