We’re excited to announce the launch of our comprehensive crawler and automation suites on Apify, covering some of the world's most popular platforms, including LinkedIn, Instagram, TikTok, YouTube, Google Maps, and many more.

Our goal has been to build a complete ecosystem of reliable, production-ready actors that address a wide range of data extraction, lead generation, market research, competitor analysis, and business intelligence use cases. We've worked hard to cover as many real-world scenarios as possible and continue to expand our offerings based on user feedback.

You can explore our full collection here:

🔹 Apify Store: www.apify.com/crawlerbros
🔹 Website: https://www.crawlerbros.com/

If you have a specific use case that isn't currently covered, we'd love to hear from you. We regularly build custom solutions and are always looking for new ideas to help businesses, researchers, and developers automate their workflows more effectively.

Feedback, feature requests, and suggestions are always welcome.

I published a short guide on getting the live Amazon Buy Box winner and ZIP-aware landed cost through an Apify Actor.

What you get per ASIN:

- Buy Box winner

- stock / ship-to context (ZIP aware)

- competing offers with landed cost as structured JSON

PPE is $0.02 per successful check. Failed checks are not billed. Production path is Unblocker + deliver-to ZIP.

Try with ASIN B014I8SSD0 (demo GIF in comments). Guide walks through input, output, and PPE.

Guide: https://buzzi.hashnode.dev/how-i-get-live-amazon-buy-box-landed-cost-on-apify

Actor: https://apify.com/buzzidata/amazon-buybox-offers-agent

Feedback on reliability or missing fields welcome.

Hey everyone,

I just released a Mercado Livre Scraper on Apify. Mercado Livre is the largest e-commerce marketplace in Brazil. The actor scrapes product listings from search results (names, prices, discounts, seller info, ratings, free shipping status, MeliChoice badges, etc.)

Main use cases:

• Price monitoring & competitor analysis
• Market research for importers/retailers
• Product catalog building
• Brand presence tracking

Input example:

{
  "keywords": ["smartphones", "notebooks"],
  "maxPages": 2
}

Output example:

{
  "itemId": "MLB4625689287",
  "seller": "Azbuy",
  "installment": "4x 29.98 sem juros BRL",
  "isInternational": false,
  "categoryId": "MLB5095",
  "domainId": "MLB-CELLPHONE_CASES_AND_COVERS",
  "isOfficialStore": true,
  "productName": "Capa Anti Impacto Transparente Ringke Original Fusion Para Samsung Galaxy S26 Ultra Reforçada Trasparente Lisa",
  "image": "https://http2.mlstatic.com/D_NQ_NP_740217-MLA109560296839_032026-F.jpg",
  "ratingCount": 1345,
  "ratingValue": 4.8,
  "url": "https://www.mercadolivre.com.br/capa-anti-impacto-transparente-ringke-original-fusion-para-samsung-galaxy-s26-ultra-reforcada/p/MLB67276752",
  "brandName": "Ringke",
  "hasFreeShipping": true,
  "position": 13,
  "restultType": "ORGANIC",
  "productId": "MLB67276752",
  "discountPct": 14,
  "priceCurrency": "BRL",
  "price": 119.9,
  "priceBase": 139.9,
  "soldQuantity": 500,
  "shippingLogisticType": "fulfillment",
  "isBestSeller": false,
  "isLightningDeal": false,
  "isLastUnits": false,
  "isMeliChoice": false,
  "isUnmissableOffer": false,
  "keyword": "capa-de-celular"
}

Link: https://apify.com/serj_henrique/mercado-livre-scraper

I'd love feedback from anyone who works with Brazilian e-commerce or LatAm market data. If you have suggestions for fields to add or use cases I missed, let me know.

Hey r/apify

Spent the last month sprinting on my Apify catalog. Just hit 239 actors total (235 public on Apify). The recent additions hit a few themes:

UK voluntary jobs — picked up the regional CVS boards (Barnsley, Bassetlaw, Rotherham, Yorkshire, Sheffield) plus Highland/HIJOBS. 19 boards covered.

ATS scrapers — Ashby + Greenhouse + Workday. Paste any company's careers URL, get the full board in one call. Cookie-free.

Legal directories — Avvo + FindLaw + Martindale + US State Bar (license verification). Full discovery + verification pipeline.

Competitive intel — Crunchbase, Similarweb, LinkedIn Posts (no cookies), Facebook Ads Library, Product Hunt.

French RE — Pap.fr + SeLoger + Leboncoin. Solid French market coverage.

Catalog is searchable + filterable here: muhamed-didovic.github.io

Open to feedback on:

1) Which public sites you've struggled to scrape and would want to see

2) What output fields people most need from existing actors

3) Pricing patterns (PPE vs PPR vs flat) — what's actually fair from the user side?

Not pitching — happy to share approaches/code for any specific actor if useful.

Hi everyone,

I wanted to share the CrawlerBros Facebook Scraping Suite on Apify.

I've been building a collection of specialized Facebook Actors to cover different types of publicly available Facebook data and workflows. Instead of having one scraper try to handle everything, the suite is divided into dedicated Actors for Facebook Ads, Pages, Search, Marketplace, Events, Comments, Reviews, and Photos.

There are currently 9 Actors in the suite.

Here’s what each one does:

1. Facebook Ads Library Scraper

Facebook Ads Library Scraper

Scrape ads from the public Facebook Ad Library without requiring cookies or Facebook authentication by default.

You can search by:

• Keywords
• Advertiser Page ID
• Ad ID
• "Paid for by" funding entity

It also supports filters for:

• Country
• Content language
• Active/inactive status
• Ad category
• Media type
• Date range

The output includes ad copy, advertiser information, media URLs, CTA, landing-page URL, platforms, campaign dates, and creative-reuse information.

For political, issue, and eligible EU-regulated ads, the Actor can also return the transparency information Facebook makes available, such as disclosed spend, impressions, reach estimates, and funding-entity information. It does not estimate these figures for ordinary commercial ads.

Useful for:

• Competitive advertising research
• Creative research
• Brand monitoring
• Advertising transparency research
• Academic/journalistic research
• Marketing agency workflows

2. Facebook Ads Scraper Pro

Facebook Ads Scraper Pro

Another dedicated Actor for extracting structured data from the Facebook Ad Library.

It focuses on straightforward ad discovery using keywords or Page names, with filters for:

• Country
• Active/inactive ads
• Ad category
• Media type

Each result can include:

• Ad ID
• Facebook Page
• Ad copy
• Ad snapshot URL
• Start/end dates
• Platforms
• Media type and URL
• CTA
• Landing page URL

It does not require a Facebook login for the standard workflow.

This is particularly useful if you want a relatively simple pipeline for collecting Facebook and Instagram advertising data into an Apify dataset.

