PLONK vs STARKs: Proving Stack

Key advantage: Single, reusable trusted setup (Perpetual Powers of Tau) supports any circuit up to a fixed size. This enables interoperability between different ZK-rollups (e.g., Aztec, ZKSync Era) and simplifies developer onboarding. It matters for ecosystems prioritizing modularity and rapid dApp deployment.

Key advantage: Faster prover times for complex, non-arithmetic-heavy circuits, leading to lower operational costs on commodity hardware. This matters for applications like private DeFi (zk.money) or gaming where latency and prover cost predictability are critical.

Key advantage: No trusted setup required, relying on collision-resistant hashes. Offers post-quantum security and theoretically superior scalability with proof sizes growing logarithmically with computation size. This matters for long-term, high-value state transitions (e.g., StarkNet, Polygon Miden) where trust minimization and massive scale are non-negotiable.

Key advantage: Extremely fast verification on-chain, often sub-second, due to simpler cryptographic operations. Enables high TPS finality for L2s. This matters for high-frequency trading protocols or global payment networks where low on-chain verification gas costs and throughput are paramount.

Slower proof generation: Prover times are generally higher than optimized STARKs, especially for large computations. This impacts user experience in high-frequency applications. Heavy prover hardware is often required, increasing operational costs for sequencers or rollups (e.g., Aztec, Scroll).

No trusted setup: STARKs rely on public randomness, eliminating a critical trust assumption and attack vector. They are also post-quantum secure, based on hash functions (e.g., Rescue, Poseidon) resistant to quantum attacks. This is critical for long-lived, high-value state (e.g., Starknet, Immutable X).

Larger proof sizes: STARK proofs are typically kilobytes (vs. PLONK's ~400 bytes), increasing L1 verification gas costs. While recursive STARKs (e.g., Stone) mitigate this, they add complexity. High arithmetic intensity requires careful circuit design to avoid prover bottlenecks.

EVM-centric ecosystems where small proof size minimizes L1 costs (e.g., Polygon zkEVM, zkSync Era). Teams prioritizing developer tooling with mature frameworks like Circom, Halo2, and Noir. Applications needing efficient recursion for proof aggregation.

Maximizing scalability for high-throughput validiums or volitions (e.g., dYdX, Sorare). Institutions requiring auditability and quantum-resistant security. Projects with complex, non-arithmetic logic where STARK's flexibility with AIR (Algebraic Intermediate Representation) excels.

Single, reusable ceremony: One-time setup supports any circuit up to a fixed size (e.g., ~2^20 gates). This enables rapid deployment for new applications like zkEVMs (Scroll, Polygon zkEVM) and private DeFi (Aztec) without new ceremonies. Trade-off: introduces a small, persistent trust assumption.

Faster prover times for complex logic: Optimized for circuits with high arity and custom gates. Benchmarks show ~10-100x faster proving than generic STARKs for EVM-equivalent execution. This is critical for high-throughput L2 rollups where block time is a constraint.

No trusted ceremony required: Proof security relies only on cryptographic hashes (collision-resistant). This eliminates a major trust vector and operational overhead, making it ideal for permissionless, credibly neutral protocols like Starknet and Immutable X.

Logarithmic verification scaling: Proof size and verification time grow O(log n) with computation size. Enables efficient proof recursion (proving proofs), a key advantage for validiums (dYdX, Sorare) and layer-3 architectures. Trade-off: larger proof sizes (~45-200 KB) vs. PLONK (~1 KB).

Bounded by initial setup: The universal trusted setup defines a maximum constraint limit. Scaling beyond this requires a new, large-scale ceremony. This can be a bottleneck for massively scalable apps aiming for orders-of-magnitude more constraints than initially planned.

High computational & memory cost: The FRI-based proving process is computationally intensive, leading to slower prover times and higher hardware requirements. This increases operational costs for high-frequency applications like a decentralized exchange matching engine.