Compare
Blockchain data products differ less in coverage than in what the buyer ends up holding: a query seat, a metered endpoint, a warehouse share, a pipeline to operate, or the files themselves. These pages compare those product models, not vendor feature lists.
Short answer
Historical blockchain data is sold in five shapes: hosted SQL platforms, metered APIs, warehouse feeds, hosted indexers, and bulk file delivery. The right choice follows the access pattern. Interactive questions suit SQL platforms, application traffic suits APIs, and repeated reads over deep history suit Parquet files you own.
Categories reviewed September 2026. Vendor prices and limits change without notice, so none are quoted here.
Head to head
Each page covers what the other product is, how the two differ across delivery, cost, and schema guarantees, when each one is the better choice, and how teams use both together.
SQL analytics platform
Dune is a hosted SQL platform: decoded blockchain tables stay on Dune infrastructure and you query them there, billed by seats and credits. datastore.sh sells the data itself as partitioned Parquet files, priced per dataset and coverage window. Choose Dune for exploration and dashboards. Choose file delivery for repeated reads over deep history.
Read the comparison →SQL analytics platform
Flipside Crypto provides curated, analyst-facing blockchain models queried in SQL on Flipside infrastructure, with a community of published analysis around them. datastore.sh delivers Solana and Hyperliquid history as versioned Parquet files you keep and query with your own engine. The split is hosted queries against modeled tables versus owned files with pinned schemas.
Read the comparison →Enterprise data platform
Allium delivers enterprise blockchain data into customer warehouses and pipelines under commercial contracts, covering many chains. datastore.sh sells Solana and Hyperliquid datasets as plain Parquet, priced per dataset and coverage window, with no enterprise motion required. Allium suits broad managed coverage. datastore.sh suits narrow, deep, self-serve archives.
Read the comparison →Blockchain data API
Bitquery serves blockchain data through GraphQL, REST, and streaming endpoints, billed per request or credit, which suits application traffic and point lookups. datastore.sh delivers historical Solana and Hyperliquid data as bulk Parquet files priced per coverage window. APIs are the wrong unit for history; files are the wrong unit for live lookups.
Read the comparison →Multi-chain data infrastructure
Chainbase provides multi-chain data infrastructure, exposing indexed blockchain data through APIs and hosted datasets across many networks. datastore.sh publishes two networks, Solana and Hyperliquid, as partitioned Parquet files delivered to storage you control. The tradeoff is breadth with hosted access against depth with owned files and pinned schemas.
Read the comparison →Warehouse data delivery
SonarX makes blockchain data available inside cloud data warehouses and marketplaces, so analysts query it in SQL next to their existing tables. datastore.sh delivers Solana and Hyperliquid history as Parquet files you own outright. A share ends when the subscription ends; delivered files do not.
Read the comparison →Hosted indexing
Hosted indexers such as Goldsky, subgraphs, and Substreams run indexing logic that you write, sinking the output where you point it. datastore.sh sells the finished historical output as Parquet. Hosted indexing removes the servers, not the engineering: decoders, backfills, and migrations remain yours to own.
Read the comparison →Your own pipeline
Building in-house means operating archive nodes, writing decoders per program, reconciling against chain state, and re-running backfills whenever logic changes. datastore.sh sells the output of that work as documented Parquet. Build when coverage does not exist commercially, when data cannot leave your perimeter, or when the pipeline is your edge.
Read the comparison →Start from the workload
The access pattern decides the product, not the vendor. Find the row that matches the job in front of you.
