An Apify alternative, costed line by line.

How each one bills

Apify prices the items. FetchLayer prices the call.

Apify

1. 01 · Plan

   Free ($5 of usage a month), Starter $19, Scale $199, or Business $999. The fee becomes prepaid usage. Overage goes on the next invoice, and unused usage expires at the end of the month.

2. 02 · Actor price

   Each actor’s author sets prices per event. That usually means per result (each post, comment, review or ad), often with a start fee per run. Most include compute and proxies; some charge them separately.

3. 03 · Plan discount

   Paid plans unlock Bronze, Silver and Gold prices on actors whose authors opt in. The scenarios below use the discounted price for whichever plan is cheapest.

FetchLayer

1. 01 · One price list

   $1.99 per 1,000 requests pay-as-you-go, with credits that never expire. Or subscribe from $25 a month for 15,000 requests. The price is the same on every platform.

2. 02 · Requests, then pages

   A call is one request. A call that walks several pages bills one request per page, and the response says how many in pagesScraped.

3. 03 · Items are free riders

   A page costs the same whether it holds 20 reviews or 250 Shopify products. 33 of our 35 platforms are in beta at half credit right now, but this page quotes the full price.

Side by side

What you’re actually choosing between.

Choosing an actor

On Apify, picking the scraper is a job in itself.

We counted Apify Store actors with each platform’s name in the title. That count includes neighbouring jobs, like jobs versus reviews on Glassdoor. Each one has its own author, input format, output fields and price. On FetchLayer, each platform is a single documented API.

Actors with the platform in the title / distinct authors. Apify Store API, 14 September 2026. The TikTok and Instagram rows are a later census of the same API, read on 16 September 2026, and count only actors with at least 100 users: 969 actors from 323 developers carry TikTok in the title and 187 from 91 developers have been used by 100 people or more; 1,020 actors from 306 developers carry Instagram and 289 from 109 developers clear the same bar. The Link in Bio row is stricter again — a keyword match returns 99 actors, of which 33 from 14 developers actually target link pages. The Upwork and Email Finder rows are the same later census, read on 17 September 2026: 71 actors from 63 developers carry Upwork in the title and 14 from 13 developers clear 100 users; 61 actors from 44 developers match “email finder” and 29 from 23 developers clear the same bar. The Airbnb row is the same later census, read on 17 September 2026: a raw keyword match returns 1,341 hits, which sampling narrows to 299 genuinely Airbnb-titled actors from 163 developers — a measured lower bound, not a ceiling. Every figure here is a keyword match computed locally from the Store API, not a number Apify publishes.

Airbnb is a crowded shelf where the thing most people want is the thing fewest actors do. A raw keyword match returns 1,341 hits; sampling what they actually are narrows it to 299 genuinely Airbnb-titled actors from 163 developers — a measured lower bound rather than a ceiling. Across that set, 85% do search and 71% return a price, but only 53% return a host profile and 49% return reviews at all. Reviews are the half of this category that half the shelf skips.

Reviews are also where the sticker price can mislead. creation/airbnb-property-reviews-scraper advertises $0.0005 per review — $0.50 per 1,000 — and then charges a flat $0.50 every time the actor runs. On a run that returns ten reviews that is $0.505 for ten, or $50.50 per 1,000: about a hundred times the advertised rate, and the gap closes only if every run is enormous. This is a trap among niche actors, not how the category works. The clear leader, tri_angle/airbnb-scraper with 16,845 users, charges a start fee of $0.0001 — small enough to be a rounding error at any run size, and nothing like the above. FetchLayer bills the page read: one credit per page, no start fee, and a page of fifty reviews costs the same as a page of five. Read from the Store API on 17 September 2026 and computed here.

What separates the reviews here is not the price but what the response admits. Every FetchLayer reviews response states the listing's full review count next to how many came back — "These are 10 of the 52 reviews the source reports for this listing." — so an average built on a page is never mistaken for the listing's rating, and no second call is needed to find out which you are holding. On the other half of the category, FetchLayer returns a night-by-night availability calendar with the minimum and maximum stay length for each night.