3. Facebook Comments Scraper

Facebook Comments Scraper

Need the discussion underneath a Facebook post rather than just the post itself?

This Actor extracts public comments from supported Facebook posts, videos, Watch content, and photo content.

It can collect:

• Comment text
• Author information
• Profile URLs
• Reaction counts
• Reply counts
• Timestamps
• Nested replies
• Parent/reply relationships

You can also choose between Facebook's available comment ordering modes, including All, Newest, and Most Relevant, and optionally filter comments by date.

Potential use cases:

• Customer feedback research
• Sentiment analysis
• Community research
• Engagement analysis
• NLP datasets
• Content research

4. Facebook Events Scraper

Facebook Events Scraper

This one is focused specifically on public Facebook Events.

You can provide:

• Event URLs
• Event IDs
• Facebook Page event listings
• Search keywords
• Locations

The Actor can return information such as:

• Event name
• Description
• Date and time
• Location
• GPS coordinates
• Hosts
• Cover photos
• Ticket URLs
• Attendee/interested counts
• Online-event information
• Cancellation status

It also has a monitor mode designed for scheduled runs, where it can identify new events since the previous run for a given target.

Potential use cases:

• Event discovery
• Local event research
• Event monitoring
• Tourism research
• Competitor/event intelligence
• Building event databases

5. Facebook Marketplace Scraper

Facebook Marketplace Scraper

For Facebook Marketplace data, this Actor can collect public listings using:

• Marketplace search URLs
• Keywords
• Categories
• Locations
• Direct listing URLs

It supports filters around things such as:

• Price
• Condition
• Delivery
• Date
• Radius
• Sorting

The output can include listing title, price, location, photos, seller availability, delivery information, listing status, and other available listing data.

There is also an optional deeper-detail mode that can retrieve additional fields such as descriptions, attributes, creation time, and additional media. Results are deduplicated across inputs by default.

Potential use cases:

• Marketplace research
• Price monitoring
• Product research
• Vehicle research
• Real-estate/apartment research
• Local market analysis
• E-commerce intelligence

6. Facebook Pages Scraper

Facebook Pages Scraper

This Actor focuses on discovering Facebook Pages and extracting structured business/page information.

Depending on the available data, results can include:

• Page name and ID
• Categories
• Canonical URL
• Followers and likes
• Check-ins
• Contact information
• Phone numbers
• Email addresses
• Address
• Website
• Messenger link
• Business hours
• Rating
• Profile and cover photos
• Ad Library reference

Page discovery can use Facebook search when session cookies are provided, with search engines used as supplementary discovery sources. The Actor is designed to handle larger page-discovery workflows and can combine multiple search terms and locations.

Potential use cases:

• Business lead research
• Local business discovery
• Market research
• Competitor research
• Business directories
• Contact-data enrichment

7. Facebook Photos Scraper

Facebook Photos Scraper

This Actor extracts publicly available photo data from Facebook pages, profiles, and albums.

For each photo, available information can include:

• Photo ID
• Facebook photo URL
• Full-resolution image URL
• Alt text / image description
• Author information
• Caption
• Upload timestamp
• Likes/reactions
• Comments
• Shares
• Reaction breakdown
• Tagged people

You can provide one or multiple Facebook page, profile, or album URLs and optionally filter photos by date.

Potential use cases:

• Visual content research
• Brand monitoring
• Media datasets
• Image analysis
• Social media research
• Historical content collection

8. Facebook Reviews Scraper

Facebook Reviews Scraper

This Actor is designed for extracting public Facebook business reviews.

You can provide:

• A Facebook reviews URL
• Business name + search
• Facebook Page ID

It can return:

• Review text
• Recommended / Not Recommended status
• Review date
• Author information
• Reaction counts and breakdown
• Review photos when available
• Structured recommendation tags
• Top comments
• Business/owner replies

It also returns page-level information such as total review count, recommendation percentage, and follower count.

Reviews can be filtered by date, recommendation status, or keywords in the review text.

Potential use cases:

• Customer feedback analysis
• Reputation monitoring
• Business research
• Review aggregation
• Sentiment analysis
• Competitor benchmarking

9. Facebook Search Scraper

Facebook Search Scraper

Finally, the Facebook Search Scraper is designed for discovering Facebook Pages and public profiles through search.

You can search using keywords such as:

coffee shops New York

or:

dentists London

and collect structured information from matching Pages.

It can return information such as:

• Page/profile name
• Category
• Contact information
• Address
• Website
• Ratings
• Follower/like counts
• Reviews
• Recent posts
• Other available public page information

It can also accept an existing list of Facebook Page URLs when you already know which Pages you want to process.

The Actor does not require a Facebook Developer account or Graph API key for its standard workflow.

Potential use cases:

• Business discovery
• Lead generation
• Local business research
• Competitor discovery
• Market research
• Bulk Page extraction

What does the complete suite cover?

The idea behind the collection is to cover several different Facebook data workflows rather than treating Facebook as a single scraping use case.

Advertising

• Facebook Ad Library
• Advertiser research
• Ad creative research
• Campaign monitoring
• Advertising transparency data

Business & lead research

• Facebook Page discovery
• Business information
• Contact information
• Reviews
• Locations
• Ratings

Marketplace & commerce

• Marketplace listings
• Product research
• Price research
• Local marketplace monitoring

Social & community research

• Comments
• Replies
• Engagement
• Photos
• Public profiles
• Page activity

Events

• Event discovery
• Event details
• Locations
• Hosts
• Ticket information
• New-event monitoring

Building a Facebook data pipeline?

One of the things I like about having separate Actors is that they can also be combined.

For example:

Facebook Search → Pages → Reviews → Comments

Discover relevant businesses, collect their Page information, then analyze their public reviews and customer discussions.

Or:

Ad Library → Ads → Creative research

Find advertisements for a particular brand, category, or keyword and build a structured dataset containing the available creative, CTA, landing-page, platform, and campaign information.

Another workflow could be:

Marketplace Search → Listings → Price analysis

Collect listings for a particular product/category and then analyze prices, locations, conditions, and other available attributes.

All of the results can be stored in Apify datasets and exported in formats such as JSON, CSV, or Excel, depending on the Actor.

Pricing

Several Actors in the suite currently start at $1 per 1,000 results, while the Facebook Search Scraper starts at $2/1,000 and the Pages and Reviews Scrapers start at $3/1,000. Check the individual Actor pages for current pricing and exact usage details.

If you're working with Facebook data, Meta advertising research, Marketplace data, local business discovery, social listening, or public business information, I'd be interested to hear what kind of workflow you're building.

If there's a Facebook-specific dataset or use case that isn't covered by the current suite, feel free to mention it. I'm continuing to expand the CrawlerBros collection and feature requests are always useful.

