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Live Tennis API VS mlsql

Compare Live Tennis API VS mlsql and see what are their differences

Live Tennis API

Real-time tennis data and model analysis over REST + WebSocket. ATP, WTA, Challenger, ITF — singles and doubles.

Rating
0 reviews
Pricing
Freemium $9.99 / Monthly (Basic — history + point-by-point, 1k req/day)
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Alternatives Is it good?
mlsql

Infer SQL queries from plain-text questions and table headers.

Rating
0 reviews
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Alternatives Is it good?
Firma.dev
Ship e-signatures in your app today, ~5¢ per envelope (€0.049) sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, mlsql seems to be more popular. It has been mentioned 2 times since March 2021.

social mentions
0 vs 2
Sports popularity
100% vs 0%
alternatives listed
5 vs 1

Base details

Website, pricing, platforms and company facts side by side.

Live Tennis API
mlsql
Website livetennisapi.com github.com
Pricing
Freemium $9.99 / Monthly (Basic — history + point-by-point, 1k req/day) Official pricing
—
Platforms
REST API Python JavaScript Node JS SaaS WebSocket +3
—
Company Startup from the United States · 1 - 9 employees · 2026 —
Listed in
Sports Scores Developer APIs Sports Live Scores APIs +2
Education LMS Online Learning Web App +1

About Live Tennis API and mlsql

In their own words, as submitted to SaaSHub.

Live Tennis API
mlsql

Live Tennis API is a real-time tennis data API for developers, traders and analysts. One REST + WebSocket feed covers ATP, WTA, Challenger and ITF — singles and doubles. What you get Live scores updated point by point, with full match state (server, game and set score, tiebreaks) Fixtures, player...

Read more about Live Tennis API

No description of mlsql yet.

Features and specs

What each product offers, as listed by its team.

Live Tennis API 12 features
mlsql 0 features
  • Coverage
    ATP, WTA, Challenger and ITF — singles and doubles
  • Live scores
    Point-by-point, sub-second, with server, game/set score and tiebreak state
  • Streaming
    WebSocket push feed and webhooks for live matches (Ultra)
  • Win-probability
    Model win-probability and analysis on every live point (Ultra)
  • Market odds
    Match-winner market odds (Pro)
  • Match history
    Completed results, events and timeline; point-by-point match tape back to 2023
  • In-play statistics
    Aces, double faults, serve split, hold/break, break points (Ultra)
  • Rankings
    As-of-match ATP/WTA, ITF and UTR rankings for backtests
  • Shot-level charting
    Serve direction, rally construction and serve/return charting for 11,000+ matches back to 1960
  • Developer tooling
    OpenAPI 3.1 spec, official Python and JavaScript SDKs, MCP server for AI agents
  • Rate limits
    Free 30 req/min · 100/day up to Ultra 600 req/min · 500k/day
  • Format
    JSON over REST, UTC timestamps, read-only, bearer-key auth

No features have been listed yet.

Analysis

An editorial look at what each product does well and who it suits.

Live Tennis API
mlsql

No analysis of Live Tennis API yet.

Overall verdict

  • MLSQL is a solid open-source unified platform that blends SQL with machine learning, making data engineering and ML workflows accessible through a single, declarative language. It's a good choice for teams looking to streamline big data and ML pipelines without switching between multiple tools.

Why this product is good

  • It unifies data processing and machine learning under a single SQL-like syntax, lowering the learning curve for data teams.
  • Built on top of Apache Spark, it leverages a proven distributed computing engine for handling large-scale data.
  • Open-source and actively developed, allowing customization and community-driven improvements.
  • Supports the full ML lifecycle including data preprocessing, training, and deployment within one workflow.
  • Reduces the need to context-switch between separate ETL, analytics, and ML tools.

Recommended for

  • Data engineers and data scientists who prefer SQL-centric workflows
  • Teams working with big data on Apache Spark
  • Organizations wanting to unify ETL and machine learning pipelines
  • Companies seeking an open-source alternative to fragmented ML tooling
  • Analysts looking to build ML models without deep programming expertise

Videos

Walkthroughs and reviews on video.

Live Tennis API 1 video + Add
mlsql 0 videos + Add

Live Tennis API — Real-Time Tennis Scores over REST & WebSocket

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Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Live Tennis API
mlsql
100% 100%
Sports
0% 0%
0% 0%
LMS
100% 100%
100% 100%
Developer APIs
0% 0%
0% 0%
Education
100% 100%

Questions & Answers

As answered by people managing Live Tennis API and mlsql.

What makes your product unique?

Live Tennis API's answer

Most sports-data APIs hand you a score every few seconds. Live Tennis API hands you the match point by point — server, game and set state, tiebreaks — with a model win-probability on every point, over one REST + WebSocket feed.

It covers the whole professional tour: ATP, WTA, Challenger and ITF, singles and doubles, not only the main-tour matches. And the same key opens the historical layer: point-by-point tapes back to 2023, match-winner market odds, as-of-match rankings for backtests, and shot-by-shot charting for 11,000+ matches going back to 1960.

Why should a person choose your product over its competitors?

Live Tennis API's answer

  • Depth: point-level live data, in-play statistics and win-probability, not just scores.
  • Coverage: ATP, WTA, Challenger and ITF — including the lower tiers many feeds skip.
  • Price: a free tier with no card, paid plans from $9.99/month, and Ultra at $99.99/month with 500k requests/day and the WebSocket feed.
  • Developer experience: OpenAPI 3.1 spec, official Python and JavaScript SDKs, an MCP server for AI agents, instant keys, and upgrades prorated in place on the same key.
  • Honesty about coverage: a /history/coverage endpoint states the measured completeness per tour and season instead of promising 100%.

How would you describe the primary audience of your product?

Live Tennis API's answer

Developers and small teams building tennis products: live-score apps and dashboards, betting and trading models, fantasy and prediction tools, and AI agents that need structured tennis data.

A second group is researchers and quants who want point-by-point history, as-of-match rankings and shot-level charting for backtests.

The pricing is aimed at individual developers and startups rather than broadcasters.

What's the story behind your product?

Live Tennis API's answer

Live Tennis API started in 2026 as the data layer behind the founder's own tennis trading research: a point-by-point live feed with a win-probability model for every professional match, including Challenger and ITF.

Rather than keep it internal, the feed, the model and the historical tape were opened as a public API in July 2026, with a free tier so developers can try it without a card. It is operated by JSB Holdings LLC, a small Delaware company.

Which are the primary technologies used for building your product?

Live Tennis API's answer

  • Python (Flask, SQLAlchemy, scikit-learn) for the API and the win-probability model
  • PostgreSQL for match, point and odds storage
  • Redis and Centrifugo for the real-time WebSocket push feed
  • Cloudflare in front of the API; Stripe for billing
  • OpenAPI 3.1 spec, with official Python and JavaScript SDKs and an MCP server

User comments

Share your experience with using Live Tennis API and mlsql. For example, how are they different and which one is better?

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Live Tennis API 0 mentions
mlsql 2 mentions

Tracking Live Tennis API since Aug 2026.

  • Download the files from Docker Container and locally edit them
    I am working on a project that requires me to take user input (as English) and return SQL queries. We will eventually be working with multiple datasets, which is why the valuenet over at https://github.com/paulfitz/mlsql ended up being... Source: over 4 years ago
  • Clone and edit Docker Projects hosted on Github
    I found this great resource here: https://github.com/paulfitz/mlsql. Source: almost 5 years ago

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