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A flight price tracker is a scheduled search system: it saves a traveler’s exact route and date rules, requests fares from a provider, records each observation, and sends an alert when a clearly defined price condition is met. The hard parts are not just making the request—they are keeping searches comparable, distinguishing live offers from indicative prices, avoiding duplicate alerts, and showing when a fare was observed.
This guide builds the data and alerting foundation and explains how to connect it to a documented flight-search provider. Google Flights already tracks routes and flights for consumers; a custom tracker makes sense when you need your own thresholds, price history, flexible-date scans, or notification channels.
Decide what a watch means before you write code
A tracker can only compare prices meaningfully if every observation represents the same search. Define a watch as a complete query, not merely an origin and destination. Save the user’s choices in a durable record and preserve them when you submit later searches.
- Route: origin and destination airport codes or the provider’s entity IDs. Store the provider and identifier type; do not assume every provider resolves locations identically.
- Trip shape: one-way, return, or multi-city legs, with departure and return dates where applicable.
- Date rule: exact dates or a flexible-date rule. A flexible scan should retain the date attached to every result instead of collapsing distinct itineraries into one price.
- Travelers and fare scope: passenger count, cabin, stop limit, and preferred or excluded carriers.
- Market settings: market, locale, and currency. Send these with every request and keep them with each observation; the same trip can return different displayed prices under different settings.
- Alert behavior: threshold, comparison baseline, cooldown, and delivery channel.
Keep a stable itinerary signature derived from the route, dates, passenger count, cabin, stop rule, and other filters that affect the result. This lets the system distinguish a changed price for the same itinerary from a different flight that happens to share the route.
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Choose a provider and understand its price semantics
For a live workflow, Skyscanner’s Flights Live Prices API uses a /create request followed by /poll. Its quick-start requires market, locale, currency, query legs, and adult count. Skyscanner says prices are retrieved in real time from airline and inventory partners for each search request. That describes retrieval at search time; it is not a promise that a fare remains available until a user books.
Skyscanner also distinguishes live searches from indicative prices. Its documentation says indicative prices may be cached for up to four days and can differ from live prices. Use indicative data to explore calendars or identify dates worth checking, but label it as indicative and run a live search before alerting a traveler or sending them to book.
When evaluating providers, compare the workflow and the meaning of a result—not just the number of routes in a marketing claim. Check whether you must create and poll a session, how selected itineraries are refreshed, what flexible-date and multi-city searches are supported, and how authentication, rate limits, latency, retries, retention, and booking links work. Duffel describes its results as offers purchasable from an airline at a particular price, which is a different price semantic from an indicative observation. Verify current documentation and access terms before implementing a provider; access to an API or commercial handoff may require partner approval.
Store observations as history, not a single current price
Retaining only the latest result makes it impossible to explain whether a fare is genuinely lower than before, to deduplicate alerts reliably, or to investigate a surprising change. Save each observation with its query context and timestamp. Keep both a normalized row for comparisons and the raw provider response for debugging.
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| Record | Useful fields | Why it matters |
|---|---|---|
| Watch | Watch ID, route and legs, date rule, passenger count, cabin, filters, market, locale, currency, threshold, notification destination, enabled status | Reconstructs the exact search and its alert rule. |
| Observation | Watch ID, itinerary signature, observed-at time, provider, currency, total price, price type (live or indicative), and raw response | Preserves comparable history and evidence for troubleshooting. |
| Alert delivery | Watch ID, itinerary signature, triggering observation, sent-at time, channel, and delivery status | Supports cooldowns, deduplication, and delivery diagnosis. |
Store times in a consistent format, such as UTC timestamps, and render them in the traveler’s chosen locale. Never compare totals across currencies without an explicit conversion policy; if you convert, retain the original amount, currency, and conversion context rather than overwriting the observed fare.
Build and run a small alert-history foundation
The Python example below is a runnable local demonstration of the persistence and alert-decision layer. It uses SQLite, accepts an observation, stores it, and decides whether a threshold alert is due. It deliberately does not pretend to call an airline search API: connect the provider adapter only after implementing the exact request, authentication, polling, and response parsing from that provider’s current documentation.
