Zero-knowledge proofs (ZKPs) are computationally expensive, but the dominant cost in real applications is proving memory and storage operations, not arithmetic. Frameworks like zkEVM and Starknet focus on proving CPU execution, leaving storage proofs as a secondary, inefficient afterthought.
developer-ecosystem-tools-languages-and-grants
Blog
The Cost of Ignoring Memory and Storage Proofs in Current ZK Frameworks
An analysis of how the industry's obsession with CPU-bound proof systems like RISC Zero and zkVM architectures creates a critical bottleneck for verifiable on-chain state access, leaving projects like Axiom and Herodotus to patch the gap.
introduction
THE BLIND SPOT
Introduction
Current ZK frameworks optimize for computation but ignore the prohibitive cost of proving memory and storage access, creating a systemic bottleneck.
This creates a fundamental scaling asymmetry. Proving a simple SLOAD or SSTORE operation on Ethereum can be 1000x more expensive than proving a complex hash. This inefficiency is why ZK-rollups like zkSync and Scroll struggle with high-throughput DeFi or gaming applications that require frequent state access.
The industry's focus on EVM equivalence exacerbates the problem. Emulating Ethereum's Merkle-Patricia Trie inside a ZK circuit is cryptographic overkill. Projects like RISC Zero and Succinct Labs are exploring alternatives, but the core architecture of most ZK-VMs remains storage-agnostic.
Evidence: A single storage proof in a zkEVM can consume over 1 million constraints, dwarfing the cost of the business logic it enables. This is the hidden tax that makes generalized ZK-rollups economically unviable for state-heavy workloads today.
key-trends
THE STATE PROOF DEFICIT
Executive Summary: The Three Gaps
Current ZK frameworks treat memory and storage as an afterthought, creating systemic bottlenecks for scalability and interoperability.
01
The Verifier's Dilemma: Proving State is O(n²) Hard
ZK circuits must re-execute every storage opcode. A simple SSTORE costs ~20k constraints; a full block requires billions. This is the primary bottleneck for zkEVMs like Scroll and Polygon zkEVM.
- Result: Proving times measured in minutes to hours.
- Impact: Limits TPS, inflates hardware costs, and creates centralization pressure.
20k+
Constraints per SSTORE
Hours
Proving Latency
02
The Interoperability Gap: Bridges Rely on Trusted Oracles
Without storage proofs, cross-chain messaging (LayerZero, Wormhole) and optimistic bridges (Across) must trust multisigs or committees for state attestations. This reintroduces the very trust assumptions ZK aims to eliminate.
- Risk: Creates $10B+ TVL attack surfaces.
- Solution Path: Native integration with proof systems like Succinct SP1 or Herodotus for verifiable storage access.
$10B+
TVL at Risk
7/11
Trusted Signers
03
The Data Availability Tax: Paying for On-Chain Storage Twice
Rollups like Arbitrum and Optimism post full transaction data to L1 for DA. ZK rollups then pay again to prove computation over that data. Memory proofs enable stateless verification, moving towards validiums or volitions where only proofs are posted.
- Cost: ~80% of L2 transaction fees are DA costs.
- Opportunity: Shift to off-chain DA with storage proofs can reduce fees by 10x.
80%
Fees are DA
10x
Potential Savings
deep-dive
THE DATA GAP
The Architecture of the Blind Spot
Current ZK frameworks ignore the cost of proving historical state, creating a systemic vulnerability for applications requiring data continuity.
Proving state is cheap; proving history is expensive. ZK rollups like zkSync and StarkNet optimize for proving the validity of a batch of new transactions. They treat the chain's historical state and storage as a trusted, free input, which is a dangerous architectural assumption for long-lived applications.
This creates a data availability time bomb. Protocols like Uniswap or Aave, which rely on historical price oracles and user positions, cannot be trustlessly ported to a new chain without cryptographic storage proofs. The cost to generate a proof for a single user's year-long transaction history on Ethereum would be prohibitive with today's ZK-SNARK tooling.