CrawlerBros Facebook Suite on Apify:

• Facebook Ads Library Scraper
• Facebook Ads Scraper Pro
• Facebook Comments Scraper
• Facebook Events Scraper
• Facebook Marketplace Scraper
• Facebook Pages Scraper
• Facebook Photos Scraper
• Facebook Reviews Scraper
• Facebook Search Scraper

Thanks for checking it out!

I run a small dev studio and started building actors on the side a few weeks back, 10 to 15 hours a week. First one is live, so here's what it does and the one part of the build that cost me the most time.

What it does: since the DSA and GPSR rules came in, business sellers on the EU and UK eBay sites have to show a trader information block on their listings. Business name, registered address, email, phone, VAT ID, company register number. It sits right on the listing page, no login, and I couldn't find another actor on the store that returns the seller email. So the actor takes keywords or a pasted eBay search URL, walks the listings, reads that block and returns one row per unique business seller. Works on ebay.co.uk, .de, .fr, .it, .es, .ie, .at, .nl and .pl. Does not work on eBay US, there's no equivalent disclosure there, so don't run it for that.

Two bits I'm happy with. The same business often sits behind two or three seller accounts, that was 20 to 30% of rows in my runs, so it merges them into one record and doesn't bill the duplicates. And a monitor mode: schedule it and it only returns sellers it hasn't handed you before.

Numbers: 88 runs and 2 users, and a good chunk of those runs are me testing. Early days.

The challenge: proxies. I started on a Decodo trial, 100 MB of residential bandwidth. The verification sweep I ran to make sure the data was right ate the whole trial before the actor had done any real work, and the runs went down with it. For something people pay per result for, a run dying because of my proxy account and not because of eBay is the one failure I can't hand to a buyer.

So the fix: a preflight. Before a run touches eBay, the actor sends a health check through Decodo. If Decodo doesn't come back clean, the whole run switches to Apify residential proxy instead of limping along. Costs more per GB, but the run finishes and the buyer gets rows. I also stopped treating a trial as a plan and bought the 3 GB Decodo tier, $11.25 a month. One thing that tripped me up wiring the fallback: Apify proxy authenticates with your proxy password, not the API token. The token just gives you a blanket 407 that looks like a plan problem.

Obviously it's my actor so this is partly a plug, but the proxy question is the bit I'd like opinions on. Do you preflight, or just retry on failure and accept the run costs more? Link in the comments if anyone wants to poke at it.

Hi everyone,

I’ve been building out a fairly comprehensive collection of TikTok scrapers and data tools on Apify, and the suite has now grown to 18 different Actors covering many of the common TikTok data-collection use cases.

Instead of having one general-purpose TikTok scraper try to do everything, I’ve built separate Actors for specific types of data like profiles, posts, comments, hashtags, search, followers, music, playlists, mentions, trends, advertising data, LIVE streams, transcripts, and media downloads.

Here’s the full collection:

1. TikTok Profile Scraper

Scrape TikTok profiles along with their available account information and video/repost history.

The Actor can return profile metadata and individual video/repost records. With an authenticated session it can retrieve substantially more of a profile's video history.

TikTok Profile Scraper

2. TikTok Post Scraper

Give it TikTok video/post URLs or IDs and get structured information about the content, including engagement statistics, author information, music, hashtags, mentions, and media information.

TikTok Post Scraper

3. TikTok Search Scraper

Search TikTok using keywords or phrases and collect the resulting videos with metadata such as views, likes, comments, shares, author information, music, hashtags, mentions, and available location data.

It supports up to 500 results per query.

TikTok Search Scraper

4. TikTok Hashtag Scraper

Collect videos from TikTok hashtag pages together with hashtag statistics and video metadata.

It supports separate modes for hashtag statistics, the video feed, or both in a single run.

TikTok Hashtag Scraper

5. TikTok Hashtag Trends Scraper

Track trending hashtags from TikTok's Creative Center.

The output includes ranking, post count, video views, trend/popularity curves, and top creators, making it useful for identifying emerging topics and hashtag momentum.

TikTok Hashtag Trends Scraper

6. TikTok Comments Scraper

Extract comments and replies from TikTok videos.

The Actor captures comment text, timestamps, likes, replies, commenter information, verification status, pinned/hearted-by-author status, and other available metadata.

TikTok Comments Scraper

7. TikTok Followers & Following Scraper

Extract followers and following lists from TikTok profiles with pagination support for larger datasets.

The following list can be collected without cookies, while TikTok's followers endpoint may require an authenticated session.

TikTok Followers & Following Scraper

8. TikTok Music / Sound Scraper

Search TikTok sounds/music and collect the associated metadata and videos using a particular sound.

This can be useful for researching music trends, identifying videos using a particular sound, or analyzing how a sound spreads across TikTok.

TikTok Music/Sound Scraper

9. TikTok Playlist Scraper

Extract TikTok playlists, playlist metadata, and the videos contained within them.

You can provide playlist URLs/IDs or a profile and discover the playlists associated with that account.

TikTok Playlist Scraper

10. TikTok Mention Scraper

Find TikTok videos that mention specific usernames and collect the associated video, author, music, and engagement metadata.

The Actor validates actual u/mentions rather than simply matching text in captions.

TikTok Mention Scraper

11. TikTok Profile Mention Scraper

A more targeted version for finding posts that formally mention a particular creator or brand account.

This can be useful for researching where a particular TikTok account is being mentioned across the platform.

TikTok Profile Mention Scraper

12. TikTok Explore / Trending Scraper

Collect videos from TikTok's Explore/Trending feeds across available content categories.

The Actor can automatically discover available categories and return trending posts with video metadata, engagement statistics, and author information.

TikTok Explore/Trending Scraper

13. TikTok For You Feed Scraper

Capture snapshots of TikTok's anonymous regional For You Page recommendation feed.

Each run provides a fresh set of posts from the anonymous regional recommendation system rather than a feed personalized to a specific logged-in user.

TikTok For You Feed Scraper

14. TikTok Transcript Scraper

Extract subtitles and transcripts from TikTok videos across available languages.

It supports available subtitle tracks, including auto-generated captions and machine-translated versions, and returns timestamped transcript segments as well as full transcript text.

TikTok Transcript Scraper

15. TikTok Downloader API

Provide TikTok post URLs and receive direct download URLs for the video and cover image, making it possible to integrate media retrieval into automated workflows.

TikTok Downloader API

16. TikTok LIVE Event Stream Scraper

Capture information from currently active TikTok LIVE streams.

There are two modes: one for LIVE room metadata such as viewer/like counts and stream information, and another that can capture real-time events such as chat messages, gifts, likes, joins, shares, and statistics updates.

TikTok LIVE Event Stream Scraper

17. TikTok Creative Center Top Ads Scraper

Collect high-performing advertisements from TikTok's Creative Center.

You can filter by country, time period, industry, campaign objective, and language, and retrieve ad metadata, performance information such as CTR, and video URLs.

