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What are blobs on Ethereum?
Rollups execute transactions offchain and publish data or state information to Ethereum. That information must be available so independent participants can check state updates and, depending on the rollup, reconstruct activity or challenge an invalid transition. Data availability is a verification requirement; it does not mean every Ethereum node must permanently store every byte.
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Introduced by EIP-4844 at the Dencun upgrade in March 2024, blobs are data carried in special transactions through Ethereum’s consensus layer. The EVM cannot directly read blob contents. Blob gas has a fee market separate from ordinary execution gas, so the cost of publishing rollup data can move differently from the cost of executing Ethereum transactions.
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Optimistic rollups need transaction data available to support their challenge process. Validity-proof rollups establish correctness differently, but users and state updates still depend on access to relevant rollup data. The exact data and security arrangements vary by rollup.
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Are blobs permanent data storage?
No. Ethereum.org describes the protocol’s blob-serving window as roughly 18 days. After that limited period, the protocol does not guarantee that blob data remains available. By contrast, calldata is part of Ethereum’s permanent history. Blobs are therefore a temporary protocol service, not an archival-storage system.
Blob data is public while it is being served; blobs do not provide privacy. Anyone who needs the data for longer than the protocol’s serving window—such as an archive service or a rollup’s own users and operators—needs an appropriate longer-term source. Rollups also need arrangements that meet their own verification and security needs; the protocol’s minimum serving window should not be mistaken for a guarantee of indefinite access.
How does blob space affect Ethereum L2 fees?
A rollup buys Ethereum publication capacity, generally by batching and compressing transactions into data posted on L1. The publication cost is one input into its economics, not the entire fee a user pays. The rollup sets its own fee policy, and execution costs, batching, congestion, operating costs, and its decision about whether to pass savings through all matter.
When demand for blobs is below the market’s target, additional capacity can make data publication cheaper. When demand is high relative to capacity, the blob fee market can make posting more expensive. Increasing a target or maximum can ease scarcity, but the size and duration of any cost reduction depend on how much of the new capacity rollups use.
Coinbase Institutional’s post-Dencun analysis reported that average costs paid by L2s to Ethereum fell by more than 90%, or approximately Ξ200–300 per day on average in the analysis. The publication date was not specified in the retrieved page, and these are findings for that study’s Dencun-era comparison—not a current spending estimate. Coinbase also argued that much of the savings flowed through to users.
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A lower L1 posting bill can support lower user fees, a larger sequencer margin, or some combination. It does not guarantee that a user’s fee falls by the same percentage as the rollup’s data cost: a rollup may retain part of the savings, and other fee components can change at the same time.
Do cheaper blobs make L2s more profitable?
They can improve a rollup’s margin if its publication costs fall while its revenue and other costs stay broadly similar. But the effect varies by rollup and period, and lower data costs alone do not establish overall profitability.
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A separate Galaxy Research analysis of rollup margins found improved observed margins after onchain costs in the five full days following Pectra, compared with its preceding 60-day window. The result applies to the rollups and windows included in that analysis; it is not a current ranking or a forecast for every rollup.
What changed in blob capacity, and what is known now?
Protocol capacity has changed over time, so historical parameter values should not be presented as live settings. Galaxy Research reported that Pectra activated on May 7, 2025, and that EIP-7691 raised the blob target and maximum from 3 and 6 per block to 6 and 9. In its five full days after Pectra, average blob use was below the new target, and average daily blob-related costs were lower than in its preceding 60-day comparison period.
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The Ethereum Foundation’s August 22, 2025 protocol update described PeerDAS and blob-parameter-only (BPO) forks as part of the scaling path. Its November 6, 2025 mainnet announcement listed activation scheduling and planned BPO parameter adjustments. Ethereum.org’s Danksharding page, last updated June 24, 2026, describes full Danksharding as a later stage and data-availability sampling as necessary for further scaling.
The evidence cited here does not establish the live blob target, maximum, fee floor, utilization, or blob price on October 11, 2026. The 6/9 values are a Pectra-era report, not a verified current parameter reading.
Why nominal capacity does not tell the whole story
More capacity can reduce scarcity, but how efficiently rollups use it also matters. Blob usage depends on batch sizes, transaction packing, posting schedules, and bidding behavior. A rollup that waits to fill a blob may spread its fixed posting cost over more data, but that strategy can delay publication when transactions arrive slowly.
A 2023 Robust Incentives Group paper modeled this latency-versus-cost tradeoff before EIP-4844 went live. It explains the mechanism, not current measured rollup behavior. In a 2025 study of the early blob market, Lioba Heimbach and Jason Milionis identified suboptimal packing that at times resulted in up to 70% relative fee loss. Their study also described a subset-bidding problem tied to inflexible transaction structure. These findings are evidence of early market-design frictions, not proof that the same inefficiencies persist at that level today.
Data availability sampling (DAS) lets nodes check randomly selected portions of data instead of each node downloading all of it. Erasure coding adds redundancy so missing portions can be recovered. PeerDAS is part of Ethereum’s capacity-scaling path; these mechanisms address the challenge of making more data available without requiring every node to download every byte.
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Who pays for data availability, and what happens to Ethereum revenue?
In the basic flow, rollups pay Ethereum to publish data; users pay transaction fees to their rollup; Ethereum receives L1 fees associated with posting. The amount users ultimately bear depends on the rollup’s pricing and pass-through decisions.
If blob capacity is cheaper and a rollup posts less expensively, Ethereum can receive less direct fee income per unit of published data. That does not by itself determine Ethereum’s total revenue: usage, demand, other L1 activity, and fee-market conditions also matter. Nor does a cheaper blob market establish whether ETH is better or worse off overall.
There are trade-offs beyond fee income. Higher capacity can increase the amount of data consensus nodes must serve before pruning. Cheaper publication could also make new rollup applications more affordable and increase future demand, but that is a possible demand response, not a guaranteed outcome. The effects differ by stakeholder and by time horizon: users may benefit from lower costs, sequencers may retain some savings, and Ethereum may collect less per unit of blob data while supporting more activity.
How to compare blob economics across rollups
A blob-fee figure alone is not a fair basis for ranking L2s. A useful comparison needs the same period and consistent definitions, as well as the rollup’s data-publication and fee policies.
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- Publication method: establish whether the rollup posts to Ethereum using blobs, calldata, or another data-availability arrangement.
- Cost per transaction: account for batch size and compression, not just the aggregate fee paid to L1.
- User fees: check what users actually pay and whether lower posting costs are passed through.
- Sequencer economics: compare revenue with operating and onchain costs for the same stated measurement period.
- Posting behavior: consider latency, batch filling, and transaction inclusion alongside nominal capacity.
- Verification and security: understand the rollup’s assumptions and how users or independent verifiers can obtain relevant data.
- Market conditions: note dependence on Ethereum L1 fees, blob demand, and the capacity parameters in effect during the comparison.
The cited analyses are dated snapshots from 2024–2025, not a like-for-like scorecard of current rollups. A credible present-day comparison needs current, consistently measured data for each of these factors.
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