Prediction Market Data Providers: A Complete Comparison

Why this matters

The prediction market space has grown a lot in the last few years. There are now a handful of major platforms — Polymarket, Kalshi, Novig, PredictIt, Metaculus — each with real volume and real users. For developers and data teams, that's both an opportunity and a headache.

The opportunity is obvious: this data is genuinely useful. Prediction market prices are one of the cleanest real-time signals for event probabilities that exist. The headache is that getting it requires stitching together multiple APIs with different schemas, rate limits, and authentication methods — and maintaining those integrations over time.

This post breaks down the main options for accessing prediction market data and what to look for when comparing them.

Direct platform APIs

Every major prediction market has some form of API, but they vary a lot in maturity and developer experience.

Polymarket has a public REST API and is one of the more developer-friendly platforms. It's built on Polygon, so market data is also accessible on-chain if you want to go that route. The schema is reasonably well-documented and it's the largest platform by volume for political and macro markets.

Kalshi is a regulated exchange, which gives it some credibility advantages, but its API requires an account and is more focused on its own trading infrastructure than on raw data access. Documentation has improved but can still be sparse in places.

Novig is newer and has competitive odds on sports markets. Its API is functional but less mature than Polymarket's. Worth watching as the platform grows.

PredictIt and Metaculus both have APIs but are further behind on developer tooling. PredictIt has regulatory constraints that limit its market selection. Metaculus is more focused on community forecasting than trading.

If you only need one platform, direct access is fine. But most use cases benefit from having at least two or three platforms, and that's where things get complicated quickly.

The fragmentation problem

Here's the practical problem with direct platform APIs: every one of them is different.

A market on Polymarket is identified by a CLOB-style order book ID. The same event on Kalshi has a ticker like PRES-28-D. On Novig it's a completely different string. There's no standard — no shared event ID, no shared schema, no way to join records across platforms without custom mapping logic.

If you want to build a cross-platform view — a simple probability comparison for the same event across three platforms — you need to write three different API integrations, build your own event matching logic, normalize the schemas yourself, and then handle all the edge cases when platforms disagree on event boundaries or resolution rules.

This is doable. Plenty of teams have done it. But it's a lot of undifferentiated infrastructure work, and it breaks whenever a platform changes its API (which happens more than you'd think).

What a unified prediction market data API does differently

A unified data provider handles all of that infrastructure so you don't have to. Instead of writing five integrations, you write one. Instead of normalizing five schemas, you get one clean schema. Instead of building your own event matching, you get cross-platform entity resolution out of the box.

The specific things to look for when evaluating a unified provider:

Platform coverage. How many platforms does it cover, and are they the ones you care about? A provider that covers Polymarket and Kalshi but not Novig or Metaculus might not be enough depending on your use case.

Data freshness. How often does the data update? For arbitrage use cases, sub-minute updates matter. For a research dashboard, 5-minute updates might be fine. Make sure the update frequency matches what you're building.

Historical data. Resolved markets are genuinely valuable for backtesting and training models. Not all providers offer historical data, and those that do vary significantly in how far back they go and how the data is structured.

Schema consistency. Does the normalized schema actually cover the fields you need? Things like volume, open interest, resolution date, and order book depth aren't always included.

Rate limits and reliability. What happens when you hit the limit? Is the uptime tracked publicly? These matter a lot in production.

Prediction Markets API: what we cover

Prediction Markets API gives you a single endpoint for normalized market data across Polymarket, Kalshi, Novig, PredictIt, and Metaculus. Cross-platform entity resolution is built in — so the same event is matched across platforms and returned under a single market ID.

The free Signal plan gives you 2,000 entities per month across three platforms with no time limit. The Edge plan ($49/month) bumps that to 100,000 entities with sub-minute updates and all platforms. The Alpha plan ($199/month) covers 3,000,000 entities per month with the fastest available update frequency.

All plans include REST access. Historical data is available on Edge (90 days) and Alpha (full archive).

Setup takes a few minutes. You get a key, make a call, and get back normalized JSON. There's no custom integration work, no schema mapping, and no event matching to build.