Comparison
datastore.sh vs Bitquery
Blockchain data API · reviewed September 2026
Short answer
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.
What Bitquery is
Bitquery exposes transactions, transfers, DEX trades, and decoded events through query endpoints and streams. For an application that needs a wallet view, a token balance, or a live feed, a request-shaped interface is the natural fit and integration is fast.
Historical work has a different access pattern. It reads large contiguous ranges, repeatedly, and cares that the result is complete and verifiable. Paginating years of a program's activity through a metered endpoint converts a bulk read into millions of billed requests, with reassembly and gap checking left to the caller.
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 | Bitquery |
|---|---|---|
| What you receive | Partitioned Parquet files with typed schemas, manifests, and checksums | JSON responses per request, or a live stream |
| Access pattern it suits | Bulk reads over large contiguous ranges | Point lookups, filters, and live subscriptions |
| Billing unit | Per dataset and coverage window, paid once | Per request, credit, or compute unit |
| Cost of a full backfill | Fixed and known before purchase | Scales with the number of paginated calls |
| Completeness evidence | Manifests and per-file checksums | Caller reassembles pages and checks for gaps |
| Latency profile | Batch delivery, not real time | Real time, including streaming |
| Rate limits | None after delivery, since reads are local | Plan limits apply to sustained extraction |
| Best fit | Backtests, training corpora, warehouse loads | Application backends and live monitoring |
When Bitquery is the better choice
- You are serving an application: wallet views, balances, notifications, or live trade feeds.
- You need sub-second freshness rather than a complete historical archive.
- Your queries are selective lookups, not full scans of a date range.
When datastore.sh is the better choice
- You need every instruction in a range, not the subset a filter returns.
- The same history will be scanned many times and per-request billing would repeat on each pass.
- You need checksums and manifests to prove the archive is complete.
- Extraction volume would sit against plan rate limits for days.
Using both together
The two fit together cleanly. An API keeps an application current, while file delivery supplies the deep history behind research, backtesting, and model training. Neither replaces the other, because they are billed and shaped for opposite access patterns.
Frequently asked questions
What is the difference between datastore.sh and Bitquery?
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.
Why is a blockchain API expensive for historical data?
Because the billing unit does not match the access pattern. History is read in large contiguous ranges, so extraction becomes a long sequence of paginated requests, each billed and rate limited. Cost then scales with the size of the range and repeats on every re-read. File delivery charges once for the range and makes later reads local and free.
Can I use datastore.sh for real-time data?
No. Delivery is batch: partitioned files published on an agreed cadence, not a sub-second stream. Applications that react to live activity should use a streaming API or an indexer. datastore.sh is built for historical depth, where completeness and reproducibility matter more than latency.
Do I still need an API if I buy historical files?
Only if something in your stack needs live data. Research, backtesting, and training run entirely on delivered files. Production applications that display current state usually keep an API or RPC provider alongside the archive, because the two serve different halves of the workload.
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.