Setting Up a Gas Fee Optimization Dashboard for Users

For decentralized applications (dApps), unpredictable and hidden gas costs are a primary source of user frustration and abandonment. A user interacting with a smart contract might approve a transaction expecting a $5 fee, only to be surprised by a $50 cost due to network congestion or complex contract logic. This lack of fee transparency erodes trust. Studies, including those from the Ethereum Foundation, consistently cite high and volatile transaction costs as a major barrier to mainstream adoption. Providing users with clear, real-time, and predictive fee information is no longer a luxury—it's a necessity for any dApp aiming for sustainable growth.

A Gas Fee Optimization Dashboard directly addresses this pain point by aggregating and visualizing cost data. Instead of forcing users to check block explorers or rely on wallet estimates that can be inaccurate, the dashboard serves as a single source of truth. It can display metrics like: current network base fee, priority fee (tip) suggestions, historical fee trends for specific actions (e.g., "Your last NFT mint cost 0.005 ETH"), and cost comparisons between different transaction timings or L2 solutions. This empowers users to make informed decisions, such as batching transactions or delaying non-urgent actions.

Implementing this requires fetching data from multiple sources. You'll need to query the EIP-1559 base fee from a provider like Alchemy or Infura, calculate realistic priority fees by analyzing recent block inclusion data, and potentially integrate with gas estimation APIs from services like Blocknative or Gas Station Network (GSN). The dashboard logic must also account for different operation types; a simple ETH transfer requires less gas than interacting with a complex DeFi protocol like Aave or Uniswap V3. Accurate estimates require simulating the transaction using eth_estimateGas.

From a product perspective, transparency builds loyalty. Users who understand costs are more likely to return. Furthermore, a dashboard can be a proactive tool. It can alert users to optimal low-fee windows or suggest moving assets to an affiliated Layer 2 (e.g., Arbitrum, Optimism) where fees are consistently lower. By demystifying gas, you reduce support tickets related to failed transactions (often due to insufficient gas) and create a more professional, user-centric experience that stands out in a crowded dApp landscape.

A modern development environment is the first prerequisite. You will need Node.js (v18 or later) and npm or yarn installed. We recommend using a code editor like VS Code with extensions for Solidity and JavaScript. This tutorial uses Next.js 14 (App Router) for the frontend framework, providing server-side rendering and API route capabilities out of the box. For styling, we'll use Tailwind CSS to quickly build a responsive UI.

Core blockchain interaction will be handled by viem and wagmi, the leading TypeScript libraries for Ethereum. viem provides low-level, type-safe RPC clients and utilities, while wagmi offers React hooks for managing wallet connections and state. You'll also need @tanstack/react-query for efficient data fetching and caching of on-chain data like gas prices and transaction histories. Install these with npm install viem wagmi @tanstack/react-query.

To fetch real-time and historical gas data, you'll need API keys from service providers. The tutorial integrates with Etherscan for fetching transaction histories and Blocknative or ETH Gas Station for real-time gas price estimates and mempool data. For advanced predictions, we'll use the Chainscore Gas API, which provides machine-learning forecasts for optimal transaction timing. Securely store these API keys in environment variables using a .env.local file.

A basic understanding of Ethereum gas mechanics is required. You should be familiar with concepts like base fee, priority fee (tip), max fee, EIP-1559, and how gas limits work. Knowledge of React hooks (useState, useEffect) and asynchronous JavaScript (async/await) is assumed. We will write utility functions to calculate potential savings and format data from multiple sources into a unified dashboard view.

Finally, you will need a Web3 wallet for testing, such as MetaMask, and access to a testnet like Sepolia. We'll use the wagmi configuration to connect to both mainnet and testnet RPC providers, allowing you to test gas estimation functions without spending real ETH. The complete project structure and initial configuration code will be provided in the next section.