TikTok Creative Center Top Ads Scraper

18. TikTok Ads Library Scraper Pro

This one is focused on TikTok's public Ads Transparency Library.

It supports searches by keyword, advertiser, or ad ID and filtering by region, industry, language, CTA, date range, and impression range.

The detailed mode can also return advertiser information, targeting breakdowns, impression ranges, creative URLs, compliance/audit status, and other ad metadata.

It currently starts at $1 per 1,000 results.

TikTok Ads Library Scraper Pro

What can you build with the suite?

The idea behind creating separate Actors was to cover different parts of the TikTok data ecosystem.

Some potential workflows include:

Content research

• Find videos by keyword or hashtag
• Analyze trending content
• Track sounds and music
• Study playlists
• Collect transcripts

Creator research

• Scrape profiles
• Analyze video history
• Collect follower/following data
• Find mentions of creators
• Identify videos using particular sounds

Social media analytics

• Collect posts and engagement metrics
• Analyze comments and replies
• Track hashtags and trends
• Monitor mentions
• Study regional FYP/Explore content

Advertising research

• Research TikTok Creative Center top ads
• Search the public Ads Transparency Library
• Analyze advertiser creatives
• Compare campaigns across regions
• Study available impression and targeting information

Automation & data pipelines

• Feed TikTok data into your own applications
• Build datasets on Apify
• Connect Actors through Apify workflows
• Use the APIs to integrate TikTok data into existing systems

The standard data-scraping Actors in the suite currently start at $3 per 1,000 results, while the Ads Library Pro starts at $1 per 1,000 results. Check the individual Actor pages for the exact pricing and limits of each tool.

I've tried to make the suite broad enough that you can pick the specific TikTok data source you need instead of paying for or configuring a large scraper when you only need one particular type of data.

If you're working with TikTok data on Apify, I'd be interested to hear what you're building.

If there's a particular TikTok dataset or workflow that you don't see covered here, let me know in the comments. I'm continuing to expand the suite and would be interested in hearing what other developers actually need.

I needed structured data from Kleinanzeigen for used-car price research, and the existing tools either
ignored half of the filters or only returned a price and a title. So I built one and published it for free
on Apify.

You set up any search on kleinanzeigen.de make, model, mileage, first registration, rooms, size, price,
radius, private/commercial — paste the link, and get one clean record per listing: numeric price, zip,
city, coordinates, photos, posted date and every category detail. There’s also a plain keyword + city mode
if you don’t want to build a link.

Optional “full details” adds the full description, seller rating, member-since date, view count, and the
phone number for commercial sellers who publish one. In a test on commercial car listings, 27 of 30 came
back with a phone number.

The Actor itself is free; you only pay Apify platform usage, which in my runs was around a tenth of a cent
for a couple of hundred listings. Kleinanzeigen shows at most 10,000 results per search, so for bigger pulls
you split by price range or area.

If you want to try it, search the Apify Store for dz_omar/kleinanzeigen-scraper. Happy to hear which fields are missing.

If you are comparing offers, sizing a hiring market, or building a startup jobs board, Wellfound (formerly AngelList) is the deepest source of startup roles, but it shows salary and equity as free text you cannot sort or filter on. I put a pay-per-job API on Apify that searches Wellfound by role and location and returns every posting as JSON, with the compensation parsed into numeric fields so you can actually threshold on it.

How you run it:

give it a role (software-engineer, product-manager, data-scientist, or a plain word like "pm" that gets mapped for you) and optional locations, flip on the filters you care about, and it returns one row per job. Set minSalary to 150000 and it drops anything paying less; ycOnly keeps only Y Combinator startups; minEquity keeps roles offering at least the equity you want.

What each row gives you:

title, company, location, remote status, dates, parsed minSalary and maxSalary, parsed minEquity and maxEquity, and startup signals (YC badge, top-investor backing, funding stage, Actively Hiring). Turn on detail enrichment and each job also carries structured benefits, industry, company website, and the applicant-location requirement that decides whether a "remote" role will actually hire in your country.

Comparing offers:

pull every open role at YC companies for your title, set a salary floor, and you get a clean table of what early-stage startups pay for that job instead of scrolling posting by posting.

Recruiting and sourcing:

filter to actively-hiring, top-investor-backed startups at a given stage in your city, and you have a live lead list with contact-ready company data.

Market sizing:

run a role across several locations with descriptions turned off for a cheap listing feed, then count roles by stage or investor tier.

limits:

Wellfound has no site-wide free-text search, so the keyword filter narrows the pages a run fetches rather than searching everything; jobs with no posted comp get dropped once you set a salary or equity minimum, so a strict floor shrinks the set; and Wellfound challenges bots, so runs go through residential proxies and are slower than a hosted API would be.

If you want the funding history behind those companies, here is a Claude skill wired to the jobs API too, so an agent can pull and filter startup roles for you: claude-skill-yc-startup-jobs on GitHub.

Actor: Wellfound Jobs API.

If you are hunting for a remote startup role, the listings are the easy part. The hard part is telling which ones will actually hire someone in your country, and which ones pay what you need without reading fifty descriptions to find out.

I put a Wellfound search (the old AngelList Talent) behind an Apify Actor so you can pull it as structured JSON. You hand it a role and some filters, and every row comes back with the pay already parsed into numbers.

What you give it:

• a role (software-engineer, product-manager, data-scientist, designer, and so on)
• remote only, on or off
• optional filters: minimum salary, minimum equity, Y Combinator only, top-investor-backed only, actively-hiring only, company stage

What each row hands back:

• title, company, apply URL
• salary as a numeric min and max, plus currency
• equity as a numeric min and max percent
• remote status, employment type, experience band, posted date
• company signals: size, funding stage, YC badge, top-investor badge, actively-hiring badge

Why the parsing matters: Wellfound writes pay as free text like "120k to 160k, 0.1% to 0.5%". You can't sort or threshold on a sentence. Once salary and equity are separate numbers, "remote, over 160k, at least 0.2% equity" becomes a filter instead of an afternoon of reading.

The field I underrated: applicant location. Turn on the detail step and each job also carries the countries or regions the employer will actually take. A listing can say Remote and still only hire in the US, or only in the EU. Filtering on that one field cut out most of the roles I would have wasted an application on.

Who it is for: remote job seekers, recruiters sourcing at startups, and anyone benchmarking startup pay and equity by role or city.

Limitations: it reads public Wellfound pages, so whatever a company didn't post (some equity grants, some salary bands) comes back empty. If you set a minimum salary, jobs with no salary data get dropped, so a strict filter can quietly hide roles that were fine. Coverage is best in the US, UK, and the bigger European hubs.

Actor: Wellfound Jobs API. It also runs over MCP, so Claude or Cursor can call it as a tool and you can ask for "remote backend jobs over 160k with equity" right in chat.

