Why Data Redundancy in Web3 Requires a New Architectural Paradigm

Storing data on hundreds of nodes for Byzantine fault tolerance creates massive overhead. This leads to ~10-100x higher storage costs versus centralized S3 for hot data, making it prohibitive for high-throughput dApps and rollups.

• Economic Inefficiency: Users pay for massive over-provisioning.
• Limited Use Cases: Priced out of video, gaming, and high-frequency data.

Consensus for data retrieval introduces multi-second latencies, breaking real-time applications. This is the antithesis of the sub-200ms expectations set by Web2 CDNs, crippling DeFi, social, and gaming use cases.

• Slow Finality: Data availability proofs and erasure coding add delay.
• No Hot Cache Layer: Architectures like IPFS/Filecoin lack a fast, global cache by default.

To reduce cost, protocols like Arweave use a small subset of nodes (Succinct Labs' design) for storage, creating a liveness risk. If those few nodes go offline, data becomes temporarily unavailable, violating the core promise of permanent, resilient storage.

• Centralization Pressure: Economic incentives favor large, professional storage providers.
• Weak Guarantees: Data is only as available as its least reliable keeper.

Separate the intent to store/retrieve from the execution. Let users express SLAs for cost, speed, and redundancy. A solver network (inspired by UniswapX, Across Protocol) competes to fulfill this intent by dynamically routing to optimal storage layers (hot CDN, cold archival).

• Market Efficiency: Solvers optimize for the best provider mix.
• User Sovereignty: Control is shifted from protocol defaults to user-defined parameters.

Instead of storing raw data everywhere, store cryptographic commitments (e.g., KZG polynomials, Merkle roots) on-chain or in a light client network. Use ZK or TEE-based provers (like Risc Zero, Espresso Systems) to attest that a centralized or lightly-redundant provider holds the correct data and can serve it.

• Massive Cost Reduction: Pay for proofs, not petabytes of duplication.
• Strong Guarantees: Cryptographic security replaces infrastructural overkill.

Architect storage in layers based on access frequency. Hot data (last 24h) lives in a high-performance, paid CDN-like layer (e.g., Arweave's Bundlr, Storj). Warm data is erasure-coded across a mid-tier network. Cold, permanent data is pushed to the most decentralized, cost-efficient base layer (e.g., Filecoin, Arweave).

• Optimized Spend: Pay for performance only when needed.
• Seamless UX: Automated tier migration is abstracted from the user.

Centralized RPC providers like Infura and Alchemy become de facto network operators, creating censorship vectors and downtime risks for $100B+ in DeFi TVL.\n- Risk: A single provider outage can brick major dApps and wallets.\n- Solution: Decentralized RPC networks (e.g., POKT Network, Lava Network) incentivize a global node fleet, eliminating centralized chokepoints.

Price feeds from Chainlink or Pyth are trust-minimized but not trustless; latency and source aggregation create windows for extractable value.\n- Risk: Flash loan attacks exploit price lag, draining millions from AMMs.\n- Solution: Redundant, competing oracle networks with cryptographic attestations (e.g., API3's dAPIs, Witnet) and on-chain verification reduce reliance on any single data layer.

Bridges and cross-chain messaging protocols (LayerZero, Axelar, Wormhole) depend on a handful of attestors or guardians to validate state, creating a new consensus layer risk.\n- Risk: A colluding super-majority can mint unlimited bridged assets.\n- Solution: Light client bridges (e.g., IBC, Succinct) and zero-knowledge proofs enable cryptographic verification of state transitions without trusted committees.

Rollups (Arbitrum, Optimism, zkSync) post data to a single Data Availability (DA) layer (often Ethereum), creating cost and scalability ceilings.\n- Risk: Ethereum congestion makes L2s prohibitively expensive and slow.\n- Solution: Modular DA layers (Celestia, EigenDA, Avail) and danksharding provide cheaper, redundant data posting, decoupling execution from consensus security.

The dominant query protocol The Graph relies on a curated set of indexers, leading to potential data integrity and liveness failures.\n- Risk: Indexer collusion or failure returns Web3 to centralized API reliance.\n- Solution: Redundant, permissionless indexing networks with slashing for incorrect proofs and client-side verification shift trust from operators to code.

Off-chain compute for AI, gaming, and DeFi (e.g., EigenLayer AVS, Espresso Sequencers) introduces a new trust assumption: that the computation is correct.\n- Risk: A malicious or buggy operator corrupts the entire service layer.\n- Solution: Fraud proofs and ZK-proofs (e.g., Risc Zero, Jolt) allow any user to cryptographically verify execution integrity, making redundancy verifiable.

Relying on a single RPC provider like Infura or Alchemy creates a single point of failure for your entire application. When they go down, your app goes down. This is the antithesis of decentralization.

• 99.9%+ of dApps are vulnerable to this systemic risk.
• ~$10B+ TVL is routinely at risk during major outages.
• Forces developers into vendor lock-in with opaque pricing.

Every new chain or L2 requires building a custom, complex indexing stack (e.g., The Graph subgraphs). This is a massive time and capital sink, fragmenting data and killing composability.

• 6-12 month lead time to launch a production-ready indexer.
• Data is siloed by protocol and chain, breaking cross-chain logic.
• Creates maintenance nightmares with every protocol upgrade.

The new paradigm is specialized, decentralized data networks that provide redundancy, performance, and unified access. Think POKT Network for RPCs or Goldsky for indexing.

• 1000+ independent nodes provide >99.99% uptime via redundancy.
• Single GraphQL endpoint queries data across any chain or protocol.
• Pay-for-usage models eliminate vendor lock-in and reduce costs by ~30-50%.

Stop querying for raw state. Declare your data intent (e.g., "best price for 1000 ETH") and let a decentralized solver network (like UniswapX or CowSwap for trades) fetch, verify, and deliver the result. This abstracts away the underlying data chaos.

• Dramatically simplifies application logic and state management.
• Inherently cross-chain by design, enabled by bridges like Across and LayerZero.
• Shifts risk from the application to the specialized solver network.