A gas fee optimization dashboard aggregates real-time and historical data from various blockchains to provide actionable insights. The core system architecture consists of three main layers: a data ingestion layer that pulls raw data from node RPCs and mempool APIs, a processing and storage layer that normalizes and indexes this data, and a presentation layer that serves the analyzed information via a web interface or API. Each layer must be designed for high availability and low latency to ensure users receive timely fee estimates.

The data flow begins with the ingestion layer. This component continuously polls sources like Ethereum's eth_gasPrice, Polygon's gas station, and mempool services like Blocknative or EigenPhi. For historical analysis, it also archives data from block explorers. A critical design decision is whether to use a centralized backend or a decentralized oracle network like Chainlink Functions for data fetching. The ingested raw data—including base fees, priority fees, pending transactions, and network congestion metrics—is then streamed to a message queue like Apache Kafka or RabbitMQ for reliable processing.

In the processing layer, services consume the raw data stream to calculate key metrics. This involves computing Estimated Gas Prices (low, average, high) using algorithms that weigh recent block data, predicting optimal transaction timing, and identifying periods of low congestion. Processed data is stored in a time-series database like TimescaleDB or InfluxDB for efficient querying of historical trends, and in a standard SQL database for user-specific data like saved addresses or alert preferences. This layer often employs caching with Redis to speed up frequent queries for current gas prices.

The presentation layer exposes this data to end-users. A frontend built with frameworks like React or Vue.js fetches data from a backend API (e.g., built with Node.js or Python/FastAPI). The dashboard should visualize data clearly: line charts for fee trends over time, heatmaps for identifying cheap times to transact, and real-time alerts when gas prices drop below a user-set threshold. For developers, the system should also offer a public API, allowing integration into other dApps or wallet interfaces for automated fee optimization.

To handle multi-chain complexity, the architecture must abstract chain-specific details. A common pattern is to use a unified gas model adapter that converts fee data from different chains (e.g., Ethereum's gwei, Solana's lamports, Avalanche's nAVAX) into a standardized unit for comparison. Security is paramount; the backend must validate all user-input addresses, implement rate limiting, and secure API keys for data providers. The entire system should be deployable via Docker containers and orchestrated with Kubernetes for scalability during network congestion events.

Finally, consider adding advanced features like simulation using tools like Tenderly to estimate transaction costs before broadcasting, or batch transaction analysis to identify opportunities for gas savings through bundling. The dashboard's value increases by providing not just data, but concrete recommendations, such as suggesting the best time to execute a large NFT mint or a DeFi strategy based on predictive models trained on historical gas patterns.

Ethereum gas prices are dynamic and vary based on network congestion. To provide accurate recommendations, your dashboard must pull data from primary sources. The most direct method is using the eth_gasPrice JSON-RPC call to an Ethereum node, which returns the base fee plus the miner tip. However, for a more nuanced view, you should integrate with services like the Etherscan Gas Tracker API or Blocknative's Gas Platform API. These services aggregate data from pending transaction pools, providing estimates for low, standard, and fast priority levels, often measured in Gwei (1 Gwei = 10^9 Wei).

A robust implementation fetches from multiple endpoints to ensure reliability and accuracy. Here's a basic Node.js example using axios to fetch from Etherscan's public API (requires a free API key):

javascriptCopy
const axios = require('axios');
const API_KEY = 'YOUR_ETHERSCAN_KEY';
const url = `https://api.etherscan.io/api?module=gastracker&action=gasoracle&apikey=${API_KEY}`;

async function fetchGasData() {
  try {
    const response = await axios.get(url);
    const result = response.data.result;
    console.log('Safe Low:', result.SafeGasPrice, 'Gwei');
    console.log('Propose:', result.ProposeGasPrice, 'Gwei');
    console.log('Fast:', result.FastGasPrice, 'Gwei');
    return result;
  } catch (error) {
    console.error('Failed to fetch gas data:', error);
  }
}

This returns an object with estimated gas prices for different confirmation speeds.