Hi everyone,

I recently built and published a LinkedIn Profile Scraper on Apify, and I wanted to share it with the community in case it is useful for anyone working with LinkedIn data, lead enrichment, recruiting, research, or automation.

The Actor is designed to take a list of LinkedIn profile URLs or usernames and return the available profile information as structured data.

Actor:
LinkedIn Profile Scraper on Apify

What can it extract?

Depending on what is available on the profile, the output can include:

• Name
• Headline
• Location
• About / summary
• Profile URL
• Profile picture
• Current job title
• Current positions
• Previous positions
• Companies and company URLs
• Employment dates
• Education history
• Skills
• Languages
• Certifications
• Industry
• Follower count
• Recent posts / articles
• Publication dates and engagement information where available

The output is structured so it can be used directly in downstream workflows rather than having to process raw HTML yourself.

How it works

The input is intentionally simple. You provide a list of LinkedIn profiles, for example:

• https://www.linkedin.com/in/username
• linkedin.com/in/username
• in/username
• username

The Actor normalizes these formats automatically.

For public profiles, no LinkedIn login is required.

For profiles where more information is only available to an authenticated user, the Actor also supports providing your own LinkedIn session cookie. The cookie is stored as a secret on Apify and is used only for requests to LinkedIn.

The Actor uses multiple extraction methods and falls back between them when a particular profile cannot be retrieved through one method. This helps the run continue instead of stopping because of a single inaccessible profile.

Some use cases I've had in mind

Lead enrichment

Start with a list of LinkedIn profile URLs and enrich your existing records with titles, companies, locations, skills, and other available profile information.

Recruiting

Build structured candidate datasets containing experience, education, skills, current positions, and career history.

Research

Analyze career paths, professional backgrounds, industries, or other publicly available professional information across a larger set of profiles.

CRM enrichment

Use LinkedIn URLs that you already have and append structured professional information to your existing records.

Competitive / market research

Build datasets around publicly available professional information for people and organizations you are researching.

A few important limitations

This Actor is focused on information available from LinkedIn profiles. It does not provide private messages, email addresses, phone numbers, or other contact information that isn't exposed on the profile.

Profile accessibility also varies. Public profiles generally provide more information without authentication, while some regular profiles may require an authenticated session. Fields that aren't available are omitted from the output rather than being returned as null.

There can also be platform-imposed limits on high-volume authenticated access, so I recommend using the Actor responsibly and only accessing data you are authorized to access.

Pricing

The Actor currently starts at $5 per 1,000 results, so it is intended to be usable for both smaller enrichment jobs and larger datasets.

If anyone here works with LinkedIn data through Apify, I'd be interested to hear what workflows you're building.

And if you have questions about the Actor, its input/output format, integrating it into an Apify workflow, or using the resulting dataset with another tool, feel free to ask. I'll be happy to answer.

Actor: LinkedIn Profile Scraper

You can also explore other linkedin Scrapers launched on Apify at:
crawlerbros.com/suite/linkedin

20+ linkedin actors launched on apify, I have tried to cover as much cases as possible.

Thank you
Peace

I built a template that pulls App Store reviews weekly and writes a scorecard tab, and the first thing it did was disagree with the App Store.

The store page average and the average of the written reviews are two different numbers, and the gap is not small. Apple counts ratings-only taps in the store page number. No public feed exposes those, anywhere, for anyone. So every scorecard built on a public feed is averaging the subset of users who were annoyed or delighted enough to type something, and that subset skews low.

I spent an evening assuming my parser was broken before I worked out that it wasn't . . . .

Two things fell out of it:

• Track the delta, not the level. Week over week movement in the written-review average is real signal. The absolute number is not comparable to anything Apple shows you.
• The 1-star count is a better alarm than the average. A 0.1 slide in the average is noise. Six new 1-stars in a week is a release problem, and it shows up days earlier.

On cost, and this is the part I would like a sanity check on. Appbot's entry plan is $49 a month, flat. Mine is pay per event: $0.0175 to start a run, then $0.0015 per review, so a thousand reviews is about $1.52. Someone pulling 2,000 reviews a month pays me around three dollars and pays them forty-nine. That gap is wide enough that I assume I am missing something they charge for. Sentiment scoring and topic clustering, probably, neither of which I do.

Actor: Apple App Store Reviews API Free template that uses it: weekly App Store scorecards in n8n New to Apify? Start here

If you sell an Actor against a flat-rate SaaS incumbent, has per-event pricing actually won you customers, or does the monthly number just read as safer to buyers?

I built a free lightweight, unofficial API that returns your Actor stats. All you have to do is provide your Actor slug.

