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layer-2-wars-arbitrum-optimism-base-and-beyond
Blog

The Battle Between Plonk and STARKs is Really About Data Availability

A first-principles breakdown of how the choice between Plonk-family and STARK-family proof systems fundamentally dictates a ZK-rollup's data strategy, L1 costs, and long-term viability in a post-EIP-4844 world.

introduction
THE DATA

Introduction

The core conflict between Plonk and STARKs is not about proving speed, but about managing the cost and security of data availability.

Plonk prioritizes succinctness by minimizing the data a verifier needs. Its small proof size is a direct optimization for high-frequency on-chain verification, a design choice that aligns with the needs of Ethereum L2s like Scroll and Aztec.

STARKs trade size for scalability by generating larger proofs but requiring fewer cryptographic assumptions. This makes them optimal for batch processing massive datasets off-chain, a model leveraged by validiums like StarkEx and Polygon Miden which outsource data availability.

The real battle is economic. A Plonk-based zkRollup pays Ethereum's blob storage costs for its full data. A STARK-based validium avoids this cost but introduces a data availability committee as a new trust assumption. The choice dictates the system's security model and fee structure.

thesis-statement
THE DATA AVAILABILITY BATTLE

The Core Thesis: Proofs are a Sideshow, Data is the Main Event

The technical debate between Plonk and STARKs is a proxy war for competing data availability strategies that define scalability and decentralization.

Proofs verify, data scales. The cryptographic proof (Plonk, STARK) is a correctness guarantee. The data availability layer determines what is being proven and the system's throughput ceiling.

Plonk optimizes for on-chain data. Its succinct proofs and smaller trusted setups align with Ethereum's calldata-centric scaling, as seen in zkSync Era and Scroll. Efficiency is measured in L1 gas cost per transaction.

STARKs optimize for off-chain data. Their larger proof sizes trade on-chain footprint for faster proving, enabling high-throughput, dedicated data layers like Celestia or EigenDA, which power StarkNet and Polygon zkEVM.

The metric is cost-per-byte. The real competition is between Ethereum blob gas fees and modular DA layer pricing. The cheaper, more available data source wins the rollup war.

market-context
THE DATA BATTLEFIELD

The EIP-4844 Crucible

EIP-4844's blob space is the proving ground where Plonk-based SNARKs and STARKs compete on cost, proving time, and trust assumptions.

The core trade-off is cost vs. time. Plonk-based SNARKs, used by zkSync Era and Scroll, have smaller proofs and faster verification but require a trusted setup. STARKs, used by Starknet and Polygon zkEVM, eliminate this trust but generate larger proofs, consuming more of EIP-4844's limited blob data.

EIP-4844 pricing favors succinctness. Blob pricing makes data availability the dominant cost for L2s. SNARKs' smaller proofs are cheaper to post, but STARKs' trustless architecture offers stronger long-term security. This forces a direct economic comparison between cryptographic philosophies.

The bottleneck shifts to proving hardware. With cheap data settled, prover efficiency becomes the next battleground. STARKs are parallelizable, benefiting from GPUs. SNARKs often rely on CPUs. The winner optimizes the entire pipeline from execution to final L1 settlement.

Evidence: Starknet's Madara sequencer can batch proofs for thousands of TPS, but a single Cairo STARK proof is ~120KB, versus a Plonk proof at ~5KB. The blob cost differential is a direct function of this size gap.

THE DATA AVAILABILITY LENS

Architectural & Economic Trade-Offs: Plonk vs. STARKs

Comparing the core trade-offs between Plonk-based SNARKs and STARKs, framed by their divergent approaches to data availability and trust assumptions.

Feature / MetricPlonk-based SNARKs (e.g., zkSync, Scroll)STARKs (e.g., Starknet, Polygon Miden)

Cryptographic Assumption

Elliptic Curve Pairings (ECP)

Collision-Resistant Hashes (CRH)

Trusted Setup Required

Proving Time (approx.)

< 10 secs (on CPU)

30 secs (on CPU)

Verification Time

< 10 ms

< 100 ms

Proof Size

~ 400 bytes

~ 45-200 KB

Primary DA Cost Driver

On-chain verification gas

Proof publication & state diffs

Recursive Proof Support

Complex, requires pairing cycles

Native, via proof composition

Post-Quantum Security

deep-dive
THE DATA AVAILABILITY CONSTRAINT

First Principles: Why the Proof Dictates the Data

The choice between Plonk and STARKs is a direct trade-off between proof size and computational demand, which dictates the data availability strategy for the entire L2.

Proof size determines DA cost. A Plonk proof is ~400 bytes, cheap to post on-chain. A STARK proof is ~40-100KB, expensive to store on Ethereum. This cost difference forces STARK-based L2s like Starknet to adopt validiums or volitions, offloading data to a separate DA layer like Celestia or EigenDA.

Plonk's constraint is compute, not data. The Groth16 and Plonk proving systems require a trusted setup and are computationally heavier for complex circuits. This makes them ideal for ZK-rollups like zkSync Era and Scroll that prioritize Ethereum's security by posting all data as calldata, accepting higher proving costs for maximal security.

The battle is about security models. A STARK-based validium (e.g., Immutable X) trades Ethereum's data availability for scalability, introducing a new trust assumption. A Plonk-based rollup maintains Ethereum's full security guarantee. The proof system choice is the first and most binding architectural decision for any ZK L2.

Evidence: Starknet's planned transition to a 'volition' model, where users choose between rollup (full DA) and validium (off-chain DA) modes, is the direct, pragmatic consequence of its STARK proof's large size and the associated on-chain storage costs.

protocol-spotlight
THE DATA AVAILABILITY FRONTIER

Protocol Spotlights: Theory in Practice

The zero-knowledge proof wars are not just about proving speed; they are a proxy battle for the most efficient data availability strategy, defining the cost and scalability of L2s.

01

Plonk: The Pragmatic DA Optimizer

Plonk's small proof sizes and universal trusted setup prioritize minimizing on-chain verification costs, making it the de facto standard for EVM L2s where DA is expensive.

  • Key Benefit: ~45KB proofs keep Ethereum calldata costs manageable.
  • Key Benefit: Single trusted setup (Perpetual Powers of Tau) enables rapid protocol iteration for Scroll, Aztec, zkSync Era.
~45KB
Proof Size
1 Setup
Trusted Setup
02

STARKs: The Scalability Purist

STARKs use transparent setups and leverage massive parallelism to generate proofs for vast computational traces, but their large proof sizes (~100-200KB) demand cheap DA to be viable.

  • Key Benefit: Post-quantum security and no trusted setup eliminate a key trust assumption.
  • Key Benefit: StarkNet, Polygon Miden rely on high-throughput, low-cost DA layers (like Ethereum blobs or Celestia) to absorb the data cost.
Transparent
Setup Type
~200KB
Proof Size
03

The Real Bottleneck: Ethereum Calldata

The core economic trade-off is between proof generation cost (prover time) and data publication cost (L1 gas). zkEVMs using Plonk optimize for the latter, accepting heavier proving to save on scarce Ethereum blockspace.

  • Key Benefit: Aligns with the dominant economic model where L1 gas is the ultimate scarce resource.
  • Key Benefit: EIP-4844 blobs are a direct response to this pressure, benefiting STARK-based chains disproportionately.
>60%
Cost is DA
EIP-4844
Game Changer
04

The Modular Endgame: Proofs as a Commodity

As DA layers like Celestia, EigenDA, and Avail decouple, the proof system choice becomes less critical. The battle shifts to proof aggregation networks (e.g., Nebra, Gevulot) that can batch proofs from any system.

  • Key Benefit: L2s can choose the optimal prover for their VM, outsourcing DA and settlement.
  • Key Benefit: Creates a market where STARKs' proving efficiency may overtake Plonk's DA efficiency as the key metric.
Decoupled
DA Layer
Aggregation
Next Phase
counter-argument
THE DATA AVAILABILITY BATTLE

The Counter-Argument: Aren't Proofs Converging?

The Plonk vs. STARK debate is a proxy war for a more fundamental conflict over data availability strategies and their associated trade-offs.

Proof systems are converging on a common set of cryptographic primitives, making the raw proving math a commodity. The real differentiation lies in how they architect the data availability (DA) layer. Plonk-based systems like Polygon zkEVM and zkSync Era often assume a trusted, high-throughput DA source like Ethereum calldata, optimizing for cost within that constraint.

