Best Prediction Market Data APIs: What to Compare Before You Build

The comparison that matters

Official API versus recorded archive

Polymarket and Kalshi publish their own developer surfaces, and those official interfaces should remain the reference for current market definitions, venue rules and supported trading workflows. They are the correct starting point when an application needs the current state of the venue.

Historical depth is a different product. A full order book is transient: once levels change, a later price-history point cannot reveal the size that disappeared, the spread that was paid or the price impact of an order. A provider claiming realistic replay should show how it captured, normalized and retained those ladders.

How DepthFeed fits the comparison

DepthFeed is purpose-built for research across Polymarket and Kalshi. It serves recorded full-depth books, normalized timestamps, underlying reference fields and settlement-aware market records through REST, with live WebSocket channels for current books. The same schema is used by the browser Backtest Lab and the API.

The product does not turn missing observations into synthetic fills. Coverage stays missing, venue-specific capture is documented, and researchers can test a rule under midpoint, fixed-slippage and depth-aware VWAP assumptions before sending a survivor to paper trading.

A practical selection workflow

• Write down whether the application needs current state, historical replay, or both.
• Request one real market sample with bids, asks, sizes and timestamps before comparing price.
• Verify the earliest usable date and the missing-data policy for each venue.
• Run the same sized order through the ladder instead of comparing only midpoints.
• Confirm export formats, pagination, rate limits and authentication with a small integration.
• Keep venue trading APIs separate from a research archive unless the provider documents both roles.

Start with the data layer, not the vendor list

Prediction-market APIs are often compared as though they sell the same object. They do not. A discovery API answers which markets exist. A trading API submits and manages orders. A price-history API returns trades, candles or sampled probabilities. A live market-data API reports the current book. A historical-depth archive preserves the book that existed before it changed. One provider may cover several layers, but the evaluation should score each layer separately.

For a chart, a timestamped price series may be sufficient. For a scanner, current market metadata and top-of-book quotes may be enough. For execution research, the minimum useful record is materially larger: stable identifiers, both sides of the ladder, size at each price, observation time, source time when available, settlement fields, and a documented policy for gaps and corrections.

A reproducible API evaluation test

Choose one liquid market and one visibly thin market from each venue. Pull the same ten-minute window, then verify that identifiers, timestamps and price units remain stable across responses. For every observation, confirm that price and size arrays have matching lengths, prices are ordered correctly, and crossed or empty books are handled explicitly.

Next, simulate a marketable order at three sizes. Record the best quote, depth-weighted average price, fillable fraction and distance from midpoint. Repeat the query and confirm pagination does not reorder or duplicate observations. This small test reveals more than a feature checklist because it exercises the exact operations a backtest or production monitor will depend on.

Coverage claims need a denominator

Large counts are not comparable unless the unit is defined. A provider may count markets, contracts, outcome tokens, trades, individual price levels or complete book observations. Ask for the counting method, earliest usable date by venue and series, known maintenance windows, and whether deleted or inactive markets remain queryable.

Cadence also needs source context. An event-driven CLOB stream and an adaptively polled REST book cannot honestly share one universal refresh claim. A useful provider exposes observation timestamps and collection method so the user can measure realized freshness for the exact market instead of relying on a site-wide marketing number.

Total cost is more than the monthly price

Estimate the number of markets, date range, book observations and bytes your workload will read. Then include engineering time for venue-specific identifiers, schema normalization, reconnect handling, storage, quality checks and reprocessing. A free upstream API can still be the expensive option when the desired history was never retained or when two incompatible data models must be maintained.

Conversely, do not buy historical depth for a use case that only needs current market discovery. The best prediction market API is the smallest reliable surface that answers the actual question, with a documented path to add live or historical depth when the application grows.

Red flags in prediction-market API comparisons

• A historical-order-book claim illustrated only with candles, last trades or midpoint charts.
• A universal latency or refresh claim with no venue, percentile, sample period or measurement method.
• Market counts that do not define whether the unit is an event, contract, token or observation.
• A backtesting product that never reports spread, slippage, partial fills or unavailable size.
• Missing observations silently converted to zero, forward-filled prices or synthetic executions.
• A comparison page with no update date, methodology, source links or disclosure that the publisher sells one of the products.

Decision guide by use case

How this comparison was researched

DepthFeed's July 2026 market study began with 46 predetermined United States search queries collected through Decodo's Bing Search target. Those results identified candidate providers and page types; they were not treated as Google rankings or monthly search volume. We then validated 70 provider and benchmark hosts and enumerated 522,823 in-scope URLs advertised through their sitemaps.

Because large market catalogs repeat one template thousands of times, the content crawl retained 15,477 representative documentation, product, editorial, comparison and ranking pages and capped programmatic samples per host. The resulting corpus contained more than eight million cleaned words for private gap analysis. No competitor wording is republished here.

The scoring criteria on this page come from repeated user jobs across that corpus, then add requirements a feature list often omits: reconstructable book state, sized fill tests, source and receive times, gap semantics, native identifier retention and a reproducible export manifest. Search-result position and provider popularity do not determine the recommendation.