Sportmonks Alternative: 350+ Bookmakers Without the Odds Add-On

Sportmonks is a good football data API. You get fixtures, live scores, standings, lineups, statistics and injury feeds across a very deep league catalogue. Then you go looking for odds and discover they are not in your plan. Odds sit behind a separate add-on, the premium feed is pre-match only, and historical odds vanish seven days after kick-off.

If odds are the part you actually need, this guide shows what the gap looks like and how to fill it. Every OddsPapi number below came off the live API on a real Brasileiro Serie A fixture before this post went up. All Sportmonks facts are quoted from their own pricing and documentation pages, linked inline.

Where Sportmonks charges for odds

Sportmonks separates odds from the core football product. Their plans and pricing page lists Starter at EUR 29/month for 5 leagues, Growth at EUR 99 for 30 leagues, Pro at EUR 249 for 120 leagues, and Enterprise on request for 2,300+ leagues. Those plans carry fixtures, live scores, standings, player and team data, lineups, statistics and injuries. Odds are not included in any of them.

Odds come as a paid add-on instead. Sportmonks lists an Odds and Predictions bundle from EUR 15/month on the pricing page, and a separate Premium Odds Feed at EUR 129/month for Lite (10+ bookmakers, 42 markets) and EUR 199/month for Pro (120+ bookmakers, 42 markets). Both premium tiers are listed as pre-match, updated around every minute, with historical access for up to 7 days after kick-off.

Their free plan is real but narrow: the free plan page covers the Danish Superliga and Scottish Premiership. Paid tiers offer a 14-day trial.

Where Sportmonks wins, honestly

Sportmonks carries a full football data layer that OddsPapi does not have and does not pretend to have. Live scores, lineups, standings, player statistics, injuries and their prediction endpoints are genuinely useful, and the league catalogue runs deeper on football than anything odds-first.

OddsPapi is an odds aggregator. There are no scores, no player statistics and no lineups in the feed. If your app needs a league table or a starting XI next to the price, you need a stats provider, and Sportmonks is a reasonable one. The realistic setup for most builders is both: Sportmonks or a similar provider for match data, OddsPapi for the pricing layer. The same logic applies to the API-Football comparison, and the honest breakdown of what an odds-first feed does and does not carry is in the free sports data API guide.

What the odds layer looks like on a free key

Grab a free API key. The key is a query parameter, not a header. No add-on, no league cap.

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.oddspapi.io/v4"

def api_get(path, **params):
    params["apiKey"] = API_KEY
    r = requests.get(f"{BASE_URL}{path}", params=params, timeout=30)
    r.raise_for_status()
    return r.json()

print(len(api_get("/sports")), "sports")          # 69
print(len(api_get("/bookmakers")), "bookmakers")   # 381

The worked example is Atletico Mineiro against EC Bahia in the Brasileiro Serie A, fixture id1000032566886848. That is a league you would need a mid or upper tier plan to reach elsewhere. On the free key it is one call.

FID = "id1000032566886848"
books = api_get("/odds", fixtureId=FID)["bookmakerOdds"]

def price(slug, market_id, outcome_id):
    node = (books.get(slug, {}).get("markets", {})
                 .get(market_id, {}).get("outcomes", {}).get(outcome_id, {}))
    player = node.get("players", {}).get("0")
    if not player or node.get("active") is False:
        return None
    return player.get("price")

for slug in sorted(books):
    h, d, a = (price(slug, "101", o) for o in ("101", "102", "103"))
    if h:
        print(f"{slug:20} {h:>7} {d:>7} {a:>7}")

Seventeen bookmakers priced the match, and 208 distinct markets were on the board across them. Pinnacle alone priced 53. Filter on active is False rather than a truthy active check, because the live feed sometimes ships active: null beside a valid price.

Two sharp books (Pinnacle, SBOBet) and two prediction markets (Kalshi, Polymarket) sit next to the US retail books in the same response. That mix is the thing a 10-bookmaker tier cannot give you, because the value of an aggregate is the disagreement between very different pricing models.

Count real bookmakers, not slugs

Notice the grouped rows. Several slugs quote the same numbers to the decimal, so 17 slugs collapse to 13 independent quotes on this fixture.

from collections import defaultdict

groups = defaultdict(list)
for slug in books:
    quote = tuple(price(slug, "101", o) for o in ("101", "102", "103"))
    if None not in quote:
        groups[quote].append(slug)

print(len(books), "slugs ->", len(groups), "distinct quotes")
for quote, slugs in groups.items():
    if len(slugs) > 1:
        print("  duplicate:", quote, slugs)

# 17 slugs -> 13 distinct quotes
#   duplicate: (2.15, 3.2, 3.5)  ['betmgm', 'borgata']
#   duplicate: (2.07, 3.3, 3.6)  ['betparx', 'ballybet', 'fourwinds']
#   duplicate: (2.1, 3.2, 3.5)   ['caesars', 'williamhill']

The catalogue flags williamhill as a clone of caesars, but it reports cloneOf: null for the BetMGM and BallyBet groups. Comparing quotes is the only reliable way to find them. Deduplicate before you average anything, or one pricing feed gets counted three times in your consensus number.

