1. Why Appchains for Trading?

Appchains enable onchain CLOBs to align execution quality with economics, ensuring performance, fairness, and sustainability reinforce one another.

On general-purpose L1s, outcomes can degrade when latency, determinism, and MEV policy are outside venue control; Appchains restore policy control over execution and value capture.

Mature modular stacks enable faster deployment while maintaining credible assurances, thereby reducing the historical trade-off between speed, decentralisation, and fairness.

What is possible?

• Low‑latency, stable fills under stress narrow realised spreads toward established exchange benchmarks.

• CLOB‑defined ordering and information‑disclosure policies reduce extractive behaviours and improve fairness for takers and makers.

• Retained economics: fees and a share of sequencing or MEV to support liquidity programs and long‑term investment.

2. L1 vs. L2 Appchains

Appchains diverge along a critical dimension: sovereignty versus security inheritance. L1 Appchains prioritise sovereignty by operating independent validator sets with venue‑defined consensus, execution, and sequencing, maximising control over ordering, MEV policy, and fee economics at the cost of bootstrapping and potential liquidity isolation.

Hyperliquid exemplifies this approach with low‑latency BFT‑style consensus, order‑book‑native execution, and CLOB controlled sequencing to target deterministic ordering and MEV capture.

L2 Appchains inherit security from a base layer and deploy modular stacks for execution and Data Availability (DA), thereby compressing time-to-market and improving ecosystem connectivity while accepting constraints from base-layer finality, censorship resistance, and congestion.

Bullet, Hibachi, Lighter, and Paradex represent L2 Appchains, inheriting security from their base chain while utilising their own sequencers to maintain fast and predictable ordering for each.

Technical Enablers

With modular DA, ZK proving, and production rollup frameworks, CLOB appchains can deliver low-latency execution with verifiable integrity and venue-controlled sequencing, aligning performance, fairness, and economics without requiring the construction of a new L1.

DA lowers publication costs while preserving verifiability. ZK proofs shorten settlement and strengthen assurances, and rollup stacks compress time-to-market with configurable sequencing and routing.

Data Availability

Modular DA enables CLOB appchains to select the assurance, latency, throughput, and cost profile that best suits their needs by separating data publication from execution and settlement.

DA guarantees that batch data can be retrieved and verified, allowing rollups to execute offchain while proving correctness onchain via fraud or validity proofs, thereby reducing reliance on expensive calldata on an L1.

Ethereum as the Baseline

Ethereum is the highest-assurance data availability (DA) baseline, with ~12-second block times and economic finality after roughly 15 minutes (two epochs). It has long supported production-grade DA for Layer 2 rollups, which post transaction data as compressed batches (blobs) on mainnet. These blobs enable anyone to download, verify, and reconstruct L2 states securely, ensuring safety through fraud or validity proofs, despite offchain execution for scalability.

However, Ethereum's limited blockspace and gas costs create a scarce resource, leading to congestion and high fees during peak demand. EIP-4844 introduced efficiency for cheaper, temporary blobspace (pruned after ~3 weeks), significantly lowering rollup costs.

Still, alternatives like Celestia trade Ethereum's decentralisation and 12-second latency for lower costs and higher throughput, making Ethereum the reference for comparing DA performance.

Celestia (The Pioneer)

• @celestia pioneers modular DA, delivering ~20-27 MB/s with 12-second blocks. Its Data Availability Sampling (DAS) supports light client verification, reducing costs to ~60% below Ethereum's, although this varies depending on demand. The roadmap targets 1 GB blocks, but this isn't yet live. For trading CLOBs, its steady 12-second cadence is reliable, matching Ethereum's latency.

EigenDA (Ethereum Anchored)

• @eigen_da, tied to Ethereum, provides ~100 MB/s (V2) with a few-second DA latency, secured by restaked ETH and committee attestations with slashing (over 300x) Ethereum's throughput. Costs are ~90% lower than Ethereum. It aims for ~1 GB/s, a goal still in progress. This low latency and high throughput suit fast CLOB trading.

Avail (Validity Proofs)

• @AvailProject offers ~0.2 MB/s currently (~200 kB/s), with 20-second blocks and 40-second DA finality via validity proofs. Its Nominated Proof of Stake (NPoS) with KZG (Kate-Zaverucha-Goldberg) commitments targets ~40s verification, costing ~50-70% less than Ethereum. Tests show 1 GB blocks at 600ms (~1.6 GB/s), but this is not yet on the mainnet. It supports CLOBs with reliable finality, though latency is higher.