- Save the code as
tracker.py. It needs Python 3 and the standard library only. - Run
python tracker.py. The script createsflights.db, inserts one sample live observation, and prints the alert decision. - Replace the sample watch and observation with normalized results from your provider adapter. Do not feed indicative calendar results into a live-price alert without marking them.
import json
import sqlite3
from datetime import datetime, timezone
DB = "flights.db"
SCHEMA = """
CREATE TABLE IF NOT EXISTS watches (
id TEXT PRIMARY KEY,
itinerary_signature TEXT NOT NULL,
currency TEXT NOT NULL,
threshold REAL NOT NULL,
cooldown_seconds INTEGER NOT NULL DEFAULT 86400
);
CREATE TABLE IF NOT EXISTS observations (
id INTEGER PRIMARY KEY AUTOINCREMENT,
watch_id TEXT NOT NULL,
itinerary_signature TEXT NOT NULL,
observed_at TEXT NOT NULL,
provider TEXT NOT NULL,
currency TEXT NOT NULL,
total REAL NOT NULL,
price_type TEXT NOT NULL,
raw_json TEXT NOT NULL
);
CREATE TABLE IF NOT EXISTS alert_deliveries (
id INTEGER PRIMARY KEY AUTOINCREMENT,
watch_id TEXT NOT NULL,
itinerary_signature TEXT NOT NULL,
observation_id INTEGER NOT NULL,
sent_at TEXT NOT NULL
);
"""
def utc_now():
return datetime.now(timezone.utc)
def record_and_decide(watch, observation, db=DB):
now = utc_now()
with sqlite3.connect(db) as con:
con.executescript(SCHEMA)
con.execute("""INSERT OR REPLACE INTO watches
(id, itinerary_signature, currency, threshold, cooldown_seconds)
VALUES (?, ?, ?, ?, ?)""", (
watch["id"], watch["itinerary_signature"], watch["currency"],
watch["threshold"], watch.get("cooldown_seconds", 86400)))
cur = con.execute("""INSERT INTO observations
(watch_id, itinerary_signature, observed_at, provider, currency,
total, price_type, raw_json) VALUES (?, ?, ?, ?, ?, ?, ?, ?)""", (
watch["id"], observation["itinerary_signature"],
now.isoformat(), observation["provider"], observation["currency"],
observation["total"], observation["price_type"],
json.dumps(observation.get("raw", {}))))
observation_id = cur.lastrowid
eligible = (observation["price_type"] == "live"
and observation["currency"] == watch["currency"]
and observation["itinerary_signature"] == watch["itinerary_signature"]
and observation["total"] <= watch["threshold"])
previous = con.execute("""SELECT sent_at FROM alert_deliveries
WHERE watch_id=? AND itinerary_signature=?
ORDER BY id DESC LIMIT 1""", (
watch["id"], observation["itinerary_signature"])).fetchone()
cooled_down = True
if previous:
last = datetime.fromisoformat(previous[0])
cooled_down = (now - last).total_seconds() >= watch.get("cooldown_seconds", 86400)
should_alert = eligible and cooled_down
if should_alert:
con.execute("""INSERT INTO alert_deliveries
(watch_id, itinerary_signature, observation_id, sent_at)
VALUES (?, ?, ?, ?)""", (
watch["id"], observation["itinerary_signature"],
observation_id, now.isoformat()))
return {"observation_id": observation_id, "should_alert": should_alert,
"observed_at": now.isoformat()}
if __name__ == "__main__":
watch = {"id": "watch-1", "itinerary_signature": "JFK-LHR-2027-04-10",
"currency": "USD", "threshold": 450, "cooldown_seconds": 86400}
sample = {"provider": "demo", "itinerary_signature": watch["itinerary_signature"],
"currency": "USD", "total": 439.50, "price_type": "live",
"raw": {"note": "Replace with the provider response"}}
print(record_and_decide(watch, sample))For production, make observation insertion and alert reservation atomic, add foreign-key constraints and indexes, and send notifications through a durable queue. The example inserts an alert-delivery record when an alert is due; a real delivery worker should update delivery status and retry transient failures without generating duplicate messages.
Connect the provider workflow without losing control of polling
Implement provider access behind a small adapter that takes a normalized watch and returns normalized observations. The rest of the tracker should not need to know whether a provider used a synchronous search or a session.
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- Validate the watch. Resolve locations to the provider’s accepted identifiers, confirm trip legs and dates are valid, and reject unsupported combinations before spending a search request.
- Create a search session. For Skyscanner Live Prices, submit the documented create request with the full query, including market, locale, currency, legs, and traveler count. Keep the session identifier and original query together.
- Poll the session. Poll according to the provider’s documented rules, with a maximum wait and bounded retries. Apply exponential backoff to transient errors, cap concurrent sessions, and honor rate limits and usage guidelines. Do not retry authentication or malformed-query errors as if they were temporary.
- Normalize results. Extract total price, currency, itinerary details, carrier, stops, baggage information when supplied, and the provider’s price semantics. Keep unrecognized response fields in the raw payload.
