How to Estimate Layer 2 Cost Savings

Estimating Layer 2 (L2) cost savings is a critical skill for developers and users navigating Ethereum's scaling ecosystem. While the promise of lower fees is universal, the actual savings depend on network activity, transaction type, and the specific L2's architecture. This guide provides a framework for making accurate, data-driven comparisons between Ethereum mainnet and leading L2s like Arbitrum, Optimism, Base, and zkSync Era. We'll move beyond simple marketing claims to show you how to calculate real-world costs using public tools and on-chain data.

The foundation of any cost estimate is understanding the fee components. On Ethereum L1, you pay a base fee (burned) and a priority fee (tip to the validator), measured in gwei. L2s inherit security from Ethereum but process transactions off-chain, bundling them into a single L1 proof. Your L2 fee typically covers: the cost to post your transaction data to L1 (data availability), the L2's own execution costs, and sometimes a sequencer profit margin. Networks using ZK-rollups also incur a cost for generating validity proofs.

To get started, you need real-time data. Use block explorers like Etherscan for L1 and L2-specific explorers (Arbiscan, Optimistic Etherscan) to check current gasPrice. Fee estimation tools are essential: the Ethereum Gas Tracker, L2 Fees.info, and Dune Analytics dashboards provide aggregated comparisons. For development, you can programmatically estimate costs using the eth_estimateGas RPC call on both L1 and L2 RPC endpoints. This allows you to simulate a transaction and see the gas units required before submitting.

Let's walk through a concrete example: estimating the cost of a Uniswap swap. First, simulate the transaction on Sepolia testnet or a live network using the methods above to get the gas units needed. Assume a swap requires 150,000 gas. On L1 with a gas price of 30 gwei and ETH at $3,000, the cost is 150,000 * 30 Gwei = 0.0045 ETH or $13.50. On an L2, the same operation might use 200,000 L2 gas (due to different VM overhead) but with a gas price of 0.1 gwei. The L2 execution cost is minimal (200,000 * 0.1 Gwei = 0.00002 ETH), but you must add the L1 data posting fee, which could be ~0.0003 ETH. The total: ~0.00032 ETH or $0.96, representing a ~93% savings.

Remember that savings are not static. They fluctuate with L1 congestion—when L1 gas prices spike, L2 fees also rise as their data posting costs increase. Different transaction types save differently: simple transfers see the largest percentage reduction, while complex DeFi interactions see smaller (but still significant) savings due to higher L2 execution gas. Always benchmark your specific contract calls. Furthermore, consider the total cost of bridging assets to the L2, which is a one-time L1 transaction, in your overall economic analysis.

For ongoing monitoring, set up alerts using services like DefiLlama's fee tracker or create a simple script that polls RPC endpoints. The key takeaway is that effective estimation requires moving from anecdotal evidence to measurement. By using the tools and methods outlined here—RPC calls, explorers, and fee dashboards—you can accurately project costs, optimize your dapp's user experience, and make informed decisions about which Layer 2 network offers the best economics for your specific use case.

Before estimating savings, you must understand the core cost components. On Ethereum mainnet, the transaction fee (gas) is calculated as Gas Units Used * (Base Fee + Priority Fee). The base fee is burned and adjusts per block based on network demand, while the priority fee (tip) incentivizes validators. Layer 2 solutions like Optimistic Rollups (Arbitrum, Optimism) and ZK-Rollups (zkSync Era, Starknet) batch thousands of transactions off-chain, submitting only compressed proofs or state differences to Ethereum. This drastically reduces the gas units used per individual user transaction, which is the primary source of savings.

You will need access to real-time and historical gas data. For mainnet estimates, use the eth_gasPrice RPC call or public endpoints from providers like Alchemy or Infura. For L2 estimates, you must query the specific chain's RPC, as fee models differ. For example, Arbitrum uses a complex model involving L1 data posting costs and L2 execution costs, while Starknet fees are paid in STRK for proving. Tools like the Gas Price API from Etherscan or the @eth-optimism/sdk for Optimism provide abstracted methods for fetching current fees.

A critical prerequisite is defining a comparable transaction. Estimating savings requires you to model the same operation—such as a token swap, an NFT mint, or a smart contract interaction—on both mainnet and the target L2. The gas consumption will vary significantly based on the transaction's complexity (computational steps and calldata size). Use a tool like Tenderly to simulate the transaction on mainnet to get an accurate gasUsed value, which serves as your baseline for comparison.