Hyve DA (Trading Optimised)

• @Hyve_DA, pre-testnet, claims ~1 GB/s throughput and sub-second finality, optimised for trading CLOBs with a permissionless design. These targets lack validation, with details and costs pending public benchmarks. Its focus on ultra-low latency makes it a speculative fit for high-speed trading.

Summary:

• Highest assurance + ETH composability: Ethereum blobs when institutional assurance outweighs cost/latency constraints.

• Lowest multi-second DA latency at high throughput: EigenDA V2 for active CLOBs with Ethereum-anchored security.

• Cost-efficient scaling with light-client verification: Celestia, with predictable cadence and sampling-based verification, is preferred.

• Validity proof-oriented DA finality across ecosystems: Avail with ~40-second DA finality suits operational needs.

Zero-Knowledge Proving

Modern ZK provers reduce settlement delay and enable verifiable matching policies for CLOB Appchains by proving correctness offchain and verifying succinctly onchain, limiting information leakage while preserving auditability. Validity proofs avoid optimistic challenge windows and reach L1 finality materially faster, cryptographic correctness guarantees for each batch, and improve latency.

Why Do These Provers Matter for Appchain CLOBs?

Appchain CLOBs rely on ZK provers to:

• Scale trading and maintain speed by executing order matching offchain.

• Guarantee correctness with succinct, privacy-preserving proofs onchain.

• Reduce transaction and gas costs.

• Enable interoperability via standardised, composable proof formats.

Platform Overviews

• SP1 (Succinct): RISC‑V zkVM with recursion and STARK‑to‑Groth16 wrapper; optimizes for developer ergonomics and on‑chain verifier cost at near real‑time targets, trading off specialised hardware and proof‑engineering effort (used by Bullet and Hibachi; evaluated by Polygon/Celestia/Avail).

• Boundless (@RiscZero): decentralized verifiable‑compute marketplace with GPU‑parallelized FRI‑STARKs; optimizes for elastic capacity and price discovery, trading off deterministic single‑prover latency (used by Citrea/AltLayer/BOB/Union/Lido/Caldera).

• @lagrangedev Prover Network: @eigenlayer AVS for multiproof throughput and liveness SLAs; optimizes for high concurrency and operator accountability, trading off minimal single‑proof latency (used by 80+ operators; integrated with AltLayer/Caldera).

• Starknet Provers: STARK validity with SHARP aggregation and Cairo toolchain; optimizes for batched rollup verification and AA‑centric UX, trading off language portability (used by Paradex; StarkEx powers @Immutable separately).

Performance comparison

SP1 and Boundless prioritize short proof times through recursion and hardware/cluster parallelization aimed at near real‑time rollup workloads, whereas Lagrange prioritizes liveness and multi‑proof throughput via an operator marketplace; Starknet focuses on batched validity proofs and recursive aggregation for Cairo‑based rollups

Similarities

All four offload heavy ZK computations offchain, returning succinct proofs for scalable onchain verification. They rely on decentralised prover networks and target multi-chain verification. All are critical to advancing blockchain throughput, privacy, and developer access in Appchain CLOB environments.

Summary:

• Sub‑12‑second batched proofs with zkVM flexibility and SNARK‑wrapped outputs: SP1.

• Market‑based proving with distributed GPU resources and “real‑time” targets: Boundless.

• High liveness and operator‑backed horizontal scaling for many concurrent proofs: Lagrange.

• Native Stark ZK‑native rollup integration with account abstraction and batched settlement: Starknet provers

Rollup Frameworks

Rollup frameworks provide scalable Layer 2 (L2) infrastructure for Appchains, enabling customized execution environments and settlement choices tailored to CLOBs.

The frameworks mentioned below improve: latency, ordering control, proof/finality pathway, language, tooling fit, and DA flexibility.

• Sovereign SDK: Flexible rollup framework delivering sub‑10 ms soft confirmations; production usage with Bullet demonstrates HFT‑grade UX. DA‑agnostic design with adapters for Celestia and Avail supports modular deployments across execution and data layers.

• OP Stack: Battle‑tested optimistic rollup from @Optimism powering the Superchain with EVM equivalence and live permissionless fault proofs on OP Mainnet. Prominent adopters include @base, @worldcoin, and @unichain, reflecting a broad DeFi‑aligned ecosystem fit.

• Arbitrum Orbit: Nitro‑based framework from @arbitrum for customizable L2/L3 chains with Stylus enabling Rust/C/C++ alongside EVM for performance‑sensitive apps. Supports Rollup to Ethereum or AnyTrust/L3 on Arbitrum L2s for cost and control trade‑offs.

• Starknet Stack (SN Stack): ZK‑native framework using Cairo and STARK proofs with native Account Abstraction support for advanced app UX. Paradex on a Starknet Appchain showcases institutional‑grade derivatives without relying on legacy StarkEx metrics.

