State of Apify Lead & Contact Extractors — Q1 2026

Lead-gen is the largest Apify category this series has measured. In Q1 2026, 2,031 actors on the Store advertise lead or contact extraction — more than forty times the aggregator segment, more than seven times the ATS segment. Together they carry 43,027 aggregated 30-day active users. The category is big, but it is not diverse: 65% of demand sits on a single target, and a single phrase in the actor title correlates with more than half of all measured buyers.

The phrase is “No Cookies”. Only 48 of the 2,031 actors use it in their title — 2.4% by count — yet those 48 actors hold 56.6% of segment demand. It is the dominant positioning signal in lead-gen on Apify, and it points at one thing: scraping LinkedIn without supplying a logged-in user session.

Three structural facts shape the rest.

First, LinkedIn is the category. LinkedIn-targeting actors (profile scrapers, people-search, Sales Navigator) hold 65% of demand in 161 actors. The next subtype — generic email finders — is 18% across 858 actors, five times the count for a third the demand. Buyers are not shopping broadly. They are solving one problem: getting LinkedIn profile data at scale.

Second, one positioning phrase eats the category. “No Cookies” means the actor scrapes LinkedIn using its own infrastructure rather than asking the buyer to supply session cookies — which is both friction and legal exposure. That value proposition is so strong that the top ten actors by demand almost all use the phrase verbatim. The phrase is now the product category name.

Third, two publisher models coexist. The top-3 publishers (harvestapi, dev_fusion, apimaestro) take 52% of demand across 17 focused actors — the concentrated model seen in ATS and aggregators. Underneath them, two publishers (scraper-mind and contacts-api) publish 319 lookalike actors between them for 3% of demand — the spray model, the opposite strategy. Both exist in the same segment.

Top actors

In plain prose, the top fifteen lead/contact extractor actors are: harvestapi / linkedin-profile-scraper (5,414 users, 12.6%, No Cookies LinkedIn); dev_fusion / Linkedin-Profile-Scraper (5,006, 11.6%, No Cookies LinkedIn); harvestapi / linkedin-profile-posts (3,416, 7.9%, No Cookies LinkedIn); harvestapi / linkedin-profile-search (2,667, 6.2%, No Cookies LinkedIn); lukaskrivka / google-maps-with-contact-details (2,417, 5.6%, Google Maps email extractor — the top non-LinkedIn entry); apimaestro / linkedin-profile-posts (1,830, 4.3%, No Cookies LinkedIn); vdrmota / contact-info-scraper (1,288, 3.0%, generic contact scraper for arbitrary URLs); apimaestro / linkedin-profile-detail (1,257, 2.9%); peakydev / leads-scraper-ppe (1,010, 2.3%, Apollo-like lead scraper); supreme_coder / linkedin-profile-scraper (971, 2.3%). Top three take 32% of segment demand; top ten take 59%.

Eleven of the top fifteen are LinkedIn profile or people-search scrapers. Ten of the top fifteen advertise “No Cookies” in the title. The exceptions are revealing: a Google Maps email extractor at #5, a generic URL contact scraper at #7, and two “Apollo-like” branded lead scrapers. Those are the only non-LinkedIn or non-No-Cookies tools that clear 1,000 monthly users.

Subtype breakdown

In plain prose: of the 2,031 lead/contact extractors on the Store, LinkedIn-people scrapers take 65.0% of demand (27,970 users across 161 actors); email finders take 18.3% (7,883 users, 858 actors); contact scrapers 5.2%; lead/prospecting tools 3.9%; phone finders 3.9%; profile enrichment 3.0%; the three smallest subtypes combined hold 0.7%. LinkedIn plus email finders together account for 83.3% of demand.

The shape is lopsided in two directions. LinkedIn has the fewest actors (161) and the most demand. Phone finders have 536 actors and almost no demand — a long tail of near-duplicates averaging three users each. The lead-gen market is not fragmented; it is concentrated on LinkedIn with a large commoditized tail of generic contact scrapers that rarely get found.

Positioning: the “No Cookies” phrase

In plain prose: “No Cookies” appears in 48 actor titles and accounts for 24,337 users — 56.6% of the entire category. The average No-Cookies actor serves 507 monthly users; the average email-finder actor serves 26. Sales Navigator-branded actors average 38 users each. Apollo-imitation actors average 261. “Verified Email” branding averages 32.

The dominance of “No Cookies” is not because it is a clever marketing trick. It is because it resolves the single hardest technical question the buyer is weighing: can I run this at scale without handing over my LinkedIn session cookies, which is both operationally fragile and a ban risk? An actor that answers that question in its title converts. An actor that doesn’t — even if it does the same underlying work — doesn’t.

This is the single most decisive positioning phrase measured across any Signal Census category so far. In the ATS segment, no phrase clears 30% of demand. In aggregators, 82.6% of demand was “silent” — no phrase at all. Here, one phrase covers more than half.