For production dashboards, consider polling these APIs at regular intervals (e.g., every 15 seconds) and implementing a fallback system. If the primary provider fails, your service should seamlessly switch to a secondary source, such as directly querying a node via Alchemy or Infura. Store the fetched data in a structured format, typically an object containing timestamps and price tiers. This real-time data layer becomes the input for the analysis and visualization components in subsequent steps, enabling features like historical trend charts and predictive fee alerts.

EIP-1559 transactions have three key fee parameters: the base fee, priority fee (tip), and max fee. The base fee is algorithmically set and burned by the network. The priority fee is a tip paid directly to the validator. The max fee is the absolute maximum a user is willing to pay per gas. Your dashboard must fetch and display these values in real-time, typically using a provider like ethers.js or viem to query the latest block. Start by creating a component that calls provider.getBlock('latest') to access the baseFeePerGas field.

To calculate a recommended total fee, you must estimate the next base fee and suggest a competitive priority fee. The base fee adjusts by a maximum of 12.5% per block based on network congestion. A simple heuristic is to multiply the current base fee by 1.125 for a high-congestion estimate. For the priority fee, query historical blocks to analyze recent tip medians. Use the eth_feeHistory RPC method, which returns an array of priority fees per block. Calculate the 50th percentile (median) of the last few blocks to suggest a reliable tip.

Present the fee breakdown visually. Use a stacked bar chart or labeled segments to show the composition: one portion for the burned base fee and another for the validator tip. Clearly state the total maxFeePerGas (base fee estimate + priority fee). Include a dynamic explanation: "If the base fee is 15 Gwei and you add a 2 Gwei tip, your max fee is 17 Gwei. If the base fee rises to 16 Gwei at execution, you pay 16 + 2 = 18 Gwei, but only up to your 17 Gwei max, causing potential delay." This clarifies the risk of underpayment.

Implement user controls for fee customization. Provide input fields or sliders for users to manually adjust the priority fee and max fee. As they adjust, update the visual breakdown and the explanatory text in real-time. For advanced users, offer presets: "Low" (slow, low tip), "Market" (median tip), and "Aggressive" (high tip for next-block inclusion). Log the typical confirmation times for each preset based on recent chain data to set accurate expectations.

Finally, integrate this component with a transaction sender. When a user initiates a transaction, construct the EIP-1559 transaction object with the chosen maxFeePerGas and maxPriorityFeePerGas. Use your provider to send the transaction and monitor its status. Display a confirmation receipt that reiterates the fee breakdown, showing the actual effectiveGasPrice paid (which can be lower than the max fee) and the portion that was burned. This closes the feedback loop, educating users through real-world results.

The core value of a gas optimization dashboard is its ability to simulate savings before a user commits a transaction. This requires building an engine that can estimate the gas cost for two scenarios: executing transactions individually versus as a single batched call. You'll need to interact with a node or a service like the Ethereum Execution API to fetch current baseFee and priorityFee (tip) data. For accurate simulations, use the eth_estimateGas RPC call on the user's intended transactions, both individually and as a batch constructed with a contract like OpenZeppelin's Multicall3.

To calculate savings, your simulation logic should process the gas estimates. A batch transaction incurs a base cost plus the sum of the gas used by each internal call, while individual transactions each pay the base cost separately. The formula is: Savings = Σ(Individual Tx Gas) - (Batch Base Gas + Σ(Batched Call Gas)). Multiply the gas difference by the current gas price (in Gwei) and the ETH/USD exchange rate to display savings in fiat currency. This tangible dollar figure is far more impactful for users than gas unit counts alone.

Present the results clearly in the dashboard UI. A simple visualization could include: a savings meter showing percentage reduction, a side-by-side comparison of total costs, and a breakdown table listing each simulated transaction. For developers, also show the raw data: estimated gas units per call and the proposed calldata for the batch. This transparency builds trust and helps users verify the simulation's logic. Always include clear disclaimers that simulations are estimates and final on-chain costs may vary with network conditions.

For advanced features, implement historical analysis. Track a user's past transactions and calculate retrospective savings they could have achieved with batching. Use libraries like ethers.js or viem to decode historical transaction calldata and group similar interactions (e.g., multiple ERC-20 approve calls). Graphing this missed opportunity over time powerfully demonstrates the ongoing value of the tool. Consider integrating with Blocknative or Gas Station Network (GSN) relays to simulate sponsored transaction scenarios as well.

Finally, ensure the simulation is interactive. Allow users to manually add or remove transactions from the proposed batch and see the savings update in real-time. Provide an option to export the batch calldata or generate a ready-to-execute transaction object for wallets like MetaMask. By making the simulation both informative and actionable, you transform the dashboard from a passive display into an active gas optimization workshop for your users.

The core of your dashboard is the historical analysis module. This component processes the aggregated transaction data from the previous steps to identify spending trends. You should query for all transactions within a user-defined timeframe (e.g., last 30, 90, or 365 days) and group them by key dimensions. Essential metrics to calculate and display include: total gas spent (in ETH/USD), average gas price per transaction, spending frequency (transactions per day/week), and peak spending periods. Visualizing this data with time-series charts immediately shows users when their gas consumption is highest.

To add depth, segment the data by transaction type and destination. Use your decoded transaction logs to categorize spending into actions like DEX swaps, NFT minting, token approvals, or bridge transfers. A stacked bar chart showing gas cost per category reveals which DeFi activities are the most expensive for the user. Furthermore, analyzing the to address can highlight if a significant portion of fees is paid to a few frequently interacted-with protocols, which may indicate opportunities for bundling transactions or exploring alternative platforms with lower fees.

Implementing this requires efficient data handling. For on-chain queries, use The Graph subgraphs for popular protocols or direct RPC calls with batch processing for custom analysis. A practical code snippet for fetching and grouping data might use a library like ethers.js: const historicalTxs = await provider.getHistory(address, startBlock, endBlock);. For dashboard rendering, libraries like Recharts or Chart.js are ideal for creating the interactive line and bar charts. Ensure all values are converted to a stable fiat currency using historical price oracles from providers like CoinGecko or Chainlink to give users a true cost perspective.

Finally, surface actionable insights directly on the dashboard. Generate plain-language summaries based on the data, such as: "You spent 0.15 ETH on gas last month. 40% was on Uniswap swaps between 2-4 PM UTC. Consider scheduling swaps during lower network congestion." This transforms raw data into a personalized optimization guide. By clearly showing users their financial footprint, you empower them to make informed decisions, potentially saving substantial amounts on future transactions.

Your gas fee optimization dashboard is now a functional tool for tracking real-time baseFee, priorityFee, and maxFeePerGas across multiple networks like Ethereum, Arbitrum, and Polygon. To deploy it, host the frontend on a service like Vercel or GitHub Pages. For the backend, consider serverless functions (Vercel, AWS Lambda) to run your fee-fetching cron jobs, ensuring your API keys remain secure in environment variables. This setup provides users with a live, accessible application.

To enhance the dashboard, integrate more data sources. Connect to a service like Blocknative for mempool visualization or Etherscan APIs for historical fee analysis. Implementing user alerts—via email, Discord webhooks, or push notifications—when gas prices drop below a configured threshold can provide immediate utility. Furthermore, adding support for Layer 2-specific metrics, such as proving costs on zkRollups or sequencing fees on Optimistic Rollups, will make your tool indispensable for advanced users.

Finally, consider open-sourcing your project on GitHub. This allows other developers to contribute, audit the code, and suggest improvements. Documenting your architecture, data sources, and deployment steps is crucial. For continued learning, explore resources like the EIP-1559 specification, the Chainlink Data Feeds for gas price oracles, and blockchain explorers' API documentation. Your dashboard is a foundation; building on it will deepen your understanding of Ethereum's economic mechanics and real-time data processing.