Summary:

• Ultra-low soft-confirmation latency with full policy control: Sovereign SDK with modular DA.

• EVM equivalence and Superchain distribution with permissionless fault proofs: OP Stack.

• Multi-VM performance and L3 cost options: Arbitrum Orbit + Stylus.

• ZK settlement assurances and native account abstraction: Starknet Stack.

3. L1 Appchain Model: Hyperliquid

Hyperliquid is the most prominent L1 example of a sovereign, exchange-optimised appchain. It features custom consensus and execution, integrated smart contracts, and a market-creation layer that vertically integrates fees and MEV. The trade-off is performance and control versus decentralisation, open-source scrutiny, and liquidity isolation.

Four Architecture Pillars

• HyperBFT (consensus): HotStuff-style Byzantine Fault Tolerant consensus mechanism (BFT) tuned for low latency and deterministic ordering. Targets sub-second finality and total ordering to suppress typical MEV paths. A single validator set governs both trading and contracts for predictable sequencing.

• HyperCore (execution): Rust-based, order book-native engine with atomic matching, cancellations, liquidations, and isolated markets for parallel throughput. Deterministic, one-block settlement to reduce adverse selection for market makers.

• HyperEVM (smart contracts): EVM on the same validator set with dual-queue scheduling so trading paths are not blocked by heavy contract execution. Enables onchain strategies, collateralised products, and vaults that interact atomically with the CLOB.

• HIP-3 (market creation): Permissionless perps with significant (500k $HYPE) stake requirements. Shared collateral and orderbook infra; builders set parameters and share fees, creating long-tail coverage and aligned incentives.

Why Does It Matter for Trading?

Latency and determinism: Predictable price-time matching and atomic settlement reduce fill uncertainty and enable exchange-grade market-making beyond typical AMM capabilities, achieving sub-second confirmations (~0.2s median under favourable conditions). HyperCore runs an on‑chain CLOB with deterministic price‑time matching, consensus‑ordered gasless actions, and atomic cancels/fills under a single validator set, sequencing both L1 and HyperEVM, improving queue positioning and inclusion predictability for MM tactics.

Throughput under stress: Independent per‑market order books run in parallel on the same chain, avoiding single global‑book bottlenecks and targeting low tail latency under pipelined HotStuff‑style BFT, which sustains responsiveness in typical volatility regimes. HyperCore isolates hotspots at the market level and can engage protective throttles during extreme events (e.g., whale liquidations, low-liquidity squeezes). This design prioritises continuity over unbounded throughput under duress, rather than guaranteeing zero UX degradation.

Protocol‑level fee recycling: Fees stay on the appchain and route to the Assistance Fund, HLP, and builder fee shares where applicable; validator and delegator rewards are emission‑based rather than fee/MEV dividends, keeping user trading fees competitive while funding liquidity and growth.

The Assistance Fund has executed large buybacks, evidencing fee internalisation at the protocol layer rather than distributions to stakers. Historically, around 92–97%, and most recent updates point to ~99% of fees going to buybacks, with ~1% to HLP,

Most base fees route to the Assistance Fund for buybacks, a small share accrues to HLP; spot deployers may keep up to 50% of base fees on their markets, HIP‑3 perp deployers receive a fixed 50% while the protocol’s net take is conserved, and builder‑code fees are optional user‑approved add‑ons that do not alter the base split

Composability without bridging: Contracts integrate exchange‑grade CLOB actions in the same security domain, enabling vaults, structured products, and automation without cross‑chain bridges.

HyperEVM exposes read precompiles for live HyperCore state and a CoreWriter system contract with a fairness delay for writes, allowing atomic placement/cancellation and account operations alongside EVM logic in the same block timeline.

Sovereignty: Benefits and Costs

Benefits: Direct control over consensus, sequencing, fee policy, and upgrade cadence under a unified validator set allows consistent total ordering and fairness policies that are difficult to guarantee on shared L1s/L2s. HyperBFT (HotStuff‑style BFT) sequences trading and contracts together with sub‑second finality and total ordering to suppress common MEV vectors and improve deterministic inclusion.

Costs: Liquidity isolation and bridge reliance versus L2‑native composability; proprietary execution reduces external auditability; HIP‑3 market creation requires 500k $HYPE per deployer, raising capital thresholds for smaller teams.

Asset inflows typically traverse third‑party bridges or messaging stacks, while the Rust CLOB engine and bespoke execution path favour performance over openness, and HIP‑3’s stake plus slashing window gate market deployment.