De-vig the sharp price

quote = {o: price("pinnacle", "101", o) for o in ("101", "102", "103")}
implied = {k: 1 / v for k, v in quote.items()}
total = sum(implied.values())

print(f"overround {(total - 1) * 100:.2f}%")
for k, label in [("101", "Atletico MG"), ("102", "Draw"), ("103", "Bahia")]:
    print(f"{label:12} fair prob {implied[k]/total:.4f}  fair odds {total/implied[k]:.4f}")

# overround 4.57%
# Atletico MG  fair prob 0.4511  fair odds 2.2169
# Draw         fair prob 0.2855  fair odds 3.5031
# Bahia        fair prob 0.2634  fair odds 3.7958

Pinnacle carried a 4.57% margin. Best available prices across all 17 books were 2.174 on Atletico at Polymarket and Kalshi, 3.571 on the draw at Kalshi, and 3.846 on Bahia at Kalshi. Those are the best numbers on the board rather than a claim that any of them is a profitable bet. For the mechanics across every sport, see line shopping in Python.

Historical odds without the seven-day clock

Sportmonks lists historical odds access for up to 7 days after kick-off on the premium feed. OddsPapi returns the recorded price history on the free tier, capped at three bookmakers per call.

hist = api_get("/historical-odds", fixtureId=FID,
               bookmakers="pinnacle,bet365,draftkings")   # max 3 per call

snaps = (hist["bookmakers"]["pinnacle"]["markets"]["101"]
             ["outcomes"]["101"]["players"]["0"])          # a LIST of snapshots

print(len(snaps), snaps[0]["price"], "->", snaps[-1]["price"])
# 14 2.14 -> 2.12

Pinnacle showed 14 recorded points on this fixture, drifting between 2.06 and 2.14, and Bet365 showed 20. Note the shape difference: the historical response nests under bookmakers rather than bookmakerOdds, and players["0"] is a list instead of a single price. Pull it once into your own store and the seven-day window stops mattering, which is what the odds database guide walks through.

Which one should you use

Pick Sportmonks if your product is built on football match data and odds are a secondary feature you are happy to pay an add-on for. Their statistics, lineups and predictions have no equivalent here.

Pick OddsPapi if odds are the product: line shopping, model building, arbitrage, closing line value, or anything that needs sharp books and exchanges in the same payload across more than football. If you want a screener rather than an API, jedibets.com covers player props with Discord alerts. For the wider field, the 2026 odds API comparison ranks six providers by use case.

Frequently asked questions

Does Sportmonks include odds in its API plans?

No. Sportmonks lists odds as a separate add-on rather than part of the Starter, Growth or Pro plans. Their pricing page shows an Odds and Predictions bundle from EUR 15/month, and the Premium Odds Feed is listed at EUR 129/month for Lite and EUR 199/month for Pro.

How many bookmakers does Sportmonks cover?

Their Premium Odds Feed page lists 10+ bookmakers on the Lite tier and 120+ on the Pro tier, both with 42 markets. OddsPapi’s catalogue lists 381 bookmakers, and 17 priced the sample Brasileiro Serie A fixture used in this guide.

Does Sportmonks offer live in-play odds?

Their Premium Odds Feed tiers are listed as pre-match, updated around every minute, and the pricing page mentions in-play odds in the Odds and Predictions bundle. OddsPapi ships pre-match and live odds plus a WebSocket feed that pushes changes rather than making you poll.

Is OddsPapi a replacement for Sportmonks?

Not for match data. OddsPapi carries no scores, lineups, standings or player statistics by design. It replaces the odds layer only. Most teams that switch keep a stats provider and use OddsPapi for pricing.

Can I get free historical odds?

Yes. The historical odds endpoint is available on the free tier, capped at three bookmakers per call. Pinnacle returned 14 recorded price points and Bet365 returned 20 on the sample fixture, with no seven-day expiry on access.

Try the odds layer free

No add-on, no league cap, no sales call. One key gets you 381 bookmakers across 69 sports, sharp books and prediction markets in the same response, plus free historical prices and a WebSocket feed. Get your free API key and point it at a fixture your current plan cannot reach.