Analytics and monitoring decision
Oddpool
A capable developer can build a useful subset (search + normalized live feed + simple arb/whale alerts) in about a week and low ongoing maintenance, but reproducing Oddpool's complete product (extensive historical microstructure, curated dashboards, enterprise delivery and SLAs) is substantially more work and operational cost.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need — the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship.
What a replacement has to do
- Poll Kalshi and Polymarket APIs, normalize contract/orderbook schemas, index events for full-text search, serve HTTP search endpoints and a WebSocket feed, compute simple cross-venue arbitrage and whale alerts.
What it still won’t have
- 750+ curated dashboards and polished UI
- Enterprise features: S3 dumps, historical microstructure delivery, SLAs and dedicated support
- Unlimited WebSocket subscriptions and high-rate production delivery
- Oddbot AI analyst and integrated paper-trading UX
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 2 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal Oddpool replacement using Node.js (Express) + Postgres (TimescaleDB) + Redis + a single EC2 (or DigitalOcean) instance and S3 for snapshots. In scope: 1) periodic fetchers that call Kalshi and Polymarket REST APIs and normalize market/event and orderbook schemas, 2) store normalized events and 1m/5m orderbook snapshots in TimescaleDB and raw snapshots in S3, 3) an HTTP search endpoint that returns aggregated event-level results (total_volume, market_count, per-venue markets), 4) a background worker that computes simple arbitrage opportunities (price differences net of fees) and flags trades over $500 as whale events, 5) a WebSocket endpoint that streams dist/book/trade messages to connected clients, 6) basic auth-protected dashboard to view search results and live feed. Out of scope: full 750+ curated dashboards, AI analyst, enterprise S3-dump delivery, multi-region scaling. Require logging, retry/backoff for upstream API calls, idempotent backfill scripts, sensible rate-limiting, unit tests for normalization logic, and end-to-end tests for search and websocket feeds.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 4 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score67
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.
How scoring works →Cited sources · 4
Every page the run actually retrieved.
- official productOddpool | Compare Kalshi & Polymarket Odds in Real Time
- official pricingPricing | Oddpool Prediction Market Data
- open sourcepmxt-dev/pmxt
- open sourceJon-Becker/prediction-market-analysis
Integrity checks
What held up, and what did not.
