Media, Gaming & Sports
Ticket Price Intelligence API
Two people buying the same seat on the same afternoon can pay very different amounts. Neither of them can easily find out which one was wrong.
- Ticketing marketplaces
- Event discovery apps
- Resale platforms
- Travel and hospitality packagers
- Venue operators
What your customers would see
example interfaceMockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.
The problem this solves
Event ticketing is one of the least transparent consumer markets left. Primary allocations release in tranches, resale inventory appears and vanishes, fees are disclosed late in the flow, and the same seat is listed at several prices across several platforms simultaneously. For a buyer it is guesswork; for a product trying to help them it is a data collection problem with real legal edges.
Price behaviour in this market is also genuinely interesting. Resale prices for a popular event typically spike on announcement, soften through the middle period, then move sharply in the final seventy-two hours in a direction that depends on demand and remaining supply. Those dynamics are learnable from observed listings, but only if somebody has been recording them continuously — historical listing data is not retrievable after the fact.
The uncomfortable part is sourcing. Most ticketing sites prohibit automated collection in their terms, several have exclusive primary arrangements, and resale is regulated differently across jurisdictions. A build here starts with which sources you may actually use, because that determines what the product can honestly offer.
Who buys this
Ticketing marketplaces, Event discovery apps, Resale platforms, Travel and hospitality packagers, Venue operators
What the API returns
What comes back
- Price comparison across permitted sources for the same event and seating category
- Resale price history captured continuously, with listing counts alongside prices
- Availability and inventory-depth signals by category
- Price trend direction with the observation window that produced it
- Fee-inclusive price normalisation so comparisons are not misleading
- Price alerts on threshold or trend conditions
- Event matching across sources despite inconsistent naming and dates
- Source and permission tags on every price point
Endpoints
| Method | Path | Returns |
|---|---|---|
| GET | /v1/events/{id}/prices | Current prices across permitted sources |
| GET | /v1/events/{id}/history | Captured price and listing history |
| GET | /v1/events/{id}/availability | Inventory depth by seating category |
| POST | /v1/events/match | Match an event across sources to one identifier |
| POST | /v1/alerts | Register price or availability alert conditions |
| GET | /v1/trends | Price trend direction across tracked events |
Request and response
illustrativeRequest
{
"event_id": "evt_442190",
"category": "lower_tier",
"include": ["current", "history", "availability"],
"history_days": 60,
"fee_inclusive": true
}Response
{
"event_id": "evt_442190",
"event": { "name": "Example Arena Tour — Manchester",
"date": "2026-12-14T19:30:00Z", "venue_id": "ven_1104" },
"as_of": "2026-10-22T11:30:00Z",
"current": {
"lowest_fee_inclusive": 84.50,
"median_fee_inclusive": 132.00,
"listings_observed": 412,
"sources_queried": 4,
"sources_permitted": 4
},
"history": {
"captured_from": "2026-08-23",
"median_by_week": [168.0, 151.5, 144.0, 139.0, 132.0],
"listing_count_by_week": [96, 188, 274, 361, 412]
},
"availability": {
"lower_tier": "deep", "upper_tier": "deep", "floor_standing": "thin"
},
"trend": { "direction": "softening", "observation_days": 60,
"note": "Listing count rising faster than demand signal" },
"sources": [
{ "source": "primary_authorised_api", "permission": "licensed_api" },
{ "source": "resale_partner_feed", "permission": "partner_agreement" }
]
}Where the data comes from
| Authorised primary APIs | Official ticketing APIs where a partner or affiliate agreement exists — the cleanest and most reliable source |
| Resale partner feeds | Marketplace feeds obtained under agreement rather than collected, which is both more reliable and legally clearer |
| Your own inventory and transactions | If you operate a marketplace, your own listings and sales are the highest-quality signal you have |
| Permitted public listing data | Only where terms of use allow automated collection and redistribution of derived measures — many ticketing sites explicitly do not |
| Event and venue reference data | Seating maps, capacities and canonical event records, needed to match the same event across sources |
| Continuously captured history | Listing snapshots stored as they are observed, because this history cannot be bought or recovered later |
The intelligence layer
The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.
