Best web3 data indexing protocol
4 models · updated 2026-07-15
The verdict
Goldsky leads — 1 of 4 models rank Goldsky the top pick.
Not unanimous: ChatGPT picks The Graph; Claude picks The Graph; Grok picks Ormi.
As of 2026-07-15, ChatGPT, Claude, Gemini and Grok collectively rank Goldsky #1 for web3 data indexing protocol on ModelsAgree by aggregate score. The models' case: Offers the best-managed developer experience with 1-click subgraph deployment and Goldsky Mirror, which streams real-time blockchain data directly to ClickHouse or. The models' main caveat: It is a closed-source, fully centralized managed service, leading to vendor lock-in and high usage costs for high-throughput operations. The strongest alternative is The Graph — Best overall for typical dapp teams: mature open-source Graph Node tooling, a large reusable subgraph catalog, GraphQL APIs, decentralized hosting. Not unanimous: ChatGPT picks The Graph; Claude picks The Graph; Grok picks Ormi. Source: https://modelsagree.com/best/best-web3-data-indexing-protocol (modelsagree.com, CC BY 4.0).
Combined ranking
- 1GPT #2Claude #3Gemini #1Grok #3
Offers the best-managed developer experience with 1-click subgraph deployment and Goldsky Mirror, which streams real-time blockchain data directly to ClickHouse or PostgreSQL databases, bypassing the GraphQL query bottleneck entirely.
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Gemini Offers the best-managed developer experience with 1-click subgraph deployment and Goldsky Mirror, which streams real-time blockchain data directly to ClickHouse or PostgreSQL databases, bypassing the GraphQL query bottleneck entirely.
GPT Near-tied with The Graph for teams prioritizing managed infrastructure; excellent subgraph compatibility, fast deployment and backfills, strong reliability, and Mirror pipelines that deliver decoded or raw multichain data into databases and queues.
Claude The strongest managed option — instant hosted subgraphs (drop-in Graph compatibility) plus Mirror pipelines that stream indexed data straight into your own Postgres/ClickHouse, giving zero-ops indexing with full SQL ownership of the data; near-tie with Ponder, the split is managed-vs-self-hosted preference
Grok Drop-in subgraph compatibility with real-time streaming/webhooks/Mirror/SQL, 140+ chains, instant deploys and strong performance; excels as enhanced managed service for event pipelines and analytics without rebuilding.
Where it falls shortper GPT It is a commercial managed platform with less portability and decentralization, and serious production workloads can become expensive.
per Claude Commercial and usage-priced — costs climb with data volume, and pipeline definitions create meaningful vendor lock-in versus open-source stacks
per Gemini It is a closed-source, fully centralized managed service, leading to vendor lock-in and high usage costs for high-throughput operations.
per Grok Managed/SaaS model means less decentralization and potential vendor dependency; not for teams wanting full self-hosting or extreme customization.
- 2GPT #1Claude #1Gemini #5Grok #2
Best overall for typical dapp teams: mature open-source Graph Node tooling, a large reusable subgraph catalog, GraphQL APIs, decentralized hosting, and broad chain support; Substreams adds high-throughput historical and real-time processing when ordinary mappings are insufficient.
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GPT Best overall for typical dapp teams: mature open-source Graph Node tooling, a large reusable subgraph catalog, GraphQL APIs, decentralized hosting, and broad chain support; Substreams adds high-throughput historical and real-time processing when ordinary mappings are insufficient.
Claude Still the category-defining standard — the largest ecosystem of reusable subgraphs, the broadest chain coverage, and Substreams for parallelized high-throughput indexing; for a typical dapp team the fastest path is often consuming a subgraph that already exists, and the decentralized network gives censorship-resistance and no single-vendor dependency that no rival matches; ranked first on breadth and ecosystem gravity, assuming the practitioner values coverage over raw DX
Grok Massive established subgraph ecosystem, decentralized network (GRT), community-driven data for major protocols (Uniswap/ENS etc.), 40+ chains with proven scale (billions of queries); gold standard for subgraph compatibility and dApp integration.
Gemini The pioneer and industry standard for decentralized indexing, offering unmatched censorship resistance, a massive library of community subgraphs, and a robust peer-to-peer network of independent indexers.
Where it falls shortper GPT Complex or high-volume subgraphs can be costly and operationally awkward, with indexing constraints, network economics, and AssemblyScript mappings adding friction.
per Claude Developer experience and economics on the decentralized network (GRT payments, curation, query keys, indexing latency) remain markedly clunkier than modern hosted or local-first alternatives, and AssemblyScript mappings feel dated next to TypeScript-native rivals
per Gemini The decentralized query routing and billing introduce cost unpredictability, higher latency, and complex coordination overhead compared to managed indexers.
per Grok Hosted service deprecated; indexing not the fastest for real-time/high-throughput needs, higher query costs on network.