Key trade‑offs

Decentralisation vs Performance: A ~21-validator active set improves latency/UX but raises censorship and jurisdiction concentration risks; validators self-delegate a minimum of 10,000 $HYPE and typically lock for one year to join the set. Consensus proceeds in rounds with validator jailing for poor responsiveness, and the validator set evolves in ~90‑minute epochs, reinforcing tight performance at the cost of a smaller active set.

Openness vs optimisation: A proprietary Rust execution engine reduces fork risk and enables tight latency/throughput optimizations but narrows external security review and community contribution compared to fully open stacks.

CLOB integration and deterministic matching pipelines are tuned for onchain HFT‑like behavior, trading transparency for performance coupling.

Permissionless design vs capital gating: HIP‑3 enables permissionless perps (think of creating fx markets, commodities, stocks,..) but requires a 500k $HYPE deployer stake and enforces governance‑controlled slashing during a 7‑day unstake window, deterring spam while concentrating market creation among well-capitalised builders. Each HIP‑3 market has independent margining and order books on HyperCore, with deployer fee shares and validator oversight on malicious inputs, aligning operation with skin‑in‑the‑game.

Token and validator economics: Fees are recycled to protocol sinks (Assistance Fund, HLP) and to builder shares rather than paid to stakers, so validator security is funded by HYPE emissions and delegation instead of fee dividends. Staking rewards accrue every minute, are distributed daily with auto‑compounding and validator commission, and follow an Ethereum‑inspired curve (illustrative ~2.3%/yr at ~400M $HYPE staked).

The trading fees are competitive and fund liquidity, buybacks, and builder alignment. The trade-off, though acute, shifts security onto emissions and healthy delegation; APR, stake distribution, validator commissions, and active set size might be tuned over time to sustain security without pressuring user fees.

The risk becomes material if emissions fall faster than delegation grows or if stake concentration rises, not because fees are misrouted.

4. L2 Appchains

There are four emerging Layer 2 solutions: Bullet, Paradex, Hibachi, and Lighter. They are converging using common architecture to solve decentralized derivatives trading: offchain execution for CEX-grade speed, zero-knowledge proofs for verifiability, and alternative data availability layers to reduce costs.

Each differentiates through different technical enablers, but all employ hybrid models.

1. Bullet

@bulletxyz_ is an application-specific "network extension" L2 for Solana that targets CEX-grade perpetuals by moving matching and risk management off the shared L1 into a Rust execution layer while anchoring data availability to Celestia and security to Solana.

This architecture addresses a critical limitation: general-purpose Solana suffers from congestion, unpredictable fees, and slow inclusion during volatility, which undermines professional market making and high-frequency trading.

Architecture

• Execution (Bullet Core): A purely Rust, VM-free runtime built with the Sovereign SDK operates through a centralised sequencer that orders and executes transactions for ~1ms soft confirmations, optimising the hot path for order entry, cancellations, liquidations, and risk management.

• Settlement (Solana L1): Executed transactions are batched and finalised on Solana, inheriting security through Solana's validator set while preserving a non-custodial model.

• Data Availability (Celestia): Transaction and state data are published to Celestia DA (up to ~27 MB/s throughput), reducing rollup operating costs versus posting bulky data on monolithic L1s.

• ZK Proving (Succinct SP1 zkVM): Bullet cross-compiles its native Rust runtime to proofs verified on Solana, using a general-purpose RISC-V zkVM that makes full-engine proving economically feasible (down to fractions of a cent per transaction).

• @PythNetwork Lazer for its oracle, which supplies millisecond-latency price feeds from major venues. The state database roadmap targets NOMT (Nearly Optimal Merkle Tree) to sustain ~40,000 TPS without state-management bottlenecks.

Key Features

• Perpetuals: High leverage up to 100x, over 100 markets, multi-collateral support, and a robust cross-margin engine that shares collateral across positions to reduce liquidation risk.

• Spot and Lending: A unified suite adds spot markets and an integrated money market with utilisation-based piecewise rate curves to balance liquidity and borrowing demand dynamically.

• Order Types and UX: Professional order types (GTC, IOC, FOK, Post-Only variants, TP/SL) plus wallet abstraction, social logins, session keys, and paymaster-based fee sponsorship to remove signing friction for active traders.

• Dive more into Bullet through our in depth research article.

Bullet recently wrapped up its testnet phase and will soon launch on mainnet. A pre-whitelist form is available to join and be among the first users: https://bulletx.typeform.com/to/zdIwlFH9

2. Paradex

@tradeparadex is a Starknet-based ZK appchain that settles to Ethereum, designed as a provably fair, privacy-preserving "superexchange" and onchain prime brokerage rather than a single-purpose perpetuals venue.

