ZK-Rollups require historical data. A ZK validity proof only verifies state transitions. To reconstruct the current state, nodes must fetch and process the entire chain's transaction history, which is stored on Ethereum as expensive calldata.
zk-rollups-the-endgame-for-scaling
Blog
Why Your ZK-Rollup Will Be Crippled by Storage Proofs
ZK-rollups optimize execution, but their dependence on costly, latency-inducing Ethereum storage proofs for state access creates a fundamental performance ceiling. This analysis breaks down the coming bottleneck.
introduction
THE STORAGE BOTTLENECK
The Hidden Tax on ZK-Superiority
ZK-Rollups' cryptographic security creates a systemic, expensive dependency on Ethereum's historical data.
Storage proofs are the bottleneck. This data retrieval is not free or instant. Protocols like Herodotus and Lagrange build storage proofs to bridge this gap, adding latency and cost for every state sync or cross-chain message via LayerZero or Hyperlane.
Optimistic Rollups avoid this tax. They post full transaction data to L1, so their state is always natively reconstructible. This makes their canonical bridges and Chainlink CCIP integrations simpler and cheaper for frequent, low-value cross-chain actions.
Evidence: The cost to generate a storage proof scales with the historical data size. A ZK-Rollup like zkSync Era must pay this cost continuously for its native bridge, while Arbitrum's bridge only pays for L1 gas.
thesis-statement
THE DATA BOTTLENECK
Thesis: Storage Proofs Are the Next Scaling Wall
ZK-Rollup scalability is fundamentally limited by the cost and latency of generating proofs for historical state data.
The state is the bottleneck. ZK-Rollups compress computation, but proving access to historical on-chain data (storage proofs) for applications like bridges or perpetual swaps remains expensive and slow, creating a hard ceiling on TPS.
Provers are not databases. ZK circuits must re-execute state reads, making them I/O-bound. This contrasts with optimistic rollups like Arbitrum or Optimism, which only need to post raw calldata and defer fraud proofs.
Proof latency kills composability. A 10-minute proof generation time for a storage-heavy transaction breaks atomic cross-rollup swaps, undermining the interoperability promise of ecosystems like zkSync and StarkNet.
Evidence: A single storage proof for an Ethereum block header using RISC Zero can cost over $0.50 in compute, making micro-transactions for data-heavy dApps economically impossible at scale.
key-trends
THE DATA AVAILABILITY CRISIS
Three Trends Converging on the Bottleneck
The scaling promise of ZK-Rollups is being undermined by a fundamental mismatch: exponential state growth versus linear proof verification.
01
The Problem: State Growth Outpaces Proofs
ZK-Rollups compress execution, but the state (account balances, smart contract storage) still grows linearly with usage. Proving access to this ever-expanding state is the new bottleneck.\n- ZK-EVM state grows at ~50GB/year\n- Proving a simple balance check can cost >$1\n- Witness generation becomes the dominant cost, not the ZK-SNARK itself.
50GB+/year
State Growth
> $1
Per Proof Cost
02
The Solution: Stateless Clients via Storage Proofs
The industry's answer is to make clients stateless using cryptographic storage proofs (e.g., Verkle Trees, Binius). The rollup sequencer provides a proof that a piece of data exists in the canonical state, eliminating the need for the verifier to hold the full state.\n- Enables light clients to verify any state claim\n- Critical for cross-chain interoperability (LayerZero, Chainlink CCIP)\n- Shifts trust from consensus to cryptography.
~10KB
Proof Size
Trustless
Verification
03
The Bottleneck: Proof Generation Latency
Generating a storage proof requires querying and proving against the latest state root. With state scattered across decentralized networks (Ethereum, Celestia, EigenLayer), fetching and proving this data introduces fatal latency.\n- Current proofs can take 10s of seconds to generate\n- Makes real-time intents (UniswapX, Across) impossible\n- Centralized sequencers become a necessity, breaking decentralization.
10-30s
Proof Time
~500ms
Target Latency
04
The Architecture: Prover Networks & Cache Layers
Solving this requires a new infrastructure layer: decentralized prover networks (e.g., RiscZero, Succinct) specializing in low-latency storage proofs, paired with ephemeral data cache layers.\n- Specialized hardware (GPUs/FPGAs) for proof acceleration\n- In-memory state caches for hot data\n- Brings proof time down to <1 second for critical paths.
< 1s
Proof Target
DeFi First
Use Case
05
The Consequence: Fragmented Security Models
Offloading proof generation creates a new trust vector. The security of your ZK-Rollup now depends on the liveness and honesty of an external prover network, not just Ethereum.\n- Introduces prover liveness as a new failure mode\n- Economic security of prover networks is untested at scale\n- Creates a meta-game of proving the proofs.
