Databento Alternatives: Market Data APIs for Quant Researchers

Databento Alternatives: Market Data APIs for Quant Researchers

What to consider when you need something different from Databento's institutional tick data — or something that sits on top of it.

What Databento Does Well

Databento carved out a distinctive position in the market data landscape by offering institutional-grade data through a modern, developer-first API. Founded by Christina Qi, a former hedge fund manager with direct experience in high-frequency trading, the platform was designed to address the gap between expensive legacy terminal products and basic retail-tier APIs.

The core product is tick-level market data with nanosecond timestamps, covering U.S. equities, futures, and options across 60-plus trading venues. The data is sourced from direct exchange feeds rather than consolidated SIPs, which matters for anyone doing execution analysis or microstructure research. Databento supports Python, C++, and Rust client libraries, offers batch flat-file downloads alongside streaming APIs, and uses a usage-based pricing model where you pay per gigabyte of data consumed.

For researchers working at the intersection of market microstructure and quantitative strategy development, Databento represents a genuine step forward in accessibility. Data that previously required six-figure enterprise contracts is now available to individual researchers for dollars per query.

Why Users Explore Alternatives

Despite its strengths, there are several legitimate reasons researchers look beyond Databento.

The most common concern is cost predictability. Usage-based pricing is attractive when you are making occasional queries, but it becomes difficult to budget when you are running large-scale backtests that consume terabytes of historical data. A researcher pulling full order book data for the entire U.S. options market over several years can quickly accumulate costs that rival traditional enterprise pricing.

Another reason is data type mismatch. Databento is optimized for raw, tick-level market data. If your workflow does not require nanosecond granularity — if you are running daily-frequency momentum strategies, for example — you are paying for a level of precision you do not use. A provider with daily or minute-level data at a flat rate may be more efficient.

Finally, Databento's data is intentionally raw. The platform does not provide derived datasets, regime classifications, event feeds, or point-in-time universe snapshots. If your research depends on these types of structured inputs, you need an additional data source regardless of which raw data provider you choose.

The Alternatives

Polygon.io (Massive)

Polygon is the most direct comparison point for most researchers. It offers real-time and historical price data for U.S. equities, options, forex, and crypto through a RESTful API and WebSocket streaming. The pricing is flat-rate rather than usage-based, which makes cost forecasting straightforward.

Polygon's granularity goes down to the tick level for equities, but the data is sourced from SIP feeds rather than direct exchange feeds. For daily or minute-level strategies, the practical difference is negligible. For microstructure research, the distinction matters.

algoseek

algoseek provides institutional-quality historical intraday data specifically designed for quantitative research and machine learning applications. Their data products include tick-by-tick trades and quotes, order imbalance data, and pre-computed analytics across U.S. equities, futures, and options.

algoseek differentiates itself through data quality and research readiness. The data is cleaned, normalized, and structured for immediate use in backtesting environments. The tradeoff is pricing: algoseek targets institutional budgets, and individual researchers may find the costs prohibitive.

Tick Data (now part of FactSet)

Tick Data has been a fixture in institutional quantitative research for decades. They provide historical intraday data across global equities, futures, forex, options, and indices, with coverage going back further than most competing providers. The data is available in flat-file format, which integrates cleanly with most backtesting frameworks.

The acquisition by FactSet positions Tick Data as part of a broader enterprise data stack. For independent researchers, the practical implication is that pricing and access may increasingly require enterprise-level commitments.

ORATS

ORATS occupies a specialized niche focused on options analytics and volatility data. Their products include historical implied volatility surfaces, Greeks, skew analytics, and earnings-related volatility data. For researchers whose strategies are centered on options or volatility, ORATS provides a depth of derived analytics that raw data providers simply do not offer.

ORATS is not a general-purpose market data provider. You would not use it for equity price bars or fundamental data. But within its domain, the data is among the most comprehensive available to non-institutional users.

Comparison Table

Beyond Raw Data: The Research Layer

One pattern that emerges across all of these providers is that they are fundamentally in the business of delivering raw or lightly processed market data. They give you the building blocks: prices, quotes, order books, volatility surfaces. What they do not give you are the structured datasets that translate those building blocks into systematic research inputs.

Consider a practical example. You are building a cross-sectional momentum strategy that trades weekly options on liquid U.S. equities. To backtest this properly, you need to know which stocks actually had weekly options listed on each historical date. None of the providers listed above offer this dataset. You would need to reconstruct it yourself — a pipeline that involves querying options chains for every ticker on every date, identifying expiration patterns, and storing the results in a point-in-time format that avoids lookahead bias.

This is exactly the kind of problem that Alphanume was built to solve. Alphanume operates on what we call the second layer of market data infrastructure: structured, point-in-time research datasets designed for systematic trading. Their historical optionable universe dataset provides exactly the data described above. Their dilution events feed processes SEC filings into machine-readable corporate action events. Their S&P 500 risk regime endpoint delivers a daily binary classification of market conditions.

The relationship between Databento and Alphanume is complementary, not competitive. You would use Databento when you need raw tick data with nanosecond precision. You would use Alphanume when you need structured research datasets that have already been processed into strategy-ready inputs. Many serious research workflows require both layers.

Choosing the Right Provider

If your research requires tick-level or order book data and you are comfortable with usage-based pricing, Databento remains the most accessible path to institutional-grade data for independent researchers.

If you prefer flat-rate pricing and your strategies operate at daily or minute-level frequency, Polygon provides the cleanest developer experience and broadest feature set in that category.

If your focus is specifically on options analytics and you need pre-computed volatility surfaces and Greeks, ORATS is the specialized choice.

And if your bottleneck is not raw data but the structured research datasets you need for universe construction, point-in-time validation, and event-driven strategy development, Alphanume fills a gap that raw data providers leave open by design.