- Event matching across sources using name, date, venue and artist together, with manual review for ambiguous cases — the same event is named four different ways on four platforms
- Fee-inclusive normalisation, since headline prices across sources are not comparable and a comparison that ignores fees is actively misleading
- Continuous capture of listing snapshots with counts, because price without inventory depth tells you very little
- Trend detection that separates genuine softening from a single large listing dump
- Seating category mapping between inconsistent source taxonomies, with unmapped categories reported rather than guessed
- Outlier handling for speculative and mispriced listings, which are common and distort a naive median
- Backtesting of trend signals against what prices actually did in the final days before comparable events
- Source health monitoring, since a blocked or changed source looks identical to a market with no inventory
What it changes for the business
Buyer confidence
A defensible price picture reduces the abandonment that comes from suspecting you are being overcharged.
Alert-driven return visits
Price and availability alerts give users a reason to come back that does not depend on a campaign.
Packaging decisions
Travel and hospitality packagers can price around observed ticket behaviour rather than a guess.
Inventory and pricing insight
Marketplaces and venues see how their own pricing sits against the wider observed market.
How it fits into your product
How it embeds
- REST endpoints for comparison and history, suitable for direct rendering on an event page
- Alert registration with webhook delivery into your existing notification system
- Event matching endpoint so your catalogue identifiers map to the tracked record
- Source permission tags surfaced in the response so your front end can respect attribution and display terms
- Scheduled exports into your warehouse for pricing and commercial analysis
- Capture starting during the prototype, since price history has to be accumulated going forward
Included in every build
- Authentication, scoped API keys and per-customer rate limiting
- Input validation with predictable, documented error responses
- Versioned endpoints so your integration never breaks without notice
- Request and response logging for audit and dispute resolution
- Latency, error-rate and usage monitoring with alerting
- OpenAPI specification, sandbox environment and client examples
- Backups, failover design and a documented recovery procedure
- An agreed support arrangement once the service is live
How we would build it
about 12 weeksDiscovery and data review
Weeks 1–2
We agree the exact question the API answers, review the sources you already hold, check what has to be licensed, and write down the response contract your developers will code against.
Architecture and prototype
Weeks 3–4
You get a written architecture plus a working endpoint on real data — narrow, but genuinely callable — so integration can be tested against something concrete rather than a document.
Production build
Weeks 5–10
Pipelines, models, storage, authentication, rate limiting, logging, tests and documentation. Security and observability are part of this phase, not a later one.
Integration and handover
Weeks 11–12
We support your engineers through their integration, run load tests at your expected volume, and agree how the service is monitored and supported once it is live.
Commercial models
There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.
Fixed-scope prototype
A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.
Fixed-price build
An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.
Build then retainer
We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.
White-label product
The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.
Questions buyers ask
Can you collect prices from any ticketing site?
No, and this is the central constraint of the build. Most major ticketing platforms prohibit automated collection in their terms, and several have exclusive primary arrangements. We would map which sources are available to you through an API, an affiliate or partner agreement, or terms that genuinely permit collection — and design the product around that set. A comparison product built on sources you are not permitted to use is a liability, not a feature.
How much history do we get on day one?
Very little. Listing history is not retrievable retrospectively — nobody sells you the state of a marketplace last March. Capture has to run forward, so we would start it during the prototype. Expect a few months before trend signals are worth showing to users, and we would rather say that than fabricate a backfill.
Can it predict whether a price will fall?
It reports observed trend direction with the window behind it. Genuine forecasting is possible for events with comparable history, but accuracy varies enormously by event type — a touring act with twenty comparable dates is tractable, a one-off is not. We would validate on held-out events and tell you honestly which categories support a forecast.
Is resale price tracking legal everywhere?
Reporting observed prices is generally fine. Resale itself is regulated differently across jurisdictions, with price caps and restrictions in some markets, and a product that surfaces resale listings may inherit obligations in those territories. That is a question for your legal team, and we would build to whatever territory rules they set.
What if a source blocks us?
It happens, and the API reports it rather than silently returning fewer listings. Source health monitoring exists precisely because a blocked source and an empty market look identical in the data, and the second interpretation would mislead your users.
Related blueprints
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Want this built for your platform?
Tell us what your product does today and what you want another system to be able to ask it. We will come back with an approach, a scope and a price.