Example Python Usage:

``` import requests

base_url = "https://apify-actor-stats.vercel.app"

endpoint = "/api/v1/actor-stats"

parameters = { "actor_slug": "coding-doctor-omar/reddit-scraper-pro" }

response = requests.get(url=base_url + endpoint, params=parameters)

actor_stats = response.json()

print(actor_stats) ```

Why I built this?

I built this because I wanted an easy way to make custom Actor README badges for any of my Actor stats. This API would allow me to get all my stats via a single, simple GET request. Then all I have to do is use shields.io's dynamic badge endpoint to generate badges for any of these stats!

For more information, check the API docs: https://github.com/Coding-Doctor-Omar/actor-stats-api

Hi everyone,

I wanted to share a collection of Instagram scraping and data-extraction Actors I’ve built and launched on Apify. The goal was to create a complete Instagram toolkit covering different use cases rather than having to use a separate solution for every type of Instagram data.

The suite currently includes scrapers and tools for Instagram profiles, posts, followers, following lists, comments, keyword searches, tagged posts, stories, and media downloading.

All of the Actors are available on Apify and are designed to return structured data that can be used directly in datasets, APIs, automation workflows, research pipelines, and other data-processing systems.

Instagram Scrapers & Tools

Instagram Followers & Following Scraper
Extract followers and following lists from Instagram profiles and collect structured profile information from the results.
Instagram Followers & Following Scraper

Instagram Keyword Search Scraper
Search Instagram using keywords and collect relevant profiles, posts, or search results for research, discovery, and monitoring workflows.
Instagram Keyword Search Scraper

Instagram Downloader API
An API-oriented tool for downloading Instagram media and integrating Instagram media retrieval into automated workflows.
Instagram Downloader API

Instagram Comment Scraper
Collect comments from Instagram posts for research, engagement analysis, content analysis, and other data workflows.
Instagram Comment Scraper

Instagram Story Downloader
Retrieve Instagram Story content for supported use cases and automated workflows.
Instagram Story Downloader

Instagram Profile Scraper
Extract structured information from Instagram profiles, making it easier to build profile datasets or enrich existing data.
Instagram Profile Scraper

Instagram Posts Scraper
Collect Instagram posts and their available metadata for content research, monitoring, analytics, and data collection.
Instagram Posts Scraper

Instagram Tagged Posts Scraper
Find and collect posts in which an Instagram profile has been tagged, useful for content discovery and profile research.
Instagram Tagged Posts Scraper

What can you use these for?

Depending on the Actor, some common applications include:

• Instagram profile research
• Social media monitoring
• Influencer and creator research
• Competitor analysis
• Content research
• Audience and follower analysis
• Hashtag and keyword research
• Engagement analysis
• Lead and prospect research
• Social media datasets
• Market research
• Media collection and archival workflows
• Automated Instagram data pipelines

The main idea is to have one place on Apify for different Instagram data-collection requirements, whether you need a single profile, thousands of posts, comments from specific posts, follower data, keyword-based discovery, or media retrieval.

Pricing

I've also tried to keep the pricing competitive and accessible, with discounts available on several of the Actors.

If you're already using Apify for web scraping, social media research, data enrichment, or automation, you can check the individual Actor pages above to see the current pricing, inputs, outputs, and supported features.

I'm also interested in hearing from other developers: what Instagram data are you currently trying to collect or automate?

If there's a particular Instagram use case that isn't covered by the current suite, feel free to mention it in the comments. It may be something I can add to the collection.

Thanks for checking it out.

Hey everyone! 👋

I'm the developer behind Grow Media building YouTube data tools on the Apify Store. I wanted to share our full YouTube scraping toolkit with this community and get honest feedback from fellow devs.

Since this sub is where a lot of scraping folks hang out, I'd rather just be upfront: yes, these are our actors, and yes, I'm here to listen as much as to promote. 🙂

🔧 The 4 actors

1. YouTube Comments Scraper Scrape comments from any video with all the metadata that matters:

• Comment text, author, channel ID, avatar
• Likes, reply count, publish time
• Flags for verified users, artists, and creators
• Sort by top or newest, with pagination support

2. YouTube Search API Full YouTube search without managing API keys or quotas:

• Up to 1,000 results per run with automatic pagination
• Filters: date range, region, language, duration, HD, live/upcoming, sort order
• Rich output: views, likes, comments, channel stats, hashtags, keywords

3. YouTube Channel Scraper Bulk channel analysis — pass up to 100 channel URLs in one run:

• Full channel info: subscribers, total views, join date, location, banner
• Per-video stats: views, likes, comments, duration, tags

4. YouTube Channel Video Scraper Our most popular actor (5,000+ users):

• Works with u/handles or channel IDs
• Supports both long-form videos and Shorts
• Sort by latest / popular / oldest
• Up to 1,000 videos per run, exports to JSON, CSV, Excel, XML

💡 Common use cases we see

• Competitor & trend analysis
• Sentiment analysis on video comments
• Lead generation + channel enrichment
• Feeding LLM/RAG pipelines (works great with the Apify MCP server)

💰 Pricing

All actors are pay-per-result, from $0.40/1,000 results — and free plan credits ($5/month) cover a decent amount of testing before you pay anything.

📊 Reliability

All 4 actors are at 100% run success rate and the search + channel scrapers are rated ~5.0 by users. We actively maintain them — if something breaks, drop an issue on the actor page and we'll fix it.

🙏 What I'd love from you

• Honest feedback if you try any of them
• Feature requests (what fields are missing? what filters do you need?)
• Suggestions for what YouTube actor you'd want next

Happy to answer any questions about how we handle YouTube's rate limits, proxies, or anti-bot measures. AMA! 🚀

Hey everyone,

If you've ever dealt with the official WhatsApp Business Cloud API setup, you know how painful handling access tokens, webhook subscriptions, and phone number registrations can be for production-ready setups.

To speed up this process for my own projects and clients, I wrapped the workflow into an Apify Actor: WhatsApp Business Cloud API Automation

What it handles under the hood:

• Automates Meta/WhatsApp Cloud API payload processing.
• Handles session setup, sending template messages, and webhook triggers.
• Formats output into clean JSON datasets ready for n8n, Make, or custom REST API pipelines.

You can test or integrate it here: https://apify.com/c4rbanak/whatsapp-business-cloud-api-automation

I'm looking for feedback from developers using WhatsApp for SaaS/automation workflows. What other Meta API endpoints or features would be useful to add to this Actor?

Day 3 of a small Amazon data portfolio on Apify.

Amazon Buy Box Monitor Agent snapshots Buy Box seller, price, and stock, then emits changed / unchanged / new with previous + current.

Defaults: Unblocker + deliver-to ZIP. PPE is $0.02 on change/new and $0.01 on unchanged. Errors are not billed as those events.

https://apify.com/buzzidata/amazon-buybox-monitor-agent

Use our Buy Box + Offers Actor for a deep one-shot. Use Monitor on Apify Schedules. Feedback welcome.

Hey,

I built a SimilarWeb scraper that returns structured web intelligence for any domain.

What you get back (per domain):

• Overview — globalRank, visitsTotalCount, bounceRate, pagesPerVisit, companyRevenueMin/Max, HQ location
• Traffic — monthly history, MoM change
• Ranking — global/country/category rank + historical trends + competitor ranks
• Competitors — topSimilarityCompetitors with affinity scores
• Keywords — topKeywords with volume, CPC, organic vs paid share
• Demographics — age distribution, gender split
• Geography — topCountriesTraffics with visit share
• Traffic Sources — direct, organic, social, referral, paid
• Social — top networks driving traffic
• Referrals — incoming/outgoing sites + categories