Publisher concentration

In plain prose, the top ten publishers in Q1 2026 lead/contact extractor demand run: harvestapi (12,387 users, 28.8% of segment, 8 actors); dev_fusion (5,645, 13.1%, 2 actors); apimaestro (4,404, 10.2%, 7 actors); lukaskrivka (2,417, 5.6%, 1 actor); vdrmota (1,288, 3.0%, 1 actor); peakydev (1,108, 2.6%, 2 actors); supreme_coder (1,020, 2.4%, 2 actors); scraper-mind (870, 2.0%, 109 actors); pipelinelabs (736, 1.7%, 2 actors); anchor (710, 1.7%, 3 actors). Top-3 publishers take 52%, top-10 take 71%.

The two-publisher-model split is the interesting finding. The top three — harvestapi, dev_fusion, apimaestro — all publish between two and eight LinkedIn-focused actors with “No Cookies” positioning, and together they take 52% of the segment from 17 actors. At position 8, scraper-mind shows the opposite strategy: 109 actors for 870 users total — an average of 8 users per actor, compared to harvestapi’s 1,548 per actor. Beyond the top 10, a publisher called contacts-api runs 210 actors for 422 users, an average of 2 users per actor. That is not a business model competing with the top three. It is a different product entirely: SEO surface area, where each actor is a slug bet, not a product.

Cross-category pattern: commoditization of a single phrase

What makes this category unique in the Signal Census so far is not the concentration — ATS is similarly concentrated, aggregators more so. It is that the concentration is organized around a single marketing phrase rather than a target platform, a brand, or a publisher. Buyers are not typing “harvestapi LinkedIn scraper” or even “LinkedIn scraper”; they are typing queries for which “No Cookies” is the disambiguator.

The phrase is now so load-bearing that some of the top actors include it twice — once as “No Cookies” in prose and once as “(No Cookies)” in parenthesis. One actor is literally named linkedin-profile-batch-scraper-no-cookies-required. That is not casual branding. That is treating the phrase as a SKU.

For publisher strategy, the read is concrete. If you are entering this segment and your LinkedIn scraper requires session cookies, your ceiling is an order of magnitude below the category leaders — not because your scraper is worse but because buyers literally filter for the No-Cookies phrase before they read the description.

What was excluded

The discovery pass surfaced several close calls that failed the inclusion rules.

Social-content profile scrapers — e.g., clockworks~tiktok-profile-scraper, apify~instagram-profile-scraper — were excluded even though their titles contain “profile scraper”. These tools extract posts, bios, and engagement stats for content analytics, not contact data for outbound. An Instagram profile scraper is a content tool, not a lead tool. Over 200 such actors were filtered out by requiring the name to advertise email, contact, lead, or LinkedIn-specific output.

Job scrapers that also extract recruiter emails — e.g., <site>-jobs-scraper-with-emails — were excluded. These belong in the job-board or aggregator categories, not here. The buyer intent is “find me jobs”, not “build a prospect list”.

Google Maps business scrapers without a contact-data pitch — generic local-business scrapers — were excluded. Only actors whose titles explicitly promise email or phone output from Google Maps were counted (the top non-LinkedIn actor, lukaskrivka~google-maps-with-contact-details, qualifies).

Generic “data enrichment” tools that don’t specify what data they enrich were excluded. The category requires the actor to advertise either a target (LinkedIn, Google Maps) or a contact field (email, phone, lead) — not vague “enrich any URL” branding.

Methodology

This report covers every Apify Store actor whose title or slug advertises extraction of contact data: emails, phone numbers, LinkedIn profiles, or lead/prospecting output. The Q1 2026 census began with a full snapshot of 24,333 Store actors. Each was reviewed against a positive keyword list (email finder, phone scraper, contact extractor, lead generator, LinkedIn profile, Sales Navigator, prospecting, people search, B2B data, and close variants) combined with a hard exclusion set for job scrapers, e-commerce tools, social-content scrapers, and review scrapers.

Social-media content scrapers — Instagram, TikTok, Twitter, YouTube, Facebook profile scrapers — were excluded unless the title also explicitly advertises contact-data output. A “TikTok Profile Scraper” is a content analytics tool; a “TikTok Email Finder” is a lead tool, and both are common, so the filter must read the specific claim in the title.

Only actors with at least one measured 30-day active user were counted. The 2,031 qualifying actors carry 43,027 aggregated users. Each was classified into one of nine subtypes based on the dominant signal in its name. Positioning-phrase tallies are computed by simple case-insensitive regex on the title field — rows in the positioning table are not mutually exclusive, since an actor titled “LinkedIn Profile Scraper + Email (No Cookies)” counts in three.

Raw per-actor classification is at discovery.csv. The included set is at included.csv. Per-publisher aggregation is at publishers.csv.

Next quarter’s edition rolls the census forward to the Q2 2026 snapshot. The positioning-phrase table will be the one to watch: if “No Cookies” remains dominant, the product-category framing holds. If a new phrase rises, it will be visible in the per-phrase per-actor average before it shows up in category totals.