The design abandons the "last look" microstructure with cancel-priority and replaces the latency arms race with protocol-level fairness, privacy, and universal portfolio margin, aiming to reduce slippage taxes and attract institutional flows.

Architecture

• Settlement and Proofs: Batches of matched trades are proven with STARKs (via SHARP shared prover) and verified by an Ethereum L1 contract, providing deterministic validity without optimistic challenge periods—proof-gated finality.

• Execution and Sequencing: A high-performance centralized engine (AWS Tokyo with Global Accelerator) handles order book management, matching, and pre-trade risk in sub-100ms, with a roadmap to a rotating sequencer model after token generation event (soft confirmations offchain, hard finality on L1).

• Data Efficiency: Instead of full calldata, Paradex posts state diffs to L1, leveraging EIP-4844 blobs for approximately 10× lower gas costs and enabling theoretical throughput exceeding 7,000 TPS while retaining Ethereum security guarantees.

• VM and Stack: Built on the Starknet SN Stack with CairoVM optimized for proof efficiency, enabling heavy arithmetic for real-time risk, options pricing, and cross-product margin calculations that are impractical on general-purpose EVM rollups.

• Privacy by Design: Execution privacy via no-mempool matching, RPI/RFQ "onchain dark pool" semantics, and staged onchain position privacy (RPC masking now, with ZK-encrypted accounts on the roadmap) to attract large orders without telegraphing intent.

Key Features

• Zero Fees for Retail via PFOF: Market makers pay small basis points (e.g., 0.5–2 bps for perps/spot; 1–3 bps for options) to access curated retail payment for order flow, enabling "Binance-like spreads with zero fees" for retail traders while keeping venue revenue comparable to centralized exchanges after PFOF distribution.

• Multi-Stream Revenue: Trading (PFOF), asset management (vault performance share), stablecoin and bridge deposit yield-share, and money-market spreads, with a platform take on third-party app revenues as the SuperChain scales.

• Prime Brokerage Stack: Integrated vaults, borrow/lend functionality, planned unified collateral unit (XUSD), and tokenized strategy rails (VTFs) transform the exchange into a multi-product financial supercenter rather than a single CLOB endpoint.

• Dive more into Paradex through our in depth research article.

Experience the Future: Sign up on Paradex through our referral link https://app.paradex.trade/r/a1res to start trading on mainnet and earning XP in Season 2.

3. Hibachi

In the emerging category of CLOBs on BLOBs, @hibachi_xyz is among the first to use Celestia Private DA. This privacy-first approach executes orders offchain at CEX-level speed while publishing encrypted state to Celestia and proving every action with Succinct's ZK stack for onchain verifiability.

It combines millisecond-level execution with strong privacy and full proof-based auditing, eliminating the mempool data leakage and VM overhead that hinder onchain CLOBs.

Architecture

• Execution and Sequencing: Orders are matched by an offchain engine designed to rival CEX latency, producing immediate soft confirmations while deferring finality to proof verification.

• Data Availability: Uniquely among the CLOBs examined, Hibachi encrypts exchange data (balances, positions, fills, liquidations), which is posted as blobs to Celestia's DA layer, decoupling throughput from monolithic L1 constraints.

• ZK Proving: Using Succinct's stack to generate proofs that every state transition is valid, these proofs are posted onchain and treated as the source of truth, making speed and verifiability compatible. Succinct reported 5ms end-to-end execution in head-to-head benchmarks.

• No Public Mempool: Offchain matching with proof-based settlement removes onchain front-running and sandwiching vectors common to public mempools.

Key Features

• Core Perpetuals: Perpetual contracts and cross-margin are live today, with planned strategy vaults, native lending, multi-asset collateral, and spot markets as it broadens portfolio tooling.

• Exit and Censorship Resistance: Analysis describes a forced-transaction path and blob-based recovery mechanism if the operator censors, leveraging encrypted DA to reconstruct user state.

Join Hibachi using our referral link: hibachi.xyz/r/ACELN5QDJQ

4. Lighter

After eight months of private testing, the public version of @Lighter_xyz is now live. Lighter is its own Ethereum-settled zk-rollup purpose-built for perpetuals that verifies price-time matching, liquidations, funding, and risk checks with custom ZK circuits while keeping user assets in Ethereum smart contracts.

This architecture provides L1-anchored custody and proof-enforced fairness, positioning Lighter as an alternative to appchain L1s and contract-only perpetuals.

Architecture

• Sequencing and Execution: A centralized sequencer batches orders for low latency and high throughput. There is no public mempool, reducing front-running vectors during order submission.