| Workload | Use | Why |
|---|---|---|
| Backtest a strategy over years of history | Bulk Parquet files | The same range is scanned on every run, so metered reads compound. |
| Train or fine-tune a model on on-chain behavior | Bulk Parquet files | A corpus must stay byte-identical between runs and sit beside the trainer. |
| Load on-chain history into a warehouse you already run | Bulk Parquet files or a warehouse feed | Parquet loads natively; a share keeps tables current while it is active. |
| Explore an open question in SQL this afternoon | A hosted SQL platform | Nothing to download, and public queries give you a starting point. |
| Serve wallet views or live prices in an application | A blockchain API or RPC provider | Point lookups and streams are what per-request billing is shaped for. |
| Keep an app-specific view of one protocol current | A hosted indexer | Custom pipeline logic is the point, and latency is a requirement. |
| Cover a protocol nobody sells data for | Your own pipeline | Building is correct when the coverage does not exist commercially. |
Side by side
| Dimension | datastore.shDataset delivery | SQL analytics platformsDune, Flipside Crypto | Blockchain data APIsBitquery, Chainbase | Warehouse data feedsAllium, SonarX | Hosted indexersGoldsky, subgraphs, Substreams | Build in-houseYour archive nodes and decoders |
|---|---|---|---|---|---|---|
| What you receive | Partitioned Parquet files, with manifests and checksums | Query results, exported from a hosted warehouse | JSON responses, one request at a time | Tables or shares inside a cloud warehouse | A stream or table populated by a pipeline you define | Whatever your pipeline manages to produce |
| Where the data lives | Your disk, your bucket, your warehouse | The vendor platform, with exports as the way out | The vendor platform, re-fetched on demand | Your warehouse account, for as long as the contract runs | A hosted store, or a sink you configure | Your infrastructure, at your operating cost |
| Full-history backfills | The default product, since full history is a coverage window | Queryable, but awkward to export at archive scale | Pagination against per-request billing | Included in scope, subject to contract and warehouse cost | A backfill job you run, size, and pay for | Months of recapture whenever a decoder changes |
| Cost model | Per dataset and coverage window, priced before you buy | Seats plus query or export credits | Per request, credit, or compute unit | Annual subscription, plus your own warehouse compute | Per pipeline, indexed volume, or compute | Nodes, storage, and engineer time, permanently |
| Cost of re-reading history | Zero, because the files are already yours | Another query against your credit balance | Another full paginated crawl | Warehouse compute on every scan | Another backfill run | Your own compute bill |
| Schema stability | Versioned and immutable, with corrections shipped as new versions | Curated tables evolve, so definitions can change under a query | Versioned by endpoint, at the vendor cadence | Vendor controlled, with change notices by contract | Yours to define, and yours to migrate | Yours to define, and yours to migrate |
| Decoder maintenance | Ours, with decoders pinned to program versions | Platform and community curators, coverage varying by protocol | The vendor, for the endpoints it offers | The vendor, for the tables in scope | Yours, per protocol you index | Yours, through every program upgrade |
| If you stop paying | You keep every file already delivered | Access ends, and exported extracts are what remains | Access ends | The share is revoked, so copies must be made in advance | Pipelines stop, and sinked data remains | Nothing changes, because you were always the operator |
| Strongest at | Training sets, backtests, and warehouse loads over deep history | Exploration, dashboards, and sharing analysis | Application backends and point lookups | Broad multi-chain coverage inside an existing warehouse | Protocol-specific, app-shaped views of live data | Coverage nobody sells, or data you may never send outside |
Product names identify categories for readers orienting themselves. They indicate neither partnership nor endorsement.
Honest limits
Evaluating quickly is worth more than winning every comparison. If your workload is on this list, one of the categories above will serve you better.
Not sure which? Ask us →FAQ
It depends on how the data will be read. For interactive exploration, a hosted SQL platform is fastest to start. For application traffic, a blockchain API or RPC provider is the right shape. For repeated reads over deep history, such as backtests, warehouse loads, and model training, bulk Parquet files you own are the cheapest and most reproducible option.
Dune and Flipside Crypto are hosted SQL platforms: the data stays on their infrastructure and you query it there, billed by seats and credits. datastore.sh delivers the data itself as partitioned Parquet files with manifests and checksums, priced per dataset and coverage window. Once delivered, the files are yours to keep, and re-reading history costs nothing.
APIs are billed per request, which suits point lookups and live application traffic. Historical work reads the same large ranges repeatedly, so paginating that history through a metered endpoint is slower and more expensive than receiving it once as files you own and can scan locally.
Rarely, once the second year is counted. Archive nodes, per-program decoders, reconciliation, gap repair, and backfills are ongoing obligations rather than a one-time build, and every protocol upgrade reopens them. Building makes sense when the coverage does not exist commercially, when data cannot leave your perimeter, or when the pipeline is your competitive edge.
It does not serve real-time application traffic, it does not host a query engine, and it covers two networks, Solana and Hyperliquid, rather than every chain. For live lookups or interactive exploration, an API or a SQL platform is the better tool, and often a complement rather than a replacement.
Yes. Every dataset page documents its tables, typed columns, and coverage, and free 100-row samples are available where shown. Engineers can validate schemas and integration against a sample before any purchase or contract.
Start with the data
Browse documented datasets or send the exact protocol, tables, and historical coverage your team needs.