Calculate the computational and gas costs for generating and verifying zero-knowledge proofs across different proof systems (Groth16, PLONK, STARK, etc.) and deployment targets (Ethereum, Polygon zkEVM, zkSync Era, StarkNet). Optimize your ZK implementation for cost efficiency.
Use Case Presets
Proof Configuration
Most efficient verification, requires trusted setup per circuit
Pricing Configuration
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The ZK Proof Cost Calculator helps blockchain developers and architects estimate the computational and financial costs of implementing zero-knowledge proofs. Compare different proof systems like Groth16, PLONK, and STARK across various L1 and L2 deployment targets to find the most cost-effective solution for your use case. Whether you're building a privacy-preserving DEX, zkRollup, or identity verification system, understanding proof generation and verification costs is essential for architecture decisions.
Zero-knowledge proofs allow one party to prove knowledge of information without revealing the information itself. However, this cryptographic magic comes at a computational cost. Proving costs involve the time, CPU cycles, and memory required to generate a proof. Verification costs include the on-chain gas required to validate the proof. Different proof systems offer varying trade-offs: Groth16 offers small proofs but requires a trusted setup, STARKs are transparent and quantum-secure but produce larger proofs, while PLONK provides universal setups with moderate proof sizes. Understanding these trade-offs is crucial for optimizing your ZK application.
Cost Estimation Formula
Total Cost = (Proving CPU Cost × Time) + (Verification Gas × Gas Price × ETH Price)Choose the right proof system based on your specific requirements. Some applications prioritize proof size (bandwidth), others prioritize verification cost (gas), and some need fast proving times. Understanding the cost structure helps you make informed architectural decisions.
ZK applications can have significant operational costs at scale. A zkRollup processing millions of transactions needs accurate cost projections. Plan your infrastructure budget by understanding proving hardware requirements and on-chain verification expenses.
Different L2 networks offer varying gas costs and native ZK support. Ethereum mainnet verification is expensive but highly secure. L2s like Polygon zkEVM, zkSync Era, and StarkNet offer cheaper verification with different trade-offs. Compare options before committing.
Proof aggregation and batching can dramatically reduce per-transaction costs. Understand how batch size affects your cost-per-proof to optimize your rollup economics or proof aggregation strategy.
zkRollups bundle thousands of transactions into a single proof verified on L1. Groth16 and PLONK are popular choices for their small proof sizes and low verification costs. Consider proving infrastructure costs when designing your rollup economics.
Privacy-preserving DEXs and lending protocols use ZK proofs to hide transaction amounts and participants. Balance proof size (affects bandwidth) against proving time (affects user experience) when selecting a proof system.
ZK proofs enable proving age, citizenship, or credentials without revealing underlying documents. Bulletproofs work well for simple range proofs, while more complex identity attestations may require PLONK or Groth16.
Proving that ML model inference was performed correctly is a growing use case. Large circuits require systems that scale well with constraint count. Consider STARK or Halo2 for very large computations.
ZK bridges prove state transitions across chains without trusted intermediaries. Recursive proofs (Halo2, STARK) enable efficient proof composition for complex bridge architectures.
Private NFT ownership proofs and verifiable randomness in games use ZK proofs. Focus on user experience—fast proving times matter for interactive applications.
Constraint count measures the complexity of your ZK circuit—essentially the number of algebraic constraints that must be satisfied. More complex computations require more constraints. A simple hash verification might need 10,000 constraints, while verifying an ML model inference could require millions. Proving time and memory scale with constraint count.