• Proofs and Settlement: Custom ZK circuits generate attestations that confirm price-time priority, liquidation rules, and funding calculations. An Ethereum verifier contract advances the state root only after a proof verifies on L1.

• Exit and Censorship Resistance: If the sequencer stalls or censors, users can submit priority operations onchain or exit via an emergency "Desert Mode" that relies on Ethereum-available data to resolve positions and withdrawals.

Features

• Retail-Friendly Pricing: Zero front-end fees for retail users drive adoption, while charging API/HFT participants.

• Perpetuals and Order Types: Lighter runs CLOB perpetuals and supports advanced order types such as IOC/FOK.

Try Lighter using our referral link: https://app.lighter.xyz/trade/ETH?referral=QBMKQSDBMLC2

5. Design Space Exploration

L2: Sequencer Designs and MEV Approaches The sequencer determines transaction ordering and execution priority, fundamentally shaping whether a venue is fair or exploitable. For derivatives, this matters critically because professional market makers only provide tight spreads when they can reliably cancel stale quotes during price moves, if cancellations compete unpredictably with takers in fee auctions (as on congested general-purpose L1s), makers widen spreads or exit entirely. MEV (Maximal Extractable Value) represents the profit extracted through transaction reordering, taking the form of sniping stale quotes when oracles update or front-running liquidations.

The platforms examined suppress MEV through different mechanisms, speed (executing before observers react), encryption (hiding information), flow segmentation (isolating toxic participants), or cryptographic proofs (making manipulation verifiable), each trading off decentralisation, privacy, or performance. Bullet targets traders and HFT firms through pure speed optimisation. It's a ~1ms centralised sequencer with cancel-priority that directly addresses L1 congestion, where cancellations are outpaced by taker bots in fee auctions during volatility. Application-specific ordering, favouring makers over takers, represents a microstructure bet: tight spreads require predictable cancellation.

By isolating from Solana-wide surges, market quality holds when reliability matters most. MEV is suppressed because orders execute before observers can react, 400x faster than Solana's block cadence and planned zkVM proofs add accountability without compromising performance. This serves users who evaluate venues on microsecond-level responsiveness and trust that cryptographic accountability can substitute for immediate decentralisation.

Paradex targets retail users and institutions through fairness via flow segmentation rather than speed. Retail for Price Improvement (RPI), a maker-only UI layer hidden from APIs, lets market makers post tighter quotes for non-toxic retail flow while charging 0.5–2 bps for access.

The PFOF model inverts economics: fairness comes from segregating order types, not faster execution for all. The sequencer handles matching in sub-100ms, deliberately "slow enough" to prevent HFT arms races while "fast enough" for institutional needs. STARK proofs prevent post-execution manipulation, but the real fairness mechanism is economic: if makers detect quality degradation, they stop paying, destroying primary revenue.

Hibachi targets privacy-conscious traders, large position holders and institutions protecting proprietary strategies, through architectural opacity. All exchange states (balances, positions, fills, liquidations) encrypts before posting to Celestia DA. The 5ms execution matters less than preventing information leakage: no observer can reconstruct trading activity in real-time. The ZK proofs verify state transitions follow rules without revealing content.

However, users cannot independently verify fair treatment until proofs confirm that whatever occurred was mathematically valid. There's no transparent order book to audit, no mechanism to catch unfair execution before settlement. MEV is eliminated through information hiding: attackers cannot exploit what they cannot see. This serves users who view execution transparency itself as a vulnerability.

Lighter targets Ethereum-native users requiring auditable proof of fair treatment. Custom ZK circuits purpose-built for derivatives operations (price-time priority, liquidations, funding) verify matching followed protocol rules exactly before Ethereum verifier contracts advance state. Unlike general zkVMs, specialised circuits optimise proving efficiency for the narrow use case. The centralised sequencer provides performance, but any reordering or manipulation can fail L1 verification. Desert Mode ensures sequencer failure cannot trap funds, users submit priority operations directly to Ethereum for forced withdrawals without operator cooperation. Trust reduces to "Ethereum remains available and proof contracts work correctly," both with extensive track records. After eight months of private testing, this prioritises verifiable fairness and censorship resistance over peak performance for users who value Ethereum security and operational simplicity.

6. Insights

The platforms solve fundamentally different problems based on their target users:

• Bullet optimises for traders needing microsecond responsiveness, accepting centralisation for performance

• Paradex optimises for retail users needing zero fees and institutions needing prime brokerage, using flow segmentation for fairness

• Hibachi optimises for privacy-conscious traders, sacrificing transparency for confidential execution.

• Lighter optimises for Ethereum or EVM users requiring auditable proofs, prioritising security over performance.