- 3GPT #3Claude #4Gemini #2Grok #5
Near-tie with Ponder for modern developer experience; Envio excels at multi-chain throughput, utilizing a Rust/TypeScript framework (HyperIndex) and HyperSync to bypass standard RPC bottlenecks for up to 100x faster synchronization than WASM subgraphs.
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Gemini Near-tie with Ponder for modern developer experience; Envio excels at multi-chain throughput, utilizing a Rust/TypeScript framework (HyperIndex) and HyperSync to bypass standard RPC bottlenecks for up to 100x faster synchronization than WASM subgraphs.
GPT Strongest developer-focused alternative for EVM event indexing: open-source, TypeScript-native, fast historical sync through HyperSync, automatic contract import, multichain aggregation, reorg handling, and straightforward self-hosted or managed deployment.
Claude HyperIndex plus HyperSync delivers the fastest EVM backfills in the category (orders of magnitude quicker than RPC-based sync), with TypeScript handlers and first-class multichain indexing into one schema — the pick when historical sync speed across many chains is the bottleneck; near-tie with Subsquid on speed-focused merits
Grok Blazing-fast open-source HyperIndex (benchmarks: 8s vs hours for others on Uniswap workloads), HyperSync for 100x+ speed, customizable TS/JS logic with GraphQL; top performance for EVM real-time/historical indexing without heavy infra.
Where it falls shortper GPT Its ecosystem, public reusable indexes, and non-EVM coverage remain materially narrower than the leaders.
per Claude Younger and narrower ecosystem — EVM-focused, smaller community and fewer battle-tested production references than The Graph or Ponder
per Gemini The ecosystem is relatively young, resulting in fewer community-built templates compared to mature protocols like The Graph.
per Grok More self-hosted/framework-oriented (manage your own scaling/reliability); narrower focus than broadest hosted options, less mature ecosystem than The Graph.
- 4GPT #4Claude #5Gemini #3Grok —
Uses a highly efficient modular architecture with a decentralized data lake (separating historical data retrieval from custom schema mapping) to deliver ultra-fast batch historical syncing across over 200 EVM and non-EVM chains.
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Gemini Uses a highly efficient modular architecture with a decentralized data lake (separating historical data retrieval from custom schema mapping) to deliver ultra-fast batch historical syncing across over 200 EVM and non-EVM chains.
GPT Highly flexible open-source framework for custom high-volume indexers, with selective historical retrieval, fast backfills, support beyond EVM, arbitrary TypeScript processing, PostgreSQL/GraphQL and analytical sinks, plus decentralized data access through SQD Network.
Claude The SQD decentralized data lake makes historical extraction extremely fast and cheap without hammering RPCs, and the Squid SDK covers EVM, Substrate, and Solana — the widest ecosystem reach among the modern indexers, with a credible decentralization story for the data layer
Where it falls shortper GPT It demands more engineering and database ownership than subgraph-first platforms, and parts of its newer real-time Portal stack remain less battle-tested.
per Claude More assembly required — you compose extraction, transformation, and serving yourself, and outside Substrate its mindshare trails the EVM-native tools, so examples and community answers are thinner
per Gemini The ETL-based SDK has a steeper learning curve than standard subgraphs and requires developers to manage their own query database targets.
- 5GPT —Claude #2Gemini #4Grok —
The developer-favorite open-source EVM indexing framework — end-to-end TypeScript with full type safety, hot-reload local dev, direct Postgres/SQL output, and drastically faster iteration than writing subgraphs; for teams building their own app backend it delivers the best build-and-debug loop in the category
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Claude The developer-favorite open-source EVM indexing framework — end-to-end TypeScript with full type safety, hot-reload local dev, direct Postgres/SQL output, and drastically faster iteration than writing subgraphs; for teams building their own app backend it delivers the best build-and-debug loop in the category
Gemini Near-tie with Envio for developer ergonomics; Ponder is the best local-first EVM indexer, featuring a lightweight TypeScript-native engine with hot-reloading and direct PostgreSQL integration that eliminates the operational complexity of Graph Node.
Where it falls shortper Claude Self-hosted only and EVM-centric — you own the infra, reorg handling at scale, and uptime, with no decentralized network or managed tier to fall back on
per Gemini It only supports EVM chains and has no decentralized network hosting, requiring developers to self-host or rely on Web2 hosting.