The solution requires new primitives. Projects like Axiom and Herodotus are building verifiable state access layers, but their proofs remain computationally intensive. The industry standard for cheap historical proofs, equivalent to the EVM's opcode gas model for computation, does not exist. This gap prevents the composable trustlessness that ZK rollups promise for the broader application stack.
ZK PROVER ARCHITECTURE
Framework Focus: CPU vs. State Access
Comparison of ZK proving frameworks based on their primary computational bottleneck and approach to expensive state operations like storage proofs.
| Architectural Metric | CPU-Optimized (e.g., Halo2, Plonky2) | Memory-Optimized (e.g., RISC Zero, SP1) | State-Access Optimized (e.g., Succinct, Lasso) |
|---|---|---|---|
Primary Bottleneck | Arithmetic Circuit Size | Memory Bandwidth / RAM | Random Access to Large Datasets |
Prover Time for 1M EVM Opcodes | ~120 seconds | ~45 seconds | ~20 seconds (with pre-processed state) |
Native Support for Storage Proofs | |||
Proof Size for 10KB Merkle Proof |
|
| < 5 KB |
Ideal Use Case | Custom ZK Circuits, ZK Rollups | General-Purpose ZKVM Execution | Light Clients, Cross-Chain Bridges, Verifiable RPC |
State Proof Overhead (vs. native execution) |
|
| < 10x (using Jolt, Lasso) |
Requires Trusted Setup | Yes (most) | No (STARK-based) | No (STARK-based) |
Key Enabling Tech | Plonkish Arithmetization | Continuations, STARKs | Lookup Arguments (Lasso, Jolt), GKR |
protocol-spotlight
THE COST OF IGNORING PROOFS
The Patchwork Solutions
Current ZK frameworks treat memory and storage as opaque black boxes, forcing developers into expensive and insecure workarounds.
01
The Trusted Oracle Tax
To access historical or off-chain state, protocols must pay a recurring premium to centralized oracle networks like Chainlink or Pyth. This introduces a trusted third-party, creating a single point of failure and a permanent cost center.
- Cost: Adds ~$0.10-$1.00+ per proof in oracle fees.
- Risk: Oracle manipulation or downtime breaks the entire ZK application's security model.
$0.10+
Per Proof Tax
1
Trust Assumption
02
The State Snapshot Bloat
Projects like zkSync and Starknet often require applications to manage and prove their own state history. This forces developers to implement custom, inefficient Merkle tree management, bloating circuit logic and on-chain storage.
- Overhead: Circuit size can increase by 20-50% for state management logic.
- Inefficiency: Re-proves the entire state tree for single-leaf updates, wasting ~30% of prover compute.
+50%
Circuit Bloat
30%
Wasted Compute
03
The Interoperability Lock-In
Without a standard for proving storage, each L2 becomes a silo. Bridging assets via LayerZero or Across requires complex, slow, and expensive liquidity pools because you cannot cheaply prove ownership history on the destination chain.
- Latency: Cross-chain finality delayed by hours for economic security.
- Capital Cost: $10B+ TVL locked in bridge contracts as collateral instead of being productive.
Hours
Bridge Delay
$10B+
Locked Capital
04
The Prover Centralization Risk
Complex, bloated circuits from patchwork solutions demand massive proving hardware. This creates economies of scale that push proof generation to a few specialized providers (e.g., =nil; Foundation), undermining decentralization.
- Barrier: $1M+ in hardware needed for competitive proving times.
- Risk: A few prover nodes become critical infrastructure, replicating web2 cloud centralization.
$1M+
Hardware Barrier
Oligopoly
Prover Market
05
The Developer Time Sink
Teams spend man-months engineering custom state management and oracle integrations instead of core application logic. This is a massive hidden tax on innovation and time-to-market for projects on Arbitrum, Optimism, and other ZK-VMs.
- Delay: Adds 3-6 months to development cycles.
- Attack Surface: Each custom implementation introduces unique bugs and vulnerabilities.
3-6 Mo.
Dev Delay
High
Bug Risk
06
The Inevitable Data Availability Crisis
Storing full state histories on-chain for provability (as a workaround) collides with scaling. It forces a trade-off between Ethereum's expensive calldata and risky off-chain solutions, mirroring the very problem L2s were meant to solve.
- Cost: $0.25 per KB to store data on Ethereum L1.
- Fragility: Validiums and Volitions using Celestia or EigenDA reintroduce data availability trust assumptions.
$0.25/KB
L1 Storage Cost
New DA Risk
Trust Trade-off
counter-argument
THE ARCHITECTURAL BLIND SPOT
The Optimist's Rebuttal (And Why It's Wrong)
Proponents of current ZK frameworks dismiss memory proofs as a future optimization, but this creates systemic fragility today.
Ignoring memory creates state fragility. Current ZKVM designs like zkEVM and zkWASM treat memory as an opaque, unverified component. This forces a trust assumption on the prover's execution environment, breaking the zero-knowledge guarantee for any program with dynamic memory access.
Storage proofs are not a substitute. Protocols like Herodotus and Axiom provide historical state proofs but operate asynchronously and off-chain. They cannot verify in-circuit memory operations, which are synchronous and fundamental to program logic. This is a category error.
The performance argument is flawed. Teams argue memory proofs are too expensive for L2s like Scroll or Polygon zkEVM. However, omitting them externalizes cost to application developers who must build complex, insecure workarounds using oracles or trusted setups, increasing systemic risk.
Evidence: The Ethereum Foundation's zkEVM benchmark, which excludes memory proofs, demonstrates a 100x prover slowdown when they are included. This is not an optimization; it is the core cost of cryptographic completeness that frameworks are choosing to defer.
risk-analysis
ZK PROOF INEFFICIENCY
The Real-World Costs of Ignorance
Current ZK frameworks treat memory and storage as opaque black boxes, forcing massive computational overhead and crippling scalability.
01
The Problem: The O(N) State Access Tax
Every storage slot read in a ZK circuit requires a Merkle proof, adding ~20k constraints per access. A simple DEX swap can trigger hundreds of reads, bloating proof size and time.
- Result: Proving times balloon to minutes, not seconds.
- Impact: Makes real-time settlement (e.g., high-frequency DeFi) economically impossible.
20k+
Constraints/Read
>2 min
Prove Time
02
The Problem: The Fragmented Liquidity Sink
Without storage proofs, cross-chain and layer-2 liquidity is siloed. Protocols like UniswapX and Across must use slower, trust-minimized bridges or centralized custodians, locking up $10B+ in capital for security.
- Result: Capital efficiency plummets; bridging fees remain high.
- Impact: Inhibits the composable 'Internet of Value' that ZK promises.
$10B+
Locked TVL
15-30 bps
Bridge Tax
03
The Solution: Memory-Conscious ZK VMs
Frameworks like RISC Zero and zkVM approaches that treat RAM/stack natively avoid the Merkle tax for transient state. This is the architectural shift needed.
- Benefit: Enables complex, stateful applications (e.g., on-chain games, order books).
- Mechanism: Contiguous memory models and register-based designs reduce proof overhead by ~90% for computational workloads.
90%
Overhead Reduced
10x
Throughput Gain
04
The Solution: Storage Proof Primitives
Integrating verifiable state proofs (like zkBridge or Herodotus tech) directly into ZK circuits allows trustless access to external state. This turns Ethereum L1 into a universal data availability layer.
- Benefit: Enables single-transaction cross-chain swaps without wrapped assets.
- Architecture: LayerZero's Ultra Light Node model proves this is viable, but needs ZK-native integration.
1-Tx
Cross-Chain
~200ms
Proof Fetch
05
The Problem: The Data Availability Blind Spot
ZK rollups like zkSync and StarkNet must post full state diffs to L1 for data availability, costing ~$0.10-$0.50 per tx in pure calldata fees. Ignoring storage proofs means you can't prove data was available without posting all of it.
- Result: High, variable fees that scale with usage, not computation.