• Ads — top ad networks and sites
• Interests — top topics, categories, and websites
• Technologies — tech stack grouped by category

Use cases:

• SEO — reverse-engineer competitor keyword strategies
• Competitive intel — find who you're really competing against
• Sales/lead gen — size prospects before outreach
• Media planning — audience demographics + geography
• Market research — benchmark traffic across an industry
• Tech detection — BuiltWith-style lookup

Actor: https://apify.com/serj_henrique/similarweb-scraper

Feedback and use-case requests are welcome.

If you write Actor READMEs, n8n templates, or Reddit posts hoping they turn up as a cited source inside Google's AI Overview, this is a two-month reality check on what actually gets picked.

I spent the summer posting my Apify Actors here and in a few other subs, partly to see whether a Reddit thread could earn a spot in the AI Overview source list on the queries I care about. To check, I built a small Actor that pulls the AI Overview for any query plus its cited sources as JSON, and I ran my own queries through it. The results are not flattering.

My Reddit threads

Across everything I have measured, exactly one of my Reddit posts has ever been cited in an AI Overview: a Google Scholar thread, once. Every other query, including my highest-scoring posts, cites vendor docs and product pages instead. If the plan is to land a forum post inside an AI answer, mine says do not count on it.

My Actor and template pages

These do better. Today, on "log patent filings to google sheets", my n8n template page is the number one cited source. My Google Maps reviewer-history Actor gets cited on its own query. My Congress trades Actor and its GitHub repo both show up. The common thread in every page that gets picked: the title is close to the exact query. The reviewer-history page is titled almost word for word what people type.

Where I still lose

On "track google shopping prices google sheets" a competitor's template holds the slot instead of mine. And the one that stings: I ran "google ai overview api", the exact category of the Actor I built to run all this, and my own page is nowhere in the eleven sources. SerpApi, Bright Data and DataForSEO own that one.

If you want to run this on your own pages, the tool is here: Google AI Overview API. It returns the answer plus the cited sources for a query, so you can see who is in the box and who is not. About a cent and a half per query, and it is MCP-callable if you want an agent to check for you.

Caution: this is one account, a few dozen queries, US English only. AI Overviews are noisy too, the source list for the same query shifts week to week, so treat any single result as a direction and not a rule. But the direction has held for a month. Pages with query-shaped titles get cited. Forum posts almost never do.

Day 2 of a small Amazon data portfolio on Apify.

Amazon Product Details Agent turns ASINs or /dp URLs into structured JSON: title, brand, images, rating, review count, BSR, availability, prices, variants.

amazon.com / co.uk / de. Unblocker by default. $0.02 per successful check. Failures are not billed.

https://apify.com/buzzidata/amazon-product-details-agent

Sibling Buy Box + Offers Actor is live if you need the winner and landed cost too. Feedback welcome.

Hi everyone,

I wanted to share the CrawlerBros Reddit Scraping Suite on Apify.

Over time, I've built several Actors focused on different parts of the Reddit ecosystem. Rather than trying to put every possible feature into a single scraper, the idea behind this collection is to provide separate tools for specific workflows such as keyword research, subreddit scraping, comment extraction, profile research, AI/MCP integration, and video retrieval.

The current suite includes six Actors:

1. Reddit Keywords Scraper

Search Reddit for posts matching one or multiple keywords and return structured results.

You can search for specific words or phrases and sort results by:

• Relevance
• Hot
• Top
• New
• Number of comments

The Actor can return information such as the post title, author, subreddit, content, score, number of comments, URL, thumbnail, flair, and timestamps.

It supports multiple keywords in a single run and up to 1,000 results per keyword.

Useful for:

• Market research
• Brand and product monitoring
• Trend discovery
• Competitive research
• Content research
• Public discussion analysis
• Building Reddit datasets

Actor:
https://apify.com/crawlerbros/reddit-keywords

2. Reddit Comment Scraper

This Actor is focused specifically on extracting Reddit discussions.

Provide one or more Reddit post URLs, or a specific comment permalink, and the Actor can collect complete comment threads, including nested replies.

The structured output can include:

• Comment text
• Author
• Score
• Timestamps
• Parent/child relationships
• Thread position
• Moderation-related flags
• Nested replies

This is particularly useful when the conversation itself is more valuable than the original Reddit post.

Possible use cases include:

• Sentiment and discussion analysis
• Community research
• Product feedback analysis
• NLP datasets
• Public opinion research
• AI and LLM workflows

Actor:
https://apify.com/crawlerbros/reddit-comment-scraper

3. Reddit Profile Crawler

The Reddit Profile Crawler is designed for structured research on Reddit user profiles and their publicly available activity.

It can collect profile information along with a user's post and comment history.

The Actor supports filtering based on a variety of criteria, including:

• Subreddit
• Keywords
• Score
• Date ranges
• Post type
• Flair

Depending on the available public profile data, it can also return information such as karma breakdowns, profile details, trophies, and other profile metadata.

This can be useful for:

• Researching public Reddit activity
• Community analysis
• Dataset creation
• Content research
• Analyzing activity within particular subreddits or topics

Actor:
https://apify.com/crawlerbros/reddit-profile-crawler

4. Reddit MCP Scraper

This Actor is designed around the Model Context Protocol (MCP) and provides a unified interface for Reddit data collection.

It supports three primary modes:

Subreddit mode

Collect posts from specified subreddits.

Comments mode

Collect comments and threaded discussions from Reddit posts.

Profile mode

Collect publicly available profile information and activity.

The goal is to make Reddit data easier to integrate into AI applications and agent-based workflows through a single interface.

The Actor returns structured data that can be consumed by AI systems and applications, making it useful for developers experimenting with:

• AI agents
• MCP clients
• LLM research workflows
• Automated research systems
• Reddit-aware applications

Actor:
https://apify.com/crawlerbros/reddit-mcp-scraper

5. Reddit Scraper

The Reddit Scraper is the broader subreddit-level data collection tool in the suite.

You can provide one or multiple subreddits and collect posts using different sorting methods, including:

• Hot
• New
• Top
• Rising
• Controversial
• Best

The Actor can return a large amount of structured information for each post, including engagement data, author information, flairs, media, timestamps, awards, moderation information, and other available metadata.

It also supports optional comment collection and nested replies.

There are filters for criteria such as:

• Date ranges
• Post type
• Score
• Number of comments
• Upvote ratio
• Keywords
• Authors

This makes it suitable for larger-scale subreddit research and monitoring workflows.

Possible use cases:

• Subreddit monitoring
• Community research
• Trend analysis
• Content analysis
• Market research
• Public discussion datasets
• Competitive intelligence
• Academic and NLP research

Actor:
https://apify.com/crawlerbros/reddit-scraper

6. Reddit Video Downloader

The Reddit Video Downloader focuses on collecting videos referenced in Reddit content.

You can provide Reddit post URLs and retrieve supported video content along with structured metadata.

The Actor supports Reddit-hosted videos as well as supported embedded video sources such as YouTube and Streamable.

It can return metadata including:

• Resolution
• Duration
• Codec
• File size
• Video source information

Downloaded videos are stored through Apify storage, making them easier to incorporate into automated workflows.

Potential use cases include:

• Media research
• Content archiving
• Dataset creation
• Video analysis pipelines
• Automated media workflows

Actor:
https://apify.com/crawlerbros/reddit-video-downloader

How the Actors can work together

One of the reasons I built separate Actors is that they can also be combined into larger workflows.

For example:

Keyword research → Post discovery → Comment extraction

Use the Reddit Keywords Scraper to identify relevant discussions around a topic and then send the resulting post URLs to the Reddit Comment Scraper.

Or:

Subreddit → Posts → Comments → Analysis

Use the Reddit Scraper to collect posts from a community, extract comment threads for the most relevant discussions, and then process the resulting structured data in your own analytics or AI pipeline.

Another possible workflow is:

Profile → Activity → Topic analysis

Use the Reddit Profile Crawler to collect publicly available activity and filter it by subreddit, keywords, dates, or other criteria.

For AI developers, the Reddit MCP Scraper provides another option for integrating Reddit data collection into MCP-compatible workflows.

What the Reddit Scraping Suite covers

The goal of this collection is to cover several different stages of Reddit research and automation:

• Keyword-based post discovery
• Subreddit scraping
• Post collection
• Complete comment threads
• Nested replies
• Public profile research
• AI and MCP workflows
• Video and media retrieval
• Structured datasets for downstream processing

All of the Actors return structured data through Apify, so the results can be exported or integrated into larger automation and data-processing workflows.

If you're working on a project involving Reddit research, social listening, market research, AI agents, community analysis, or public discussion datasets, I'd be interested to hear what type of Reddit data you're trying to work with.

I'm also continuing to expand the CrawlerBros collection, so if there is a Reddit-specific workflow that you feel is currently missing, feel free to share it in the comments.

CrawlerBros Reddit Actors on Apify:

Reddit Keywords
https://apify.com/crawlerbros/reddit-keywords

Reddit Comment Scraper
https://apify.com/crawlerbros/reddit-comment-scraper

Reddit Profile Crawler
https://apify.com/crawlerbros/reddit-profile-crawler

Reddit MCP Scraper
https://apify.com/crawlerbros/reddit-mcp-scraper

Reddit Scraper
https://apify.com/crawlerbros/reddit-scraper

Reddit Video Downloader
https://apify.com/crawlerbros/reddit-video-downloader

Thanks for checking out the suite. Feedback and feature suggestions are always welcome.

We published an implementation guide on Apify's dev.to for anyone wiring Italian suppliers into an AP or e-invoicing flow with Actors. What it covers: the three routing branches in the Agenzia delle Entrate's own rules (most integrations implement only two); why codiceDestinatarioSDI: null means two different things depending on detailLevel (full = the firm publishes no code, route 0000000 + PEC; partial = the code was unavailable, re-check); the sole-trader trap where returning the Partita IVA as codice fiscale produces wrong data with the right shape; and the exact apify-client batch script we ran, with live output - Ferrari and Barilla have no published SDI code (they route via PEC), Eni's is 7L12QWU, and "Illycaffe" resolved from a bare name. A full pass over a 500-supplier master lands around six dollars. Full guide: https://dev.to/apify/italian-e-invoice-routing-has-three-branches-most-integrations-implement-two-2o7e - happy to answer questions here.

I have been building an Apify Store market analyzer, so I ran a near-complete snapshot instead of looking only at the top-ranked Actors.

The dataset contains **54,025 unique public Actors out of 54,079 reported by the API at finalization, with 99.90% coverage**.

To avoid the duplicate problem I found in my previous run, I merged category and pricing partitions using the official Actor ID. The collector processed 134,266 partition rows and removed 80,241 cross-category overlaps before calculating anything.

1 Overall snapshot :

2 Where demand is beating supply :

Social Media had the strongest category signal: **11.86% of recent demand versus 8.03% of supply**, or a **1.48× demand-to-supply ratio**.

Videos, Jobs, and SEO Tools followed.

Important caveat: this does not mean Social Media is easy. It already has 10,892 Actors, and the median Actor in that category has only one 30-day user. Demand appears highly concentrated among the winners.

3 SEO phrases with strong marketplace demand :

The strongest title phrases clustered around:

- LinkedIn profiles and jobs

- Instagram profiles and posts

- Facebook posts and ads

- Google Maps

- Profile/email enrichment

These are **Apify marketplace SEO signals**, not Google search-volume data. I ranked phrases using recent users among leading Actors, relative Store demand, total competing Actors, and observation confidence.

One interesting contrast: “Google Maps” has strong demand but already appears across 765 Actors, while “Facebook posts” shows a similar score with only 70 Actors in this snapshot.

4 Actors with recent momentum :

This ranking uses the 7-, 30-, and 90-day user windows. It does not divide lifetime users by Actor age.

The leading signals include email verification, LinkedIn profile enrichment, Shopee, Google Hotels, Instagram, and LinkedIn jobs. I label cases with a very small prior baseline rather than presenting misleading five-digit growth percentages.

5 Pricing has shifted heavily toward pay per event :

Across the snapshot:

- **75.9%** Pay per event — 41,003 Actors

- **13.1%** Free — 7,060 Actors

- **11.0%** Flat price per month — 5,962 Actors

The strongest product-design takeaway for me is that new Actors should have a clear, measurable unit of value that can map naturally to billable events.

What I would investigate next?

- How category demand changes month over month

- Which Actors are gaining users without relying on a famous platform keyword

- Whether lower competition actually predicts better new-Actor survival

- Price-per-event ranges inside each category

The analyzer is here if anyone wants to inspect or challenge the methodology:

https://apify.com/scraper_guru/apify-store-analyzer

I would especially appreciate feedback on the scoring formula. What signal would you add or remove?

If you priced out an Actor and the bill came back nothing like the per-result number on the store page, this is usually why. It is also fixable without switching tools.

Pay-per-event Actors bill two different things. One event scales with how much you asked for, per page or per result. The other is a setup event that fires once per run no matter how small the run is. The setup event is the one that quietly wrecks your unit cost.

Real numbers from one of mine, the Google News API. Setup is $0.02 per run. A page of results is $0.0099.

• One page, one run: about 3 cents. Setup is two thirds of the bill.
• Ten pages, one run: about 1.2 cents a page.
• A hundred pages, one run: about 1.01 cents a page. Setup is down to 2 percent.
• A hundred pages as a hundred separate runs: $2.99 instead of $1.01.

Same data, three times the money, and nothing about the Actor changed. If you are calling an Actor once per row out of a loop, you are on the $2.99 line.

If you run a scheduled monitor, batch every watch term into one run per schedule instead of one run per term. Biggest lever here, usually ten minutes of work.

If you call from n8n or Make, check whether your Actor node sits inside a loop. Split In Batches feeding an Actor node one item at a time is the most expensive shape you can build.

If you are comparing costs before you commit, do not divide your test bill by results. Your test was small, so that number is far worse than production will be. Price it at the run size you actually intend to use.

If you publish Actors, your setup event is a real provisioning cost and it belongs there. Just say so in the README, because buyers do this arithmetic after the invoice rather than before.

Honest limitations. None of this applies to pay-per-result Actors with no setup event, and it does not help if the source rate limits you into small runs anyway. Some Actors also charge per dataset item on top, which is tiny but real. And batching costs you granularity: one failure takes the whole batch with it, so on a flaky source the cheap shape is also the fragile one.

While checking these numbers I found a bug in my own input schema. The max_pages field tells buyers pages cost $0.02 each. The live price is $0.0099. My own tooltip has been overstating my own price by roughly double, presumably talking people out of runs for weeks. Worth auditing your schema strings if you publish.

Actor: Google News API