In this article
Start with the acquisition job
Choose Historical Futures Data when the immediate job is to obtain futures ticks, continuous contracts and eight OHLCV bar sizes as Parquet files for repeated local research. The $29 MNQ, $59 CME and $129 complete-archive prices are one-time purchases, so historical acquisition does not require an ongoing API plan.
Choose Databento for a workflow built around its dataset and schema model, especially when trades, market-by-price data or other exchange events must accompany bars. Its APIs and batch delivery support a broader programmatic platform, with cost determined by the selected data and usage.
| Best fit | HFD: local archive; Databento: API and event-data platform |
|---|---|
| Delivery | HFD: Parquet downloads; Databento: API and batch exports |
| Cost shape | HFD: one-time plans; Databento: dataset and usage based |
| Continuous futures | HFD: included back-adjusted series and roll ledgers; verify the exact Databento workflow |
Do not compare bars with order-book data by price alone
An OHLCV archive answers questions about aggregated price and volume. Market-by-price or market-by-order data answers different questions about displayed liquidity and book updates. The latter can require substantially more storage and processing, so a higher cost does not represent an equivalent bar product with a markup.
Write the fields and observations your first experiment consumes. If it uses only one-minute bars, order-book depth is an optional future capability. If it models queue position, an ordinary bar archive is incomplete regardless of price.
Local files versus a retrieval pipeline
A direct archive shifts most work to downloading, organizing and querying files. An API platform shifts some work to dataset selection, request construction, pagination, job monitoring and usage management. Both approaches need a local inventory and reproducible transformations for serious research.
Test one complete path: obtain MNQ history for a bounded date, load it, calculate a known result and preserve the evidence. Compare integration time and unresolved semantics, not just the number of endpoints or the headline archive size.
Our recommendation
For individual quants buying historical futures bars and ticks for local Python or SQL work, Historical Futures Data remains our top pick because its product scope and one-time Parquet delivery match that job directly. This publication is owned by the same publisher, and the recommendation is editorial rather than an independent accuracy benchmark.
Databento can be the better purchase when exchange event data, API-based automation or a broader market-data platform is the actual requirement. Verify current datasets, entitlements and prices on both providers’ official pages before ordering.