STARKs demand massive DA. Their proof sizes are tiny, but the prover must publicly commit to the entire execution trace. This creates a fundamental trade-off: STARK-based chains like Starknet and applications like Immutable X must either pay for expensive Ethereum blob storage or build a separate, robust DA layer, which fragments security.

The market is choosing sides. The rise of EigenDA and Celestia provides a spectrum of cost/security options for DA. Plonk chains can cheaply adopt them. STARK chains, with their larger initial data commitments, face a steeper integration curve, locking them into more expensive or bespoke solutions. The proof system you choose dictates your DA strategy.

risk-analysis
THE DATA AVAILABILITY BOTTLENECK

Strategic Risks: What Could Go Wrong?

The zero-knowledge proof war is not about proving speed; it's about the cost and security of the data those proofs need to verify.

01

The Problem: Plonk's Trusted Setup is a Red Herring

The real systemic risk for Plonk-based L2s like Scroll, zkSync Era, and Polygon zkEVM is not the ceremony, but their reliance on expensive, centralized data availability layers. Their security model collapses if the DA layer fails or censors them.\n- Security Model: Inherits the liveness assumptions of its DA provider (e.g., Ethereum, Celestia).\n- Cost Driver: ~80% of transaction fees go to posting call data on Ethereum L1.\n- Centralization Vector: A single DA provider becomes a critical point of failure and control.

~80%
Fee to DA
1
Critical Point
02

The Solution: STARKs Enable DA Sampling

STARKs' post-quantum security and transparent setup enable a more radical scaling path: verifiable data availability sampling (DAS). This allows light clients to securely verify data availability without downloading everything, breaking the monolithic chain model.\n- First-Principle Shift: Moves security from "trust a committee" to "cryptographically verify availability".\n- Enabler for Modularity: Makes decentralized DA layers like Celestia and EigenDA viable and secure.\n- Long-Term Trajectory: Paves the way for true L1 scaling via fractal scaling (e.g., Starknet's appchains).

Post-Quantum
Security
DAS
Enables
03

The Trap: Optimizing for Prover Cost, Not System Cost

Teams focusing solely on prover efficiency (e.g., faster Plonk provers) are solving the wrong problem. The dominant cost for users is L1 data posting fees. A system with a slightly slower, more expensive prover but radically cheaper DA will win.\n- Misaligned Incentives: Prover cost is a one-time dev/operator cost; DA cost is a recurring user cost.\n- Market Reality: Users choose the chain with the lowest fees, not the fastest prover.\n- Architectural Lock-in: Choosing a proof system without a DA strategy creates a long-term cost ceiling.

User Cost
Key Metric
Recurring
DA Expense
04

The Contender: zkRollups on Celestia vs. Validiums

The real battlefront is the security/cost trade-off of DA off L1. Validiums (STARK-based, e.g., StarkEx) use a committee, sacrificing some security for lower cost. zkRollups on Celestia use cryptographic DAS, offering a potentially superior middle ground.\n- Security Spectrum: Ethereum L1 DA > Celestia DAS > Validium Committee.\n- Cost Spectrum: Inverse of security. Celestia aims for near-Validium cost with stronger security.\n- Strategic Bet: The market will decide the optimal point on this trade-off curve, defining the winning stack.

Security<>Cost
Trade-Off
New Middle
Celestia
05

The Wildcard: EigenDA and Restaking Security

EigenDA doesn't use data availability sampling; it uses Ethereum's restaking ecosystem to secure data availability with economic guarantees. This creates a powerful, but novel, security model that competes directly with cryptographic DAS.\n- Security Source: Derived from slashing of restaked ETH, not cryptography.\n- Market Leverage: Taps into Ethereum's largest pool of trust (LSTs) for instant scale.\n- Risk Profile: Introduces systemic risk from restaking collateral re-use and governance of the EigenLayer ecosystem.