L1: Validators + Consensus

Hyperliquids consensus model reshapes MEV rather than eliminating it: enforced total ordering narrows transaction reordering windows, and HyperCore’s deterministic single-block settlement reduces multi-slot exploitation surfaces. However, pre-inclusion visibility still allows preferential inclusion absent protocol-level constraints, such as fair ordering or encrypted mempools.

The active set is capped at 21 permissionless validators, and commits finalise when roughly two‑thirds of the stake signs the same branch, implying a practical 14‑of‑21 threshold today; while this concentrates power relative to very large validator sets, collusion risk is tempered by delegated‑stake dynamics and validator reputation in a public leaderboard regime.

Validator participation requires 10,000 HYPE self‑delegation per node, which raises a meaningful but not prohibitive entry bar; the previously cited 500,000 HYPE figure applies to HIP‑3 “builder‑deployed perps” requirements, not validator eligibility.

HyperBFT (HotStuff‑derived) and HyperCore (matching/margin state) are proprietary, enabling rapid iteration and tightly integrated single‑block settlement, but reducing external auditability and independent verification relative to fully open‑source consensus and execution stacks.

Cross-Chain Architecture

Cross-chain architecture determines whether traders can deposit use the same collateral across positions, whether market makers must maintain separate capital pools per venue, and whether DeFi protocols can integrate derivatives positions into broader strategies.

While all four platforms solve the performance problem, they fragment liquidity across incompatible settlement layers. The architecture determines exposure to bridge risk, breadth of supported assets, and whether composability with external protocols is possible or deliberately sacrificed for other priorities like privacy.

Bullet targets traders needing broad asset access while maintaining performance. Intent-based bridging via RelayProtocol and Hyperlane can make multi-chain complexity smoother. The planned BulletSVM developer layer enables composable applications through precompiles into the matching engine. However, every cross-chain interaction coordinates multiple systems (origin chain, bridge, Solana settlement, Celestia DA), introducing latency precisely when microsecond responsiveness matters. The modular approach provides cost advantages (Celestia DA reduces data posting costs versus L1), but all non-SOL assets face bridge risk. This serves traders who need asset flexibility and accept cross-chain latency for broader market access.

Hibachi targets privacy-focused users and explicitly sacrifices cross-chain composability for confidentiality. The encrypted blob architecture on Celestia DA prevents external smart contracts from reading positions or balances without decryption keys. For example, DeFi lending protocols on Ethereum or other chains could not integrate or liquidate Hibachi collateral. Portfolio trackers cannot aggregate exposure across venues, and automated strategies cannot query positions for rebalancing. The architectural choice for encryption hiding balances, positions, fills, and liquidations fundamentally prevents transparent state reads, which are required for composability. The roadmap emphasises expanding within the private venue (vaults, lending, multi-asset collateral, spot) rather than external integrations, forcing a walled-garden approach where internal composability works but cross-protocol integration fails. This serves users who view position transparency as a vulnerability and accept isolation for confidential execution.

Lighter and Paradex both target Ethereum/EVM users; both are Ethereum‑settled L2s, but they approach interoperability differently: Lighter keeps custody and settlement natively on Ethereum via a zk‑rollup, using cross‑chain rails only for deposit onboarding, whereas Paradex runs as a Starknet appchain that verifies to Ethereum and integrates bridge partners (e.g., @Orbiter_Finance) to pull USDC from many networks into its L2 while anchoring final security to Ethereum. Once funds arrive, both inherit Ethereum’s settlement guarantees through validity proofs. However, Lighter’s custody boundary remains strictly within Ethereum for ETH-domain assets, while Paradex standardises heterogeneous deposits into USDC on its app chain.

Hyperliquid uses @LayerZero_Core, @axelar and @hyperunit to manage controlled access in and out, preserving sovereign value capture with clear points of connectivity. LayerZero's independent oracle and relayer model provides flexible paths with application-level trust assumptions. Axelar's delegated PoS validator network utilises threshold signatures, which enhances decentralisation compared to single-operator bridges while retaining validator security and operational latency.

Future of Trading Appchains

Appchain trading venues have entered a phase of modular infrastructure maturity, but their long-term success hinges on adapting to market dynamics, evolving standards, and interoperability pressures.

Proliferation Predictions

Modular blockchain stacks, including DA layers, ZK provers, and rollup frameworks, now enable rapid appchain launches, shifting the binding constraint from technical feasibility to liquidity acquisition. In a bull market, dozens of similar trading appchains are likely to emerge, each targeting niche performance, privacy, or fee models. However, bear markets will force consolidation: platforms that fail to reach critical volume to cover fixed costs such as validator sets, DA fees, proof generation, and bridge operations will either exit or merge. Sovereign L1 appchains face binary outcomes: either sufficient scale sustains validator economics or validator exit spirals ensue. In contrast, L2 appchains preserve optionality through inherited security, delaying but not eliminating consolidation pressure.