- Refresh a selected result. Skyscanner documents a refresh-prices session because earlier results may be cached or older. Refresh the selected itinerary immediately before presenting a booking handoff.
- Record before notifying. Persist the observation and apply the alert rule. If notification delivery fails, retry delivery from the stored event rather than repeating the fare search unnecessarily.
Because provider request schemas, authentication details, access controls, and limits can change, use the provider’s current API documentation for exact paths, headers, fields, and polling intervals. The available provider description establishes the create/poll pattern, but not a complete endpoint recipe that can safely be copied into a production client.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose alert rules users can understand
Prefer an explicit rule over an opaque “deal” score. Common choices are an absolute ceiling, such as “notify below $450,” a percentage decline from a user-selected baseline, or a new low for a route. A percentage-drop rule should say which baseline it uses—such as the last comparable live observation—so the same data does not yield surprising results.
Add a cooldown and deduplicate on both watch and itinerary signature. A single qualifying fare can appear in multiple polls; without deduplication it can produce a burst of identical notifications. Decide whether a new alert is allowed after the cooldown only if the price is still below the threshold, or only after a further price change, and describe that behavior in the product UI.
Include enough context for a traveler to judge the alert: route, dates, carrier, stops, total, currency, baggage assumptions if known, observation time, and a booking or comparison link. Make uncertainty visible. Fares and inventory can change between the search and checkout, so instruct users to confirm the final price and terms with the airline or agency.
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- Schedule responsibly. Stagger watches rather than launching every search at once. Bound concurrency and retries, and pause or reduce frequency when the provider returns rate-limit or service errors.
- Separate exploration from alerting. Use indicative data for broad flexible-date discovery if appropriate, but label it. Trigger purchase-oriented alerts from a live search and refresh before handoff.
- Preserve audit context. Save exact query parameters, timestamps, provider, currency, and response payload. This makes price changes explainable and helps identify a changed search configuration.
- Control retention. Raw responses can be large and may include data you do not need indefinitely. Set a retention policy and avoid storing secrets in query logs or raw payloads.
- Measure the real workload. Estimate searches from the number of active watches, check frequency, and flexible-date expansion. Polling sessions and retries add requests beyond the initial watch count. Use provider quotas and current commercial terms to estimate cost; no universal per-search price can be assumed here.
- Design graceful failure. A provider outage should leave the watch due for a later retry, not mark it as having no fare. Show stale data as stale and never present a failed search as a price increase or a deal disappearance.
When to use an existing tracker or a small computer
Google Flights already lets users track a route, specific dates, or a specific flight and receive email when prices change significantly. Its help documentation also describes warnings that prices are likely to rise or that a current fare is expiring. For a personal watch with standard email alerts, that may be simpler than operating a custom service. A custom build is justified when you need your own thresholds, push or webhook delivery, full observation history, flexible-date scanning, team or family watches, or user-controlled storage.
A Raspberry Pi 5 can host a hobby, home, or small-office tracker: Raspberry Pi describes it as a Linux computer with dual-band Wi-Fi, Gigabit Ethernet, USB-C power, and an RTC connector, with production planned until at least January 2036. For a continuously running service, plan for reliable storage, cooling, and backup power; those are practical engineering considerations, not a guarantee of uninterrupted operation. A hosted database and scheduler may be easier if the tracker must notify users while your home network or power is unavailable.
Or skip the browser setup
A screenshot is useful for keeping a visual record of a flight-results page, but it is not a fare-search API and does not replace the provider workflow above. If you want a capture of a page after opening a flight search, ScreenshotNeo takes a URL and returns an image or PDF. Its [API documentation] describes the available request options. For example, this one-call cURL request captures a URL:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://www.google.com/travel/flights -o shot.webpScreenshotNeo removes cookie banners, newsletter popups, and chat widgets before capture; bot checks, blank pages, and failed loads are never billed. Its MCP server lets AI agents take screenshots, and the free plan includes 1,000 screenshots a month with no card; paid plans start at $5 for 3,000. A screenshot can document what a page looked like, but it does not make a displayed fare live or confirm that it can still be booked. Sign up for 1,000 free screenshots a month, with no card required.
Frequently Asked Questions
Can a flight price tracker guarantee that an alerted fare will still be available?
No. An alert reports an observation, not a reservation. The fare and inventory can change before checkout, so the traveler must confirm the current total with the airline or agency.
Can I use a screenshot instead of a flight prices API?
No. A screenshot records a page visually; it does not provide normalized, comparable live fare observations or replace a documented provider search.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
