Comparison
datastore.sh vs Flipside Crypto
SQL analytics platform · reviewed September 2026
Short answer
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.
What Flipside Crypto is
Flipside Crypto publishes curated models of on-chain activity and exposes them through SQL, with community analysis and programs built around that data. For an analyst who wants modeled tables without operating infrastructure, it removes a large amount of setup.
As with any hosted platform, the data stays on the vendor side and the models are maintained by the vendor. Improvements to a model are improvements to a shared resource, which is useful for analysis and inconvenient for a production pipeline that expected last quarter's column semantics.
How the two products differ
The comparison is between product models rather than feature lists. Prices, rate limits, and chain counts change without notice, so none are quoted here.
| Dimension | datastore.sh | Flipside Crypto |
|---|---|---|
| What you receive | Partitioned Parquet files with typed schemas, manifests, and checksums | Query results over curated models, exported as needed |
| Where the data lives | Your own storage, after delivery | Flipside infrastructure, queried in place |
| Table design | Close to the chain: decoded instructions and typed events | Curated analyst models, normalized for readability |
| Schema guarantees | Immutable versions, with corrections published as new versions | Models are revised as curation improves |
| Cost of repeated scans | No incremental cost once delivered | Platform limits and credits apply per query |
| Coverage shape | Deep on Solana and Hyperliquid | Broad across supported chains |
| Offline and air-gapped use | Supported, since files move to your environment | Not applicable, since access is through the platform |
| Best fit | Reproducible research, training corpora, warehouse loads | Analyst exploration and published community analysis |
When Flipside Crypto is the better choice
- You want modeled, human-readable tables and are happy to work inside a hosted SQL environment.
- Community analysis and shared queries are part of how your team works.
- You need a quick answer across several chains rather than a durable archive of two.
When datastore.sh is the better choice
- Results have to be reproducible months later against the exact bytes you analyzed.
- You want raw decoded structure rather than a curated model that may be revised.
- Your workload is a repeated full scan, not an interactive query.
- Governance requires the data to sit inside your own perimeter.
Using both together
Curated models are a good way to scope a question before committing to an archive. Once the programs, tables, and date range are known, file delivery turns that scope into a fixed asset your pipeline can depend on.
Frequently asked questions
What is the difference between datastore.sh and Flipside Crypto?
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.
Are curated models a problem for production pipelines?
They are a tradeoff rather than a defect. Curation makes tables easier to read and revises them as understanding improves, which is correct for analysis. A pipeline that pinned behavior to a column definition needs the opposite property. datastore.sh publishes immutable schema versions and ships corrections as new versions so a loaded dataset cannot change underneath a job.
Can I get Solana instruction-level data from either?
Analyst platforms typically expose decoded events and normalized activity tables. datastore.sh publishes instruction-level Solana data, including inner instructions and account state, decoded per program and pinned to program versions. If your work depends on instruction structure rather than summarized activity, check the specific tables before choosing.
Which is better for a machine learning training set?
File delivery, in most cases. Training runs read the same history many times, need the input to stay byte-identical across runs, and usually require the data inside the environment where training happens. Parquet files with pinned schema versions and checksums satisfy those constraints without repeated queries against a hosted platform.
Start with the data
Skip the indexing project. Run the query.
Browse documented datasets or send the exact protocol, tables, and historical coverage your team needs.