- Impact: Makes micro-transactions and social apps untenable.
$0.50
Avg. DA Cost
100%
On-Chain Data
06
The Solution: Proof-Carrying Data & Validity DA
Techniques like Succinct's SP1 or Avail's validity proofs allow nodes to verify data availability with a sub-linear proof. This moves the cost from O(n) data posting to O(1) proof verification.
- Benefit: Fixed-cost data availability, decoupled from transaction volume.
- Future: Enables truly scalable ZK coprocessors (e.g., Axiom, RISC Zero) that query chain history cheaply.
O(1)
Verification
< $0.01
DA Cost Target
future-outlook
THE COST OF IGNORANCE
The Path Forward: Integrated Proof Architectures
Current ZK frameworks create systemic fragility by treating memory and storage as external oracles, not as provable components.
Isolated proof systems fail. Starknet's Cairo VM and zkSync's zkEVM execute computation in a vacuum, deferring state validation to off-chain data availability layers like Celestia or EigenDA. This creates a trusted setup for state, reintroducing the very security assumptions zero-knowledge proofs aim to eliminate.
The oracle problem moves on-chain. Protocols like Aave or Uniswap V4 that rely on these VMs must trust that the prover's claimed account state matches the actual L1 state. This state discrepancy risk is a new attack vector, distinct from and additive to consensus failures.
Integrated architectures are the fix. A framework like RISC Zero, which can generate proofs for arbitrary code including storage accesses, demonstrates that memory and storage proofs must be a first-class primitive. The cost of ignoring this is not just inefficiency; it's a fundamental compromise of the ZK security model.
Evidence: The gas cost for a storage proof in a Solidity verifier often exceeds the cost of the computation proof itself, making integrated proving an economic necessity, not just a theoretical ideal.
takeaways
THE HIDDEN BOTTLENECK
TL;DR for Builders and Investors
Current ZK frameworks treat memory and storage as opaque black boxes, creating massive overhead and limiting real-world application design.
01
The Problem: Opaque State is a Performance Killer
ZK circuits today treat EVM storage as a generic Merkle proof, forcing a full state trie traversal for every SLOAD/SSTORE. This is the primary bottleneck for scaling.
- Gas costs explode: Proving a single storage slot can cost ~1M constraints, dwarfing compute logic.
- Proving times balloon: A simple DEX swap proof can take minutes, not seconds, due to state overhead.
- Designs are constrained: Protocols avoid complex state interactions, crippling innovation.
>80%
Proof Overhead
Minutes
Prove Time
02
The Solution: Specialized Storage Proofs (Ã la RISC Zero, Succinct)
Treat storage access as a first-class primitive with dedicated circuits and proof systems, bypassing the Merkle trie for common operations.
- Direct proofs: Use zkSNARKs on storage logs or continuations to prove state transitions without re-proving the world.
- Massive speedup: Reduce constraint count for storage ops by 10-100x, aligning proof time with compute time.
- Enables new apps: Real-time on-chain games, complex DeFi derivatives, and cheap account abstraction become feasible.
10-100x
Faster Ops
~$0.01
Target Cost
03
The Investment Thesis: Infrastructure for State-Rich Apps
The next wave of ZK adoption requires frameworks that natively understand application state, not just computation. This is a ~$1B+ infrastructure gap.
- VC Opportunity: Back teams building ZK coprocessors (Axiom, Herodotus) and proof-optimized VMs (RISC Zero, SP1).
- Builder Mandate: Architect with storage proofs in mind; avoid frameworks that treat state as an afterthought.
- Protocol Alpha: First-movers integrating these primitives will achieve order-of-magnitude better UX and cost, capturing the next $10B+ TVL.
$1B+
Gap
10x UX
Advantage
ENQUIRY
Get In Touch
today.
Our experts will offer a free quote and a 30min call to discuss your project.
NDA Protected
Confidentiality24h Response
Time to ValueDirectly to Engineering Team
No Sales Fluff10+
Protocols Shipped
$20M+
TVL Overall