TicketsData API Documentation

Getting Started with TicketsData API

The TicketsData API is designed to make it easy for developers, analysts, and businesses to access real-time ticket data from platforms like Ticketmaster, StubHub, SeatGeek, VividSeats, and Eventbrite. Instead of relying on fragile scrapers that constantly break on CAPTCHAs and layout changes, you can use a single API endpoint that delivers clean JSON in a consistent format. This documentation provides examples in multiple programming languages and outlines best practices for integrating the API into your own projects.

Why Use the Documentation?

Good documentation helps reduce onboarding time for developers and ensures that integrations are smooth from day one. In this section you’ll learn how to authenticate requests, query different ticket sources, and handle responses in a way that scales. We also provide example scripts so you can copy, paste, and run code in your own environment within minutes. Whether you are coding in Python, Node.js, PHP, Ruby, or Go, the examples here will give you a head start.

Authentication & API Keys

Every request to the TicketsData API requires an API key, which identifies your account and usage plan. You can generate an API key after creating an account on our website. The key must be passed in the Authorization header of your request. Without it, requests will return an authentication error. Keys are unique per customer, and rate limits apply depending on your chosen plan (Starter, Pro, Business, or Enterprise).

Making Requests

To fetch data, simply provide the event URL you want to query. The API normalizes responses across all supported ticketing platforms so that you don’t need to handle unique HTML layouts or data formats. This means your code will continue to work even if Ticketmaster or StubHub updates their websites. For developers, this saves hundreds of hours otherwise spent fixing scrapers.

Handling Responses

Responses are returned as structured JSON. Typical fields include event name, venue, date, available tickets, and pricing tiers. Depending on the platform, additional metadata such as seat maps or section-level availability may also be included. The consistency of the output makes it easy to feed results into databases, dashboards, or machine learning pipelines without writing custom parsers for each source.

Scaling Your Integration

As your usage grows, you can scale from the Starter plan to Pro, Business, or Enterprise. Higher tiers include larger request volumes, faster concurrency, and advanced features such as dedicated infrastructure. For clients with mission- critical applications, Enterprise also provides service-level agreements (SLAs) to guarantee uptime and performance. The same code you write today on the Starter plan will continue to work at scale with minimal changes.

Best Practices

To get the most out of TicketsData API, we recommend caching frequent queries, monitoring your request usage, and using concurrency responsibly to avoid hitting rate limits. For long-term analytics, store responses in a database so you can track historical trends in ticket pricing and availability. If you need guidance, our support team provides detailed recommendations for scaling architectures and integrating with cloud environments.

Support and Community

If you run into issues, our documentation is backed by responsive support and an active community of developers. You can reach out to our Sales & Support team for direct help, or join our Discord channel for community discussions, code samples, and real-world use cases. Together, these resources make sure you’re never stuck for long.

How to Build a High-Scale Events Checker with the TicketsData Python SDK

This step-by-step guide shows how to use the official TicketsData Python SDK to fetch real-time ticketing data across Ticketmaster, StubHub, SeatGeek, VividSeats, TickPick, and Gametime—with a single, consistent JSON format. Whether you are building a broker dashboard, a pricing engine, or a monitoring pipeline, the SDK lets you scale safely with concurrency controls and retry logic that work across all supported marketplaces.

Step 1 — Install the SDK

pip install ticketsdata-client

Step 2 — Configure Credentials & Concurrency

Initialize the SDK with your TicketsData credentials. Use concurrency to control how many requests run in parallel (recommended 5–15 on shared plans; higher on dedicated).

from ticketsdata_client import TicketsDataClient
import asyncio

async def main():
    client = TicketsDataClient(
        username="YOUR_EMAIL",
        password="YOUR_PASSWORD",
        concurrency=10,   # run up to 10 requests at once
        timeout=30        # per-request timeout (seconds)
    )

    # Example jobs spanning all marketplaces
    jobs = [
        {"platform": "ticketmaster", "event_url": "https://www.ticketmaster.com/lady-gaga-the-mayhem-ball-boston-massachusetts-03-29-2026/event/01006323DE0B7BE1"},
        {"platform": "stubhub", "event_url": "https://www.stubhub.com/sabrina-carpenter-los-angeles-tickets-11-23-2025/event/157413810/"},
        {"platform": "seatgeek", "event_url": "https://seatgeek.com/sabrina-carpenter-tickets/los-angeles-california-crypto-com-arena-2025-11-23-7-pm/concert/17400739"},
        {"platform": "vividseats", "event_url": "https://www.vividseats.com/sabrina-carpenter-tickets-los-angeles-cryptocom-arena-11-23-2025--concerts-pop/production/5599667"},
        {"platform": "tickpick", "event_url": "https://www.tickpick.com/buy-playboi-carti-tickets-state-farm-arena-ga-12-1-25-7pm/7364698/"},
        {"platform": "gametime", "event_url": "6908d769dfe85fe8ad73cd62", "mode": "all"}
    ]

    results = await client.fetch_many(jobs)
    for r in results:
        print(r)

    await client.close()

asyncio.run(main())

Step 3 — Feed Events from Files or a Database

Keep your job list in JSON (or load from a database) and pass it directly to fetch_many.

# jobs.json
[
  {"platform": "ticketmaster", "event_url": "https://www.ticketmaster.com/lady-gaga-the-mayhem-ball-boston-massachusetts-03-29-2026/event/01006323DE0B7BE1"},
  {"platform": "stubhub", "event_url": "https://www.stubhub.com/sabrina-carpenter-los-angeles-tickets-11-23-2025/event/157413810/"},
  {"platform": "seatgeek", "event_url": "https://seatgeek.com/sabrina-carpenter-tickets/los-angeles-california-crypto-com-arena-2025-11-23-7-pm/concert/17400739"},
  {"platform": "vividseats", "event_url": "https://www.vividseats.com/sabrina-carpenter-tickets-los-angeles-cryptocom-arena-11-23-2025--concerts-pop/production/5599667"},
  {"platform": "tickpick", "event_url": "https://www.tickpick.com/buy-playboi-carti-tickets-state-farm-arena-ga-12-1-25-7pm/7364698/"},
  {"platform": "gametime", "event_url": "6908d769dfe85fe8ad73cd62", "mode": "all"}
]

Step 4 — Throughput, Concurrency & Safe Limits

• Concurrency controls how many requests run at once. On shared (Starter/Pro) plans, soft limits ensure fair use; dedicated plans remove these limits and allocate isolated resources.
• With 10 concurrent requests and average 4–5 s per batch of 6 events, you can process about 35,000–40,000 events per hour, depending on server location and event data size.
• Real-world benchmark: scanning 12,000 Ticketmaster events typically completes in ~1.0–1.2 hours at recommended settings.
• Suggested rate: up to ~5 req/s per marketplace (≈20–25 total req/s across multiple platforms) for steady, low-latency performance.

Best Practices for Scale

• Batch intelligently: group events per marketplace to minimize warm-up latency.
• Monitor usage: read quota_remaining from responses and pace accordingly.
• Cache frequent events: avoid refetching unchanged data within short windows.
• Backoff on spikes: brief sleeps (200–500 ms) between large batches smooths load.
• Store results: persist normalized JSON in your DB for analytics & trending.

By standardizing responses for Ticketmaster, StubHub, SeatGeek, VividSeats, TickPick, and Gametime, the TicketsData SDK eliminates brittle scrapers and CAPTCHA issues, so you can focus on building a stable, high-speed events checker with clean, actionable JSON across all marketplaces.