Finally, you must account for the full cost stack. The user-perceived cost on an L2 is not just the L2 execution fee. For most rollups, it also includes a pro-rata share of the cost to post data or proofs to Ethereum L1. Furthermore, consider withdrawal times and costs; moving assets from L2 back to L1 via a bridge can incur a non-trivial fee and a delay (ranging from minutes for ZK-Rollups to 7 days for Optimistic Rollups). Your savings estimate is incomplete without modeling the entire user journey, including these exit scenarios.

The primary cost components on a Layer 2 (L2) are execution, state storage, and data availability (DA). On Ethereum mainnet, these are bundled into a single gas fee. L2s separate and optimize these costs. Execution, the cost of running your smart contract logic, is the most dramatically reduced component. By processing transactions off-chain in a high-throughput environment, L2s can offer execution at a fraction of the cost, often 10-100x cheaper than mainnet.

State storage refers to the cost of updating the global state, such as recording a new token balance. While cheaper than mainnet, this cost is more persistent. The most critical and variable component is data availability. For optimistic rollups like Arbitrum and Optimism, transaction data is published to Ethereum as calldata, which is expensive but secure. Zero-knowledge rollups like zkSync and StarkNet publish smaller cryptographic proofs, making their DA costs significantly lower and more predictable.

To estimate savings, you must model these components separately. For example, a simple ERC-20 transfer on an optimistic rollup might cost: minimal execution fees, a small state update fee, and the dominant cost of posting ~100 bytes of calldata to Ethereum. Tools like the L2 Fees dashboard provide real-time comparisons. Remember, savings are not uniform; a complex DeFi transaction with high computational load (execution) will see greater relative savings than a simple transfer, which is bottlenecked by data costs.

To accurately estimate potential savings, you need a concrete Mainnet cost baseline. This is not a theoretical exercise; you must calculate the gas units and gas price required for your specific contract interactions. Use tools like the eth_estimateGas RPC call or simulate transactions in a local fork using Hardhat or Foundry. For example, a simple ERC-20 transfer might cost 65,000 gas, while a complex Uniswap V3 swap could exceed 200,000 gas. The actual USD cost is this gas amount multiplied by the current gas price (in Gwei) and the ETH/USD price.

Focus on your application's most common and most expensive operations. Profile your smart contract functions: - A token mint or transfer - Adding liquidity to a pool - Executing a multi-step DeFi strategy. Record the gas consumption for each. Public block explorers like Etherscan provide historical gas data for verified contracts, which is useful for benchmarking. Remember, gas costs are not static; they fluctuate with network congestion, so consider calculating an average or a high-congestion scenario.

For developers, the most reliable method is programmatic simulation. Using Foundry's forge command, you can create a mainnet fork and call your functions directly to get precise gas reports: forge test --fork-url <RPC_URL> --gas-report. This will output the gas used for each contract call in your tests, giving you a reproducible and accurate baseline. This data becomes the critical numerator in your savings calculation when you later test the same operations on an L2.

Estimating cost savings requires comparing the gas fees for a specific operation on both the Ethereum Mainnet and your chosen Layer 2. The fundamental metric is the gas price, measured in Gwei. On Mainnet, you pay this price directly. On an L2 like Arbitrum, Optimism, or Base, you pay a small fee to the L2 sequencer plus a tiny portion to cover the cost of posting your transaction's data back to Ethereum, known as the data availability cost. This is why L2 fees are often 10-100x cheaper.

To get a real-world estimate, you need the gas usage for your target transaction. You can find this by simulating the transaction on a block explorer like Etherscan for a past, similar transaction. Look for the Gas Used field. For example, a simple ERC-20 transfer uses about 65,000 gas, while a complex swap on a DEX can use 150,000 gas or more. Multiply this gas used by the current gas price on each network to get the cost in ETH, then convert to USD.

A practical method is to use a fee estimation dashboard. Tools like L2Fees.info provide real-time comparisons for common actions like token transfers and swaps across all major L2s. For custom smart contract interactions, you can use the eth_estimateGas RPC call on both the Mainnet and L2 RPC endpoints to programmatically estimate gas. Remember that L2 fees are more volatile than Mainnet's; they spike during network congestion but typically settle much lower.

