Predictefy vs Probalytics: Prediction Market Data APIs Compared (2026)

Product details and monthly prices checked on October 27, 2026.

How do Predictefy and Probalytics compare?

Why choose Predictefy for research that becomes a product?

A backtest often starts with one venue and expands when the same question trades elsewhere. Predictefy keeps those markets in one schema. You can discover a contract, inspect its observations and find related listings through cross-venue matching on every plan.

That helps a researcher retain the same market and outcome vocabulary when moving from historical analysis to a live watchlist. Predictefy provides WebSocket delivery, trader intelligence and client-signed execution for supported venues. Paper trading provides a place to test how the application handles orders before committing capital.

For a cross-venue strategy, Predictefy's Pro arbitrage feed adds checks on current depth, venue fees and resolution equivalence. Keep the modeled edge separate from the result you actually obtain. Prices can move while an order is being submitted.

What does Probalytics provide for historical analysis?

Probalytics publishes market, fill and order-book datasets for Polymarket and Kalshi. Its ClickHouse interface suits researchers who want to aggregate many records in SQL. Parquet exports support analysis in a local data environment on eligible plans.

The published schema includes bid and ask ladders, observation and indexing timestamps, and continuity information. These are useful fields when reconstructing a book and deciding whether a test period has usable coverage.

Its homepage dates Polymarket book snapshots from November 2025 and Kalshi book snapshots from May 2026. Those are book-history claims. They should not be extended to the older catalog or fill archive, or read as a promise that every market has an uninterrupted recording from those dates.

Which history should you test before choosing a provider?

Begin with the decision your strategy makes. An hourly directional model may use candles. A strategy that reacts to queue changes needs order-book events and a defined fill model. A wallet-flow study needs attributed trades. These are different datasets, even when they describe the same market.

Predictefy returns provenance and quality information for historical records. Request the exact venue, outcome and date range, then inspect those fields. Its plan history windows are seven days on Free, 12 calendar months on Builder and an unlimited plan window on Pro. Choose the captured range that supports your experiment.

For either provider, sample a quiet period and a busy period. Count gaps, inspect timestamp precision, and verify that prices and sizes reconstruct the state your strategy would have seen. A large row count alone does not establish a reliable backtest.

How do the plans affect the comparison?

Predictefy meters actions in credits. Free includes 25,000 per month; Builder includes 250,000 for $48.88; Pro includes 2,000,000 for $149 and adds history replay, the price-gap feed and arbitrage. Commercial use is included through Enterprise terms. See the current plan guide for access and metering.

Probalytics Starter is $39 a month for market and trade data with a rolling 30-day history window. Pro is $129 and adds its longer market and trade archive plus bulk Parquet exports. Historical order-book data starts with Orderbook at $249. A three-day trial covers the last seven days across venues and access methods.

Keep the comparison at the workload level. Queries, result rows, exported data and action credits are different units. Estimate the cost of one complete research run and the daily monitoring that follows it.

How should you evaluate a Probalytics alternative?

1. Select one Polymarket contract and one comparable Kalshi contract with clear resolution rules.
2. Request the same date window and record the available coverage, gaps and identifiers.
3. Run the strategy with explicit fees, latency and conservative fill assumptions.
4. Use Predictefy to carry the selected markets into a live watchlist and paper-trading workflow.

Use Predictefy's history guide to choose the request shape, then create a free evaluation key. Build the test around the records your model actually consumes.

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