I’m interested in how other Apify users interpret this.

I set my monthly platform usage limit at $110. The UI and docs calls it a “hard limit” intended to prevent accidental overage or limit extra charges, while the usage page says platform services pause if the limit is reached. My dashboard later showed $130.70 of $110.00.

Support acknowledged that “hard limit” reasonably sounds like an immediate cutoff, but said it functions more like a safeguard and usage already in progress can pass it before the system catches up. In a later response, support explained that bursts of pay-per-event Actor runs can produce billable events faster than the account-wide limit check can block them. They declined a credit because the underlying usage was valid.

I understand the technical explanation, and I’m not disputing that the Actors produced billable results. My question is whether charging $20.70 above a control presented as a hard limit is consistent with how that setting is labeled and described.

Would you reasonably expect $110 to be the actual maximum here? Or is this amount of overshoot fair for delayed enforcement of pay-per-event usage? Has anyone else encountered this?

I kept hitting the same wall. Getting Crunchbase funding, investors, and firmographics into a spreadsheet basically means paying for their enterprise API, which is priced for big teams. So I put together a small n8n template that does it without a Crunchbase API key.

How it works: you paste a list of Crunchbase company URLs into one Set node, an Apify actor collects each company, a Filter drops any URL the source cannot return, and the fields you pick get appended to Google Sheets. Six nodes, works on Cloud or self-hosted.

Template: https://n8n.io/workflows/17353-collect-crunchbase-funding-data-into-google-sheets-using-apify/

Happy to answer questions, or take requests for other fields to pull.

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.

My actor (Google Maps No-Website Leads) had a nasty integration bug: the child Google Maps scraper returned valid data (title, phone, website, url), but my pipeline expected canonical fields (businessName, phonePublic, websiteUrl). Rows passed through unmapped, so buyers would pay for a run and get empty output.

The annoying part: my tests all passed, because my CSV fixtures already had canonical columns. Production was broken while the test suite was green.

Fix was a normalization layer mapping child scraper fields into the canonical schema, plus a status bug where temporarilyClosed: false got stringified into businessStatus: false.

Live smoke test after the fix:

• 2 real no-website mobile welders in Austin, priority score 88, phones included, pitch-ready outreach line
• 1 healthy-site business correctly classified as not_a_fit and skipped

Actor: https://apify.com/luminar/google-maps-no-website-leads

Two questions for fellow builders:

1. How do you validate your output pipeline against the ACTUAL child dataset shape, not fixtures? I added a regression test replaying a real child record through the mapper, but it feels like there is a better way.
2. Store discoverability: I went from invisible to ranking #3-8 for no-website-leads searches after renaming the slug to match buyer search terms. Any other Store SEO tricks that actually worked for you?

Hey everyone, I’m building Cosnify and just shipped a small free tool for Apify Actor builders:

https://cosnify.app/tools/apify-actor-audit

You paste a public Apify Actor URL and it gives a Store-readiness score across:

- Store positioning

- README quality

- SEO title/description

- input UX

- output/dataset clarity

- trust signals like maintenance, usage, reviews, and recent run reliability

Important: v1 does not run your Actor or inspect private source. It only audits public Actor metadata available from the Apify API, so it is more of a Store/docs/readiness check than a runtime quality test.

I built it because a lot of Actors are technically useful but hard for buyers to understand quickly. My goal is to help builders improve README structure, input explanations, output examples, and Store SEO before publishing or promoting.

Would love feedback on:

1. Are these the right criteria?

2. What would you add to a serious Apify Actor audit?

3. Should the next version inspect `input_schema.json`, `dataset_schema.json`, and run examples?

I made a small bulk email verifier for Apify after deciding that SMTP reachability was too invasive for a simple list-checking tool.

It checks:

- syntax

- DNS MX

- disposable domains

- role-based addresses such as support@ and sales@

- a transparent 0–100 heuristic score

It returns one structured result per unique input, up to 500 addresses.

The boundary is intentional:

- no SMTP connection

- no RCPT TO

- no email sending

- no mailbox discovery

So “valid” means the syntax and domain-level signals passed. It is not a guarantee that an individual mailbox exists.

I’ve run 4/4 local tests. The public Actor is pay-per-event at $1 per 1,000 billable results. There are no external paid runs yet; I’m sharing the actual state rather than claiming traction.

I’d appreciate blunt feedback on whether this narrower, read-only design is useful.

Public Actor: https://apify.com/invaluable_rondeau/email-verifier-bulk

Disclosure: I built this and the link is mine.

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