Track SaaS pricing & plan changes

List your competitors' homepage or pricing URLs, enable auto-discovery of the pricing page, and run a pricing-page scraper that returns plan names, prices, billing options, and features as structured JSON. Schedule recurring runs and turn on change detection so each run compares against the last and flags price moves, new tiers, or feature shifts. This tool builds that query and previews the output shape; you run it live on the Apify actor.

The query builder on this page is free to use: you configure the run, preview the example output shape, and copy a ready-to-run config. Actually executing the scrape happens on the backing Apify actor. New Apify accounts include platform free usage credits to get started, after which it is pay-as-you-go. The actor charges a small fee per run start plus a per-company-analyzed fee, so cost scales with how many competitors you track per run. AI-enhanced extraction, if enabled, may add a small per-result fee on pages where it is needed.

No. This is a query builder. It assembles a ready-to-run configuration and shows a fixed example of the JSON output shape so you know exactly what fields you will get. Live pricing pages are not CORS-open and often need browser rendering, so the actual extraction runs on the backing Apify actor. You take the generated query, run it there, and receive the structured dataset.

For each company it returns structured JSON: companyName, companyUrl, pricingPageUrl, hasFreeTier, hasEnterprisePlan, totalPlansFound, and a plans array. Each plan includes planName, priceMonthly, priceAnnualPerMonth, priceAnnualTotal, pricingModel, currency (ISO 4217), billingPeriods, a features list, featureLimits, ctaText/ctaUrl, and the flags isFree, isEnterprise, isPopular, and trialDays. There is also an optional featureComparison matrix, an extractionMethod and extractionConfidence rating (high, medium, or low), and an errors array. When change detection is on you also get a hasChanges boolean, a changes array, and previousScrapedAt. The fixed example shown on this page is a representative subset of these fields.

Enable detectChanges and persist results to a named dataset. On each scheduled re-run, the actor compares the current extraction against the prior stored snapshot and flags differences: added or removed plans, price increases or decreases, billing changes, and features that moved between tiers. It sets hasChanges to true and lists specifics in a changes array, and can post a webhook alert so your team sees the update without manual checking.

For many SaaS companies, pricing is an ongoing process rather than a once-a-year event. Industry commentary and vendor surveys (which tend to reflect self-selected, higher-growth teams) suggest that most SaaS companies revisit pricing at least annually, with faster-moving teams re-evaluating quarterly, and that larger vendors often build modest annual increases into renewals. Treat those figures as directional rather than precise. Because changes are frequent and rarely announced, weekly-to-monthly monitoring catches packaging and tier shifts you would otherwise miss.

Publicly visible pricing data is generally legal to collect, and courts have repeatedly held that accessing public web data is not inherently illegal. The caveats matter: respect each site's terms of service and robots.txt, do not bypass logins or technical barriers, avoid scraping personal data, and do not overload servers. This tool targets public pricing pages only. For your specific situation and jurisdiction, confirm with your own legal counsel.

Yes. The backing actor accepts a webhook URL and posts a notification whenever a run detects changes, including what changed. You can route that webhook into Slack, email, or your own automation so sales and product teams get pricing alerts in real time with context, instead of manually re-checking competitor sites. Pair this with a recurring schedule to build an ongoing change log.

Common uses include benchmarking your own plans against rivals, building feature-versus-price comparison matrices, populating sales battlecards with current competitor tiers, feeding a pricing model, and maintaining a historical change log that reveals strategy over time. Because the output is clean JSON, you can pipe it into BI tools, Google Sheets, or a database, and reuse the same query on a schedule to keep everything current.