Consider the full cost structure. For developers, key expenses include contract deployments and frequent user interactions. Deploying a medium-complexity smart contract can cost over $1,000 on Mainnet but often less than $100 on an L2. For users, the savings are per-transaction. If your dApp expects users to perform 10 transactions per session, multiplying the per-tx savings by your expected user base provides a powerful projection for reduced friction and increased adoption.

Finally, factor in indirect savings. Lower costs enable new design patterns: micro-transactions, gasless transactions via meta-transactions, and more frequent on-chain interactions become feasible. This estimation isn't just about cutting expenses; it's about unlocking functionality that is economically impossible on Ethereum Mainnet alone. Use these calculations to build a business case for your migration or new project launch.

To accurately estimate savings, you must first establish a baseline. Collect data on your recent Ethereum mainnet activity. For each transaction type you commonly execute—such as token swaps, NFT mints, or contract deployments—record the average gas used and the gas price (in gwei) paid. Tools like Etherscan, Tenderly, or your wallet's transaction history are essential here. Calculate the average cost in ETH, then convert it to USD using a historical price feed. This baseline cost represents your current expense on L1.

Next, simulate the same operations on your target Layer 2. The process differs from L1. Instead of a fluctuating gas price, L2s typically have a more stable fee composed of two parts: L2 execution gas and L1 data publication costs. You can estimate this using the L2's public RPC endpoint and fee estimation methods. For example, on Arbitrum, you would call eth_estimateGas and eth_gasPrice. On Optimism, you can use the GasPriceOracle precompile. Starknet and zkSync Era have their own SDKs for fee estimation. Run these estimates for your standard transaction types.

The critical calculation is the savings ratio: (L1_Cost - L2_Cost) / L1_Cost. A result of 0.95 means a 95% reduction, which is common for simple transfers. However, savings vary dramatically by transaction complexity. A basic ETH transfer may see 90-99% savings, while a complex DeFi interaction involving multiple contracts might see 70-85%. Always run estimates for your specific contract calls, as generic benchmarks can be misleading. Remember to factor in the one-time cost of bridging assets to the L2, which is an L1 transaction.

For ongoing analysis, consider building a simple monitoring script. It can periodically fetch current gas prices from both networks via providers like Etherscan's API and the L2's gateway, then calculate and log the savings ratio for your key transactions. This helps identify the optimal times for batch operations. Furthermore, understand the fee components: on Optimistic Rollups, the L1 data cost is the dominant factor; on ZK Rollups, the cost of generating validity proofs is added. This knowledge helps you predict how savings might change during periods of high L1 congestion.

Finally, compare across L2s. Your savings will differ between Arbitrum, Optimism, Base, and ZK-rollups like zkSync Era. A transaction that is cheap on one L2 due to efficient compression might be more expensive on another. Use block explorers like Arbiscan or Optimistic Etherscan to verify real transaction costs. This quantitative comparison, grounded in your own application's data, is the only reliable way to make an informed infrastructure decision and clearly communicate the value proposition to your users.

Accurate L2 cost estimation is not a one-time calculation but an ongoing analysis. Key variables like base fee fluctuations on the parent chain (Ethereum), sequencer congestion, and the specific data compression techniques used by your chosen L2 (Optimism, Arbitrum, zkSync Era, etc.) all significantly impact final savings. Regularly using the tools mentioned—block explorers, fee calculators, and gas tracking dashboards—is essential for maintaining an up-to-date understanding of the cost landscape.

To operationalize these savings, integrate estimation into your development workflow. For developers, this means: - Profiling your smart contract's function calls to identify gas-heavy operations. - Using local testnets or devnets like Hardhat or Foundry to simulate L1 vs. L2 execution. - Implementing dynamic fee estimation in your dApp's frontend using provider methods like eth_estimateGas on both networks. This data-driven approach allows for informed architectural decisions, such as batching transactions or optimizing contract logic for L2 environments.

The next step is to explore advanced cost-optimization strategies specific to your L2. For Optimistic Rollups, investigate the use of EIP-4844 blobs for even cheaper data availability. For ZK Rollups, understand how different proof systems and verifier costs affect fees. Engage with the developer communities on forums and Discord channels for your chosen L2 to stay current on best practices and new fee-saving features as the technology rapidly evolves.