Standardization Possibilities

As DA and proving providers converge on competitive pricing and interchangeable interfaces (for example, Celestia, EigenDA, and Avail for DA; SP1, Boundless, and Lagrange for proofs), these layers risk commoditization.

This creates an opportunity for standardisation efforts around:

• Middleware protocols, including intent solvers, shared sequencers, and MEV marketplaces, to coordinate order flow and MEV extraction across venues.

• Cross-chain messaging standards for secure, low-latency asset transfers.

• Proof and data-availability APIs that allow plug-and-play integration between appchains and middleware without adapters.

If the industry drives open specifications, appchain developers can focus on execution differentiation rather than infrastructure selection, accelerating time-to-market and reducing integration overhead.

Interoperability Challenges

Despite standardisation, fragmenting factors will persist:

Custom execution environments, such as HyperBFT ordering, encrypted blobs, and Rust-native runtimes, break a common execution substrate, hindering composability.

Privacy-first designs, like encrypted DA in Hibachi, deliberately isolate state to prevent external read-access, sacrificing cross-protocol integrations such as onchain lending or portfolio aggregation.

Bridging the risk and latency inherent to multi-chain architectures introduces uncertainty, especially for HFT-style applications where microsecond responsiveness is crucial.

Asynchronous finality, where soft confirmations differ from cryptographic finality, creates temporal arbitrage windows that some venues exploit, complicating unified cross-venue settlement guarantees.

Overcoming these challenges requires shared sequencing layers or intent-based solvers that respect venue-specific ordering policies while offering atomic cross-chain execution. This represents a significant technical and economic coordination problem.

Winner-Take-All vs Fragmentation

Three situations could shape the market:

1. Fragmented Specialisation: Distinct platforms coexist, each serving unique segments, whether sovereignty-focused L1s, privacy-optimised venues, or ecosystem-native L2s. Execution differences and vertical integration, for example, through proprietary routing or exclusive solver partnerships, sustain discrete value pools.

2. Middleware Consolidation: Intent solvers, shared sequencers, and cross-chain aggregators capture optimisation value, turning execution venues into price-taking utilities. This mirrors TradFi's central limit order flow, where smart order routers and HFT firms extract alpha, relegating exchanges to infrastructure providers.

3. Sovereignty-Led Concentration: Full-stack integration emerges as necessary for capturing competitive MEV and achieving optimal performance. Market share concentrates among a few vertically integrated L1 appchains that internalise DA, proving, sequencing, and routing, while modular L2 designs struggle to defend execution economics against middleware power.

Adaptive positioning, meaning platforms that maintain the option to shift along the sovereignty-compliance-ecosystem spectrum, will be critical. The winners will treat architecture as an evolving strategy rather than a permanent commitment, preserving the ability to integrate or isolate as market, regulatory, and middleware landscapes evolve.

Conclusion

Appchains now represent the default architecture for high-performance trading, but the durable winners will be those that operate as both application and protocol while navigating standardisation, interop risk, and middleware power shifts. Sovereign L1s like Hyperliquid show what full control can deliver, but modular L2s offer faster paths to market and ecosystem connectivity if they can defend execution economics against an increasingly capable intent and solver stack.

The decisive factor will be whether execution advantages and distribution moats remain strong as interfaces standardise and routing centralises, which will determine if this market resolves into specialisation, middleware consolidation, or sovereignty-led concentration.

Hyperliquid, Bullet, Hibachi, Lighter, and Paradex occupy distinct positions along sovereignty-compliance-ecosystem-privacy axes for L1 and L2 Appchains. Their sustainability depends on preserving flexibility to reconfigure value capture as infrastructure pricing, regulatory frameworks, and middleware capabilities evolve, treating architecture as an adaptive strategy rather than a fixed commitment.

The modular infrastructure enabling many of these platforms: sub-second finality with cryptographic guarantees, encrypted execution, cross-chain settlement, high-throughput DA, and efficient zkVM proving will persist and recombine into future applications whether current platforms endure or not. These technical primitives extend beyond CLOBs to general-purpose Appchain infrastructure.

The next cycle of months will test whether dual-layer value capture can survive infrastructure commoditization and middleware proliferation, or whether trading venues must choose between sovereignty and subordination in an emerging Fat Middleware stack. The outcome shapes not just CLOB design but the viability of the entire Appchain paradigm.

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