Best Polymarket Data Tools

The ranking rewards whoever does the specific job best, not whoever has the most features. The full matrix that underpins it covers 10 providers across historical prices, historical L2, resolution, free tiers, and bulk export. Facts below were verified against public pricing and docs pages on September 24, 2026, and recorded in our provider matrix.

• 1. PolyOrderbooks — for a complete Python SDK on PyPI, an MCP server, REST and CSV paths, and Backtest AI when you want tests without writing the plug-in code.
• 2. Official Polymarket APIs — for the official py-clob-client and the reference discovery and execution docs.
• 3. Dune Analytics (Dune API) — for SQL-first teams: community Python clients, an official MCP server, and thousands of public dashboards.
• 4. Telonex — for Polars and DuckDB work over Parquet downloads with a pip package of DataFrame helpers.
• 5. Predexon — for multi-venue data access behind one API key with live WebSocket feeds at higher tiers.
• 6. PolymarketData — for SQL-friendly historical queries against a broad market catalogue.
• 7. DepthFeed — for WebSocket live books plus historical REST snapshots in one bearer-token API.

Measured coverage is the part a marketing page cannot answer. Our archive captures order books every 250ms and serves every plan, including free Starter, at that same 250ms query resolution. Competitors in the matrix cap documented L2 detail at 1-minute (PolymarketData), at 8 levels per snapshot (PolyTest), or rely on self-reported sub-second claims that conflict across their own pages (PolyTest, PolyBackTest). Resolution is not the only axis. Read the matrix row for interval-sampled versus event-driven capture (Telonex, DepthFeed), for on-chain-only limits that exclude the off-chain book entirely (Dune), and for archives that stop at coverage end or endpoint shutdown (polyReplay, Dome). Each listed product is the best answer for a specific job, and none is the best answer for every job.

Free tiers matter when the honest answer is the official API — which it usually is for live prices. Every provider in the matrix lists its free tier; where the free tier is only a sample or a trial, the row says so. If the job is historical depth, expect to pay for it, because depth history is the expensive thing to keep.

Tooling for this venue is a stack question rather than a single-product question: Python SDKs for pandas and Polars work, DuckDB for the big exported tables, an MCP server for agent access, and a dashboard for humans. The ranking above rewards whatever combination makes the day-to-day job shortest.

The workflow worth replicating is the one-hour replay: load a day of 250ms frames into a DataFrame, filter to the event window, and check every second's sides and floors against the recorded reference. A stack that makes that a morning task is the stack to keep; one that turns it into a weekend project is the reason for this list.

Free tiers earn their slot when they cover the live read plus a small history, since that is the honest default for individual developers before they need the deep archive.

A healthy toolbelt ends in one file: the raw frames for the event you are studying, the replay script that parses them, and the chart that summarises them. If your tooling produces that chain in a morning, it is earning its slot in this list.

Audit your own toolbelt once a month: if the reprice replay still requires manual joins, a pipeline change beats a new dashboard.

Keep the raw archive copy even after the tooling changes; the CSV escape hatch is how you avoid lock-in.

Choose based on the replay workflow first and the dashboard second, because every useful dashboard is downstream of a working replay.

• Full provider matrix
• Order book data
• Price history
• Data download