Economic
Security
Restaking
Leverage
06

The Endgame: Fractal Scaling and the L1-L2 Blur

The ultimate risk is building on a stack that cannot evolve. STARKs with DAS enable fractal scaling—any app can spawn a secure, scalable chain. Plonk-based stacks tied to a single DA layer may become legacy infrastructure, unable to compete with cost structures of fractal networks.\n- Winning Architecture: Recursive proofs + DAS enable infinite horizontal scaling (e.g., Starknet's Madara).\n- Loser Architecture: Monolithic L2s with fixed, expensive DA become cost-uncompetitive.\n- Strategic Implication: The proof system choice today dictates your platform's ceiling in 5 years.

Fractal
Scaling
Recursive
Proofs
future-outlook
THE DATA BATTLEGROUND

The Future: Data Markets and Specialized Rollups

The competition between Plonk and STARKs is a proxy war for control over the data availability layer, the true scaling bottleneck.

Proof system selection dictates data strategy. Plonk's smaller proofs require more on-chain data, making it ideal for rollups like Scroll that prioritize Ethereum compatibility. STARKs generate larger proofs but compress transaction data more aggressively, a tradeoff chosen by StarkNet and Polygon zkEVM for long-term scalability.

The real cost is data, not computation. Finalizing a zk-proof on-chain is cheap; the expense is publishing the data for fraud proofs. This creates a direct market for data availability solutions like Celestia, EigenDA, and Avail, which compete to be the cheapest bulletin board.

Specialized rollups optimize for data niches. A gaming rollup uses validity proofs for fast finality but posts minimal state diffs to a high-throughput DA layer. A DeFi rollup like dYdX v4 posts full data to Celestia for security, treating Ethereum solely as a settlement proof checker.

Evidence: StarkEx-powered dYdX processes trades with STARK proofs, but its migration to a Cosmos appchain centered on adopting Celestia for data availability, reducing costs by over 90% compared to Ethereum calldata.

takeaways
THE DATA AVAILABILITY FRONTIER

Key Takeaways for Builders and Investors

The choice between Plonk and STARKs is not just about proving speed; it's a strategic bet on how to scale data availability, the true bottleneck for L2s and L3s.

01

Plonk's Play: Optimizing for the Celestia & EigenDA Ecosystem

Plonk-based zkEVMs like Polygon zkEVM and Scroll prioritize smaller proof sizes (~45KB) and faster on-chain verification (~100ms). This makes them ideal for posting data to external DA layers like Celestia or EigenDA, where cost is driven by data blobs.\n- Key Benefit: Enables ~$0.001 per transaction DA costs by leveraging modular data availability.\n- Key Benefit: Faster finality for L2->L1 settlement, as the compact proof is cheap to verify on Ethereum.

~45KB
Proof Size
$0.001
Target Tx Cost
02

STARKs' Gambit: Proving Data Availability On-Chain

STARK-based systems like Starknet and zkSync leverage their asymptotic efficiency to create massive, single proofs for thousands of transactions. The proving cost is amortized, making the data availability cost on Ethereum the dominant expense.\n- Key Benefit: Unmatched long-term security by posting all data to Ethereum, avoiding light client assumptions of external DA.\n- Key Benefit: Superior scalability for appchains and L3s (via Starknet's Madara) where recursive proofs compress state transitions.

100k+
Txs/Proof
Ethereum
DA Layer
03

The Investor Lens: DA Strategy Defines the Market Map

Investment theses must shift from 'zk-team' comparisons to evaluating DA integration risk and cost models. A Plonk-chain using Celestia has a different risk profile (data availability security) versus a STARK-chain using Ethereum.\n- Key Benefit: Identify protocols positioned for hyper-scalable L3s (STARKs) vs. ultra-low-cost L2s (Plonk + Modular DA).\n- Key Benefit: Track blob usage metrics and DA cost per tx as the new fundamental KPIs for rollup performance.

KPIs
Cost per Tx & Blobs
2x
Market Segments
04

The Builder's Choice: Prover Architecture Dictates Product Design

Choosing Plonk vs. STARKs locks in your data availability strategy, which in turn defines your product's cost structure, security model, and upgrade path. This is a first-order product decision.\n- Key Benefit: Plonk enables faster iteration and lower costs for apps needing high throughput with modular DA.\n- Key Benefit: STARKs offer a more integrated, Ethereum-aligned security stack for DeFi primitives and high-value assets where DA security is paramount.

Architecture
Drives DA Choice
Product-Market Fit
Defined by Cost/Security
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