- 6GPT —Claude —Gemini —Grok #1
Sub-30ms latency, 4k+ req/sec, real-time indexing on 70+ chains with GraphQL/REST; tops hosted subgraph comparisons for production reliability and speed in DeFi/trading/gaming workloads (assumption: typical practitioner prioritizes low-latency query performance over pure decentralization).
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Grok Sub-30ms latency, 4k+ req/sec, real-time indexing on 70+ chains with GraphQL/REST; tops hosted subgraph comparisons for production reliability and speed in DeFi/trading/gaming workloads (assumption: typical practitioner prioritizes low-latency query performance over pure decentralization).
Where it falls shortper Grok Not the broadest chain support or fully decentralized; less ideal for extreme multi-chain uniformity or self-hosted custom logic.
- 7GPT —Claude —Gemini —Grok #4
Unified API across 200+ chains with consistent structured/historical data (balances, txs, NFTs, DEX); simplifies multi-chain querying for typical devs/analysts, high throughput and verified data powering apps/AI without per-chain indexing.
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Grok Unified API across 200+ chains with consistent structured/historical data (balances, txs, NFTs, DEX); simplifies multi-chain querying for typical devs/analysts, high throughput and verified data powering apps/AI without per-chain indexing.
Where it falls shortper Grok Less flexible for custom subgraph-style event mappings or ultra-low latency real-time DeFi; can be slower than native indexers for some edge cases.
- 8GPT #5Claude —Gemini —Grok —
Capable open-source multichain indexing stack with managed and decentralized hosting, GraphQL delivery, strong Substrate roots, and useful support across EVM and several non-EVM ecosystems; near-tied with SQD when chain breadth matters more than maximum customization.
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GPT Capable open-source multichain indexing stack with managed and decentralized hosting, GraphQL delivery, strong Substrate roots, and useful support across EVM and several non-EVM ecosystems; near-tied with SQD when chain breadth matters more than maximum customization.
Where it falls shortper GPT Its practitioner ecosystem, reusable-project supply, and operational mindshare are thinner than The Graph’s, particularly for mainstream EVM applications.
Rank history
Just missed the top 5
GPT Ponder — excellent type-safe TypeScript EVM indexer, but narrower in chain scope and hosted-network capabilities · DipDup — strong Python-first framework with flexible databases and APIs, but more infrastructure-heavy and less broadly adopted for mainstream subgraph workloads
Claude SubQuery — capable multi-chain Graph alternative with strong Polkadot/Cosmos roots, but in the core EVM market it trails every listed option on ecosystem and tooling momentum · Alchemy Subgraphs — convenient hosted subgraphs if you're already on Alchemy, but a thin compatibility layer rather than a distinct engine — Goldsky does managed better and Ponder/Envio do custom better
Gemini SubQuery — missed the top 5 because it is less optimized for EVM speed compared to Envio and Ponder, and has higher latency than Subsquid for multi-chain historical batch indexing · Ormi — missed the top 5 due to a less-proven track record and lacking the advanced database streaming pipelines provided by Goldsky
Grok SubQuery — strong non-EVM/multi-chain flexibility and speed but edged by Ormi/Goldsky on EVM latency/ecosystem and The Graph on adoption
By model
ChatGPT
- 1.The Graph
- 2.Goldsky
- 3.Envio
- 4.SQD
- 5.SubQuery
Claude
- 1.The Graph
- 2.Ponder
- 3.Goldsky
- 4.Envio
- 5.SQD
Gemini
- 1.Goldsky
- 2.Envio
- 3.SQD
- 4.Ponder
- 5.The Graph
Grok
- 1.Ormi
- 2.The Graph
- 3.Goldsky
- 4.Covalent
- 5.Envio
Common questions
What is the best web3 data indexing protocol according to AI models?
Goldsky leads. 1 of 4 models rank Goldsky the top pick. The current top 3: Goldsky, The Graph, Envio. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-07-15. Source: modelsagree.com.
Which web3 data indexing protocol did each AI model pick first?
ChatGPT: The Graph. Claude: The Graph. Gemini: Goldsky. Grok: Ormi.
Do the AI models agree on the best web3 data indexing protocol?
Not unanimous. ChatGPT picks The Graph; Claude picks The Graph; Grok picks Ormi.
What changed in the latest web3 data indexing protocol ranking?
In the latest poll (2026-07-15): SubQuery dropped 2 spots; Ormi and Covalent entered the ranking. The models are re-polled on demand, so this ranking moves.
How is this web3 data indexing protocol ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
Cite this ranking
ModelsAgree, “Best web3 data indexing protocol” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-07-15. https://modelsagree.com/best/best-web3-data-indexing-protocol (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand