How are crypto transaction fees calculated during network congestion?

Core mechanisms during congestion

When a blockchain becomes congested, transaction fees are set by competition for limited block space. In first-price auction systems like Bitcoin, users attach a fee and miners choose transactions that maximize their revenue, so transactions with higher fees are confirmed sooner. In account-based systems such as pre-London Ethereum, users similarly bid a gas price to compete for inclusion. This basic competitive model means that under congestion the marginal fee rises until demand matches the supply of block capacity. Tim Roughgarden, Columbia University, has analyzed how different auction rules change user incentives and latency trade-offs in such fee markets, showing that auction design directly affects predictability and strategic bidding.

Ethereum’s fee-market reform introduces a different mechanism. The London upgrade implemented EIP-1559, a design discussed publicly by Vitalik Buterin, Ethereum Foundation, which replaces pure first-price bidding with a dynamically adjusted base fee that is burned and a separate tip that goes to validators. The base fee automatically increases when blocks are fuller than a target utilization level and decreases when they are underutilized, smoothing short-term price spikes and making fee estimation more predictable for users. During severe congestion the base fee can still rise substantially because it adjusts toward clearing the market, but since part of the demand is absorbed by the tip market, some of the most extreme bidding behavior is moderated.

Causes, consequences and real-world impacts

Network congestion arises from sudden spikes in demand, such as popular token drops, decentralized finance auctions, or large-scale on-chain games, and from structural limits on block size and block time. Consequences during congestion extend beyond higher direct costs. Users and small-value participants may be priced out or forced to wait, which concentrates access among those who can pay premium fees and can affect cultural and territorial inclusivity where on-chain access substitutes for traditional banking. Congestion also creates incentives for front-running and fee-bumping strategies that complicate user experience and raise systemic inefficiencies, a dynamic that researchers including Emin Gün Sirer, Cornell University, and others have examined in the context of blockchain incentives.

Mitigations and broader trade-offs

Practical responses to congestion include wallet fee estimation tools that monitor the mempool and recommend fees, transaction batching, and migration to layer-two scaling solutions such as payment channels and rollups that move transactions off the base layer. Protocol-level changes like variable block limits and fee burning change long-run incentives and security economics, because miner or validator revenue composition shifts when base fees are removed from rewards and burned. Auction-theory work by Tim Roughgarden and others highlights that no single mechanism is free of trade-offs: predictability, validator incentives, user fairness, and decentralization must be balanced according to the network’s goals. Understanding fees during congestion therefore requires both a technical grasp of the blockchain’s fee rule and attention to the human and economic contexts that drive demand and shape who benefits or loses when capacity is scarce.