New Attack
Vector
$?B
Security at Risk
06
The Future: Integrated Proof Markets
The endgame is a unified marketplace for verification. Rollups will post proof requests, and a competitive network of specialized provers (storage, execution, validity) will bid to generate them, creating a modular security economy.\n- Decouples proof generation from sequencing\n- Drives costs down via competition\n- Enables universal verification for any chain or state.
Market
Driven Cost
Universal
Verification
ZK-ROLLUP STORAGE ARCHITECTURES
The Proof Burden: A Comparative Look
Comparing the data availability and historical data access models that determine a ZK-Rollup's long-term viability and user experience.
| Core Metric / Capability | On-Chain Data (e.g., zkSync Era) | External DA + Validity Proofs (e.g., StarkNet) | EigenDA + Restaking |
|---|---|---|---|
Data Availability (DA) Cost per Byte | $0.0012 (Calldata) | $0.0003 (Celestia) | $0.0001 |
Prover Cost for Historical State Access | $50-200 per proof | $5-20 per proof | TBD (Relies on AVS) |
Time to Generate Storage Proof (Trusted) | < 1 sec (Local) | 2-5 sec (DA Layer Query) | 5-30 sec (AVS Network) |
Time to Generate Storage Proof (Untrusted) | N/A (Data On-Chain) | 2-5 sec (DA Layer Query) | 5-30 sec (AVS Network + Challenge) |
Censorship Resistance | Weak (DA Committee) | Strong (Ethereum Slashing) | |
Requires Active Data Archival | |||
Can Prune Old State from Nodes | |||
Protocols Using This Model | zkSync Era, Linea | StarkNet, Kakarot | Eclipse, Layer N |
deep-dive
THE PROOF OFFSET
Anatomy of a Bottleneck: From Merkle Proofs to User Experience
The core scaling promise of ZK-rollups is undermined by the latency and cost of generating storage proofs for cross-chain interactions.
The finality illusion is the primary user experience failure. A user's transaction finalizes on the rollup in seconds, but accessing assets on Ethereum or another chain requires a 7-day withdrawal delay. This delay is a security feature, but users perceive it as a broken promise.
Bridging solves latency, not cost. Services like Across Protocol and Stargate use liquidity pools to provide instant exits, masking the delay. However, they must still pay the prover cost for the eventual storage proof, which is passed to the user as a fee, making frequent small transfers economically unviable.
Merkle proofs are the bottleneck. Every cross-chain message requires a ZK-SNARK proof that a specific state root exists in Ethereum's history. Generating this proof is computationally intensive, creating a prover monopoly where services like Herodotus and Lagrange compete on cost, but the fundamental expense remains high.
Proof aggregation is the only path. The industry's scaling thesis relies on proof aggregation networks like Succinct and Brevis to batch proofs across many rollups. Without this, the cost of proving a $10 transfer will remain a significant percentage of the transaction value, crippling micro-transactions and composability.
counter-argument
THE FUTURE IS NOT NOW
Counterpoint: "Verkle Trees & Stateless Clients Will Save Us"
Verkle trees are a long-term L1 scaling solution that does not solve the immediate data availability and proof latency bottlenecks crippling ZK-rollups.
Verkle trees enable stateless clients by shrinking proofs for Ethereum's state, but this is an L1 consensus-layer upgrade. It does not address the core data availability (DA) problem for rollups, which rely on posting transaction data to L1 for security.
ZK-rollups need historical data proofs, not just state proofs. A stateless client verifies the current state. A ZK-rollup prover must cryptographically prove the correctness of historical execution, requiring access to the full historical data chain. This is a distinct computational workload.
Proof generation latency is the bottleneck, not state proof size. The time to generate a validity proof (SNARK/STARK) over a batch of transactions dominates rollup finality. Projects like Polygon zkEVM and zkSync Era optimize provers, not wait for Verkle trees.
Evidence: Ethereum's Verkle tree implementation is a multi-year roadmap item. Meanwhile, ZK-rollups are deploying today with solutions like Celestia/EigenDA for modular DA and dedicated prover networks to bypass these limitations.
protocol-spotlight
STRATEGIC RESPONSES
How Leading Rollups Are (Trying to) Mitigate This
Rollups are deploying a mix of cryptographic and economic strategies to circumvent the latency and cost of on-chain storage proofs.
01
The StarkNet Stack: Layer 3s & Volition
StarkWare's architecture pushes the problem down a layer. StarkNet L3s inherit L2 security but post only state diffs to L2, minimizing expensive L1 calls. Volition mode lets users choose data availability, allowing high-value apps to use on-chain proofs while others use cheaper off-chain storage like Celestia or EigenDA.\n- Key Benefit: Granular cost/security trade-offs.\n- Key Benefit: Isolates proof verification overhead from user transactions.
~100x
Cheaper L3 Txs
Modular DA
Flexibility
02
zkSync's Boojum & Async Proofs
Boojum, zkSync's new STARK-based prover, is designed for cost-efficient recursion. The real play is asynchronous proof generation, where proofs are verified on L1 only after a delay, batching many L2 blocks into one proof. This amortizes the fixed cost of the storage proof over thousands of transactions.\n- Key Benefit: Amortizes L1 verification cost across a batch.\n- Key Benefit: Recursive proofs enable future proof aggregation across chains.
10-100x
Amortization
Async
Verification
03
Polygon zkEVM & the AggLayer
Polygon's answer is unification via the AggLayer, a shared bridge and proof coordination layer. It aggregates proofs from multiple zkEVMs (and eventually other chains like Miden) into a single proof submitted to Ethereum. This shared cost model and unified liquidity pool directly attacks the economic burden of per-chain storage proofs.\n- Key Benefit: Shared security and cost for all connected chains.\n- Key Benefit: Creates a unified liquidity network, reducing bridge dependency.
Shared
Proof Cost
Unified
Liquidity
04
Arbitrum BOLD & the One-Proof Fallback
While an optimistic rollup, Arbitrum's BOLD (Bounded Liquidity Delay) dispute protocol is relevant. It allows for trust-minimized exits without a live challenger by relying on a single, on-chain fraud proof. This is a clever sidestep: instead of constantly proving state, you only run the expensive proof in a dispute—a probabilistic cost model.\n- Key Benefit: Pay-for-dispute cost model vs. constant proof cost.\n- Key Benefit: Maintains strong security guarantees for capital withdrawals.
Probabilistic
Cost Model
Strong
Exit Security
takeaways
THE DATA AVAILABILITY TRAP
TL;DR for Architects and Investors
Your ZK-Rollup's performance and decentralization are bottlenecked by its reliance on centralized data availability layers.
01
The Problem: L2 as a Data Client
Your rollup is a data client, not a sovereign chain. It's forced to trust a single DA layer (e.g., Ethereum calldata, Celestia, EigenDA) for data ordering and availability. This creates a single point of failure and vendor lock-in, ceding control over your most critical infrastructure.
1
DA Provider
100%
Dependency
02
The Solution: Sovereign Proof Verification
Shift from data availability to data verification. Use storage proofs (like Lagrange, Brevis, Herodotus) to allow your rollup's sequencer to post data anywhere (e.g., Arweave, Filecoin, even AWS S3). The validity proof only needs to attest that the data is available and correct at a specific time, breaking the DA monopoly.
- Enables Multi-Chain State: Pull verified state from Ethereum, Solana, or Cosmos.
- Reduces Costs: Bypass expensive on-chain DA, cutting fees by ~80%.
~80%
Cost Cut
Multi-Chain
State Access
03
The Architecture: Modular Proof Stack
This isn't a monolith. It's a stack: a ZK-VM (Risc Zero, SP1) for proof generation, a Proof Network (like Avail's Nexus) for aggregation and routing, and a Light Client for on-chain verification. Your rollup becomes a coordinator, not a custodian, enabling trust-minimized bridges and shared sequencer models without new trust assumptions.
3-Layer
Stack
Zero-Trust
Bridges
04
The Competitor: Celestia's MoAT
Celestia's entire value proposition is data availability sampling (DAS). Storage proofs render DAS optional for rollups. If rollups can cryptographically verify data availability off-chain, they don't need to pay Celestia for consensus. This attacks the core economic model of modular DA layers, pushing them towards commoditization.
$10B+
Market Cap at Risk
Commodity
DA Future
05
The Bottleneck: Proof Generation Cost
The fatal flaw. Generating a storage proof for 1MB of data can cost ~$0.10-$1.00 and take ~10-60 seconds today, versus ~$0.01 and ~12 seconds for posting to Ethereum blobspace. Until ZK-proving hardware (e.g., Cysic, Ulvetanna) achieves 1000x cost reduction, pure storage-proof rollups are economically non-viable for high-throughput apps.
~$1.00
Proof Cost
1000x
Improvement Needed
06
The Hybrid Path: Blobstream + Proofs
The pragmatic interim architecture. Use a cost-effective DA layer (EigenDA, Celestia) for bulk data via bridges like Blobstream or EigenDA's AVS, but use storage proofs for selective, high-value state verification. This blends low baseline costs with the sovereign security of cryptographic verification for cross-chain intents and settlements.
Hybrid
Model
Best of Both
Worlds
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