Upwork is the thinnest shelf on this page. A keyword census returns 71 matches from 63 developers, of which only 14 from 13 developers have been used by 100 people or more — fewer than any other platform here. What the survivors have in common is that none of them tells you how complete a result is: a posting returned without its proposal count looks identical to a posting nobody has applied to. FetchLayer reports enrichedJobs against the number of postings on every response and marks each row enriched, so a thin result set is something you can see rather than something you find out later. Read from the Store API on 17 September 2026 and computed here.

Email Finder is the shelf where the word covers the most different products. 61 actors from 44 developers match the term and 29 from 23 developers clear 100 users — but “finding an email” spans guessing addresses from a name-and-domain pattern, bulk-verifying a list, and reading what a target actually publishes. Only the last of those is what this is. The difference shows in what comes back: every address here carries the page it was read off and a grade capped by who published it, so an address the target never published can never be presented as a confident one. And a target that publishes nothing gets a definitive answer rather than an empty cell. Read from the Store API on 17 September 2026 and computed here.

Link in Bio is the shelf where the run counts lie loudest. A keyword census returns 99 matches, but only 33 actors from 14 developers actually target link-in-bio pages — and two developers own 19 of those 33. Twenty-one were created in August 2026 and twenty-four have two users or fewer, so the shelf is newer than it looks. The clearing price is $4–5 per 1,000. The number worth pausing on is the runs: the #2 actor by 30-day runs fails 93.5% of the time, which means its run count is mostly retries rather than results. On the features: exactly one competitor resolves redirects at all and none does it for Linktree, none offers provider detection, and none has an MCP server. Read from the Store API on 16 September 2026 and computed here.

Instagram is the most crowded shelf on the Store and the one where the sticker price says least. Across the 289 actors that clear 100 users, the median list price is $2.99 per 1,000 — but 75% of them charge a separate per-run start fee on top, so the quoted rate is only part of the bill. The clearest case advertises $0.3/1K and charges $0.005 every time the actor starts: on a run that returns ten results, that works out to roughly $800 per 1,000 items. Add-on charges elsewhere run 5–18× the base rate. FetchLayer bills the page read — no start fee, no add-on tier — and Instagram's page is twelve items, so one page is one credit whether it comes back full or half empty. Read from the Store API on 16 September 2026 and computed here.

TikTok is also where the per-event surcharges are easiest to see. The category leader, clockworks/tiktok-scraper with 285,739 users, bills $3.70 per 1,000 list items on the free tier, falling to $0.50 per 1,000 at the top discount tier — and then adds $1.30 per 1,000 items for each add-on filter a run uses (date, popularity, search sorting) plus $0.001 every time the actor starts. FetchLayer bills the page read: no start fee, no per-filter charge, and a page of thirty videos costs the same one credit as a page of five. Read from the Store API on 16 September 2026.

To be fair to Apify: the most-used actors are usually solid, and many succeed on more than 95% of public runs. Apify itself maintains a leading actor for Google Maps, Amazon, YouTube, Tripadvisor and Facebook ads. Most other platforms we checked have only community actors in their top four. And coverage runs out at the edges: we found no MakerWorld actor, two Printables actors with 11 and 12 users ever, and 18 for Temu.

Developer and agent experience

One platform to run code vs one API to call.

Apify run an actor

# 1. Pick an actor, learn its input schema
$ curl -X POST \
  "https://api.apify.com/v2/acts/trudax~reddit-scraper-lite/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"startUrls":[{"url":"https://www.reddit.com/r/programming/"}],"maxItems":100}'

# 408 if the run takes longer than 300 seconds.
# After that: start a run, poll it, read the dataset.

# 2. Switch actors, rewrite the input and output mapping
#    automation-lab~reddit-scraper expects
#    {"urls":[...],"maxPostsPerSource":100,"includeComments":true}

FetchLayer call an endpoint

# Any of 35 platforms, one key, one shape
$ curl -X POST \
  https://api.fetchlayer.dev/reddit/community-posts \
  -H "Authorization: Bearer ss-your-key" \
  -H "Content-Type: application/json" \
  -d '{"subreddit":"programming","limit":100}'

# Synchronous JSON (or "format":"markdown")
{
  "items": [ ... ],
  "pagesScraped": 5,   // what you are billed
  "nextPageUrl": "..."
}

FAQ

FetchLayer vs Apify, answered.