Market data that keeps arriving,
even after the site you watch changes its layout
Scrapers break the moment a target site redesigns, and badly built ones get the collecting account banned before that. We build with rate limits tuned to each source and alerts for structural changes, so the data keeps flowing instead of silently stopping.
What it is and who needs it
A scraping and market data product collects competitor prices, listings, or market signals from external sources and turns them into structured data your team can act on, instead of someone manually checking a competitor’s site every few days. It fits any business where pricing or product decisions depend on what is happening in the market right now. It is not worth the risk for a one-off check; the value is in ongoing, reliable collection that respects the source enough to keep running for months, not days.
What is inside
Collection is tuned specifically to each source’s actual rate limits and behavior, since the goal is staying online reliably, not extracting data as fast as technically possible and risking a ban. Output gets normalized into one consistent structure regardless of how differently each source presents its data, so your team works with one clean dataset instead of several incompatible raw dumps. Change detection watches for a source’s layout shifting, which breaks most scrapers silently, and alerts your team immediately so collection gets fixed before a data gap grows unnoticed. Deduplication and noise filtering mean the final dataset reflects real changes, not scraping artifacts or duplicate listings counted twice.
How we build it
We study each source’s actual structure and rate-limiting behavior before writing a line of collection code, since respecting a source’s limits is both an ethical requirement and the only way collection keeps running long-term. Collection gets built with deliberate pacing and backoff, never the aggressive polling that gets an account or IP banned within days. Change detection and alerting are tested by simulating a source’s layout changing, confirming the system flags it rather than silently returning empty or garbage data. We launch watching the sources that matter most first, proving reliability over a few weeks before expanding coverage.
What to watch
The real risk is not technical, it is relationship risk with the source: collection that is too aggressive gets an IP or account banned, which is a much bigger setback than a single missed data point would have been. This is why pacing and backoff are non-negotiable, even when faster collection is technically possible. The second risk is a silent layout change producing garbage data that looks structurally valid but is actually wrong, caught only by deliberate change detection, not by the absence of an error. Treat any source as something that can change its terms or structure at any time, and build for that reality from day one. Keep a fallback manual-check process for the sources that matter most, so a prolonged collection gap from a source change never becomes a complete blind spot while a fix is in progress.
Timeline and price
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| MVP | from $1,500 | One to two sources, structured output, change-detection alerts | 3 to 4 weeks |
| Production | from $4,000 | Several sources, deduplication, scheduling tuned per source, dashboard | 5 to 7 weeks |
| Full control (handover-ready) | from $6,800 | Everything in Production, plus a full handover package: architecture docs, test suite, admin access audit, and a walkthrough so your own team or another vendor can run it without us | 7 to 8 weeks |
Running cost on top of the build is usually $15 to $50 a month in hosting and proxy costs, depending on source count and polling frequency.
What you own at the end
You own the collected data, the collection scripts, the normalization logic and the full source code, running on your own infrastructure. The handover package documents each source’s specific rate limits and quirks, so your own team understands exactly what the system respects and why.
Related
Pairs with AI pricing engine and AI monitoring and alerting product, both of which often run on top of data this kind of collection provides. See the analytics service page and the e-commerce service page for marketplace-specific automation built on this foundation. Real builds: the digital goods marketplace automation case study and the game servers market research case study, built from 113 sources by a team of research agents. Checking a competitor’s prices by hand every week? Get in touch.
FAQ
How much does a scraping and market data product cost?
From $1,500 for watching one or two sources (a competitor site, a marketplace category) with structured output and change alerts. Monitoring several sources with deduplication and a dashboard runs $4,000 to $6,800.
How long does it take?
Three to four weeks for one or two sources. Each additional source extends the timeline somewhat, since every source has its own structure, rate limits and quirks that need individual tuning.
What is the stack?
Python for collection, with respect for each source's rate limits and terms, PostgreSQL for storing structured results, and alerting through Telegram or email when a source's structure changes and collection needs attention.
Who owns the data and the collection system?
You. The collected data, the collection scripts and the code are yours, running on your own infrastructure, not a scraping SaaS charging per request with its own terms on your data.
How do you avoid getting the collecting account or IP banned?
Rate limits are tuned specifically to each source, with pauses and backoff rather than aggressive polling, following the external API throttling discipline we apply everywhere we touch a third-party system. No retry storms, no hammering a source that returns an error.