Bulk Download Facebook Ads Without a Scraper Bill

What does bulk actually mean here — a page, an advertiser, or a niche?

Bulk means one of three different scopes, and mixing them up wastes a week of downloading the wrong thing. It can mean everything one advertiser is currently running, typically 5 to 40 live variants for a direct-response account. It can mean every advertiser running a specific niche or product angle, which climbs into the hundreds once you count copycats and outright clones. Or it can mean a full country sweep, the largest scope of the three and the one most likely to run into access limits that have nothing to do with volume at all.

We tested all three scopes before settling on this framing.

Country-level scope is where the exercise turns political rather than technical. You don't need a VPN to confirm whether a foreign market's active set is even visible, contrary to what most scraper guides assume — see how to see Facebook ads from other countries for what actually gates access.

What are the API's real limits per request and per day?

We could not verify Meta's current per-request result cap or per-day call ceiling for the Ad Library API, Meta's public ad archive endpoint, from a primary source in the research behind this page, and we're not printing a number we can't stand behind. The figures operators quote in forums swing from a few hundred results per call to several thousand before pagination kicks in, which is too wide a range to plan a job around. What would settle it: pulling Meta's own Ad Library API reference documentation on the day you plan to run the pull, since these ceilings have moved before without much public notice.

The one hard limit we did confirm sits in the interface, not the API.

Facebook's own Ad Library page downloads one video at a time, a restriction covered in more detail on whether you can download video from the Facebook Ad Library, and it's the reason a hundred-ad pull through the interface alone means a hundred separate clicks unless a tool is doing the fetching for you.

Which tools handle bulk, and what do they charge?

Five tools cover most of the market for bulk-downloading Facebook ads, and the pricing gap between them maps almost exactly to how much of the database you actually need.

AdSpy is the largest by database size and the simplest by pricing, one flat $149 a month against a claimed archive that dwarfs the rest of the field, per AdSpy's own site.

Bulk pulls earn their keep before a budget decision, not after. Checking what a competitor changed in their creative set is one of the more reliable signals when you're weighing whether to cut spend without wrecking a working campaign, since a sudden drop in variant count from a rival usually means they saw the same fatigue curve you did.

BigSpy and Arcads are the two to budget for carefully. BigSpy's pricing page returned no readable plan data when we checked it, so its historical $9-to-$99 range needs confirming before you commit; Arcads, an AI ad generator sometimes shopped alongside these tools, publishes no price at all, and its sitemap contains no pricing URL, so expect a sales call instead of a checkout page.

How long does a hundred-ad pull take end to end?

A hundred-ad pull runs anywhere from twenty minutes to half a day, and the swing comes almost entirely from which layer is slow: search, download, or storage. Searching and filtering to build the target list is the fast part — a well-scoped niche or advertiser query in a paid tool like AdSpy or Minea returns a hundred candidates in minutes. Downloading is where the time actually goes, because most tools resolve one signed video URL per ad rather than offering a true batch endpoint, so a hundred ads means a hundred individual fetches, and each one that times out or gets rate-limited adds a retry you have to queue rather than skip. Add metadata capture, advertiser name, ad archive ID, first-seen date, country, for every file if you want the folder to mean anything six months from now, and the honest range for a careful pull, not a rushed one, is two to four hours of active or semi-active time.

Rushing this step is how you end up with files you can't attribute later.

What breaks at scale — rate limits, storage, or naming?

Storage breaks first for most operators, rate limits break second, and naming breaks last but does the most damage, because nobody notices until the folder is already useless.

Country scope is the other place bulk pulls stall, and it isn't a rate limit at all. It's whether the platform is running ads in that market in the first place, a question that changes often enough that our page on running Facebook ads in Russia is worth checking before you plan a country-wide pull around it.

• Storage: a hundred video ads at typical VSL length, a long-form video sales pitch, runs 2 to 6GB; a $4/month DigitalOcean droplet with 10GiB of disk per [DigitalOcean's Droplet pricing](https://www.digitalocean.com/pricing/droplets) fills up in a handful of pulls, which is why most desks move to object storage instead, where Bunny's Volume tier bills from $0.005/GB delivered per [Bunny.net's pricing page](https://bunny.net/pricing/).
• Rate limits: unverified for the Ad Library API itself, as covered above, but the interface's one-file-at-a-time download is the wall most people hit first regardless of what the API technically allows.
• Naming: ad archive IDs are unique, but advertiser names and file names are not, and a folder full of "ad_final_v2.mp4" pulled from four different tools will not merge cleanly on its own.

What does a folder of a hundred mp4s tell you on its own?

On its own, a folder of a hundred mp4s tells you close to nothing. It proves ads existed; it doesn't prove they worked, ran recently, or came from a real budget rather than a $20 test. Volume without dates produces an archive, not evidence, and treating a raw scrape as market research is the mistake that wastes the two to four hours it took to build the folder in the first place.

A hundred files sorted by filename is not a dataset.

Some share of any bulk pull will be AI-generated rather than shot, and that share is rising fast enough that spotting it now saves a wasted swipe file, a reference folder of others' ads, later — the visual tells are covered separately in how to find AI-generated ads in the Facebook Ad Library.

Which fields have to travel with the file for the set to stay useful?

Six fields turn a folder into a dataset: advertiser name and page ID, the ad archive ID, first-seen and last-seen dates, the country or countries it ran in, and the landing page domain it pointed to. Miss the dates and a two-year-old ad looks identical to one launched last week; miss the domain and you've saved a hook with no offer attached to it.

Strip any one of these and the file becomes a swipe, not a signal.

• Advertiser name and page ID — ties the ad to a specific account, not just a brand name a dozen dropshippers reuse.
• Ad archive ID — Meta's own identifier per ad, the one field close to guaranteed not to collide.
• First-seen and last-seen dates — the difference between a live test and something pulled from years ago.
• Country or countries served — tells you whether the angle is even runnable where you operate.
• Landing page domain — the offer itself, not just the hook that leads to it.
• Format and placement — feed, reels, or stories; a script built for one rarely ports untouched to another.

Quick decision checklist

Use this page as a decision aid, not a generic blog post. The practical question is whether the reader needs faster evidence about what is already working in VSL-driven direct response, especially across nutra, supplements, GLP-1, weight loss, blood sugar, and adjacent high-intent health markets.

Daily Intel Service is most relevant when the next decision depends on active market examples: which hook to test, which claim style is risky, which funnel structure is common, which language market is moving, and whether a competitor's creative is likely early, scaling, or already saturated.

• Start with the TL;DR if you need the direct answer.
• Use the table to compare trade-offs quickly.
• Use the FAQ for answer-engine-ready summaries.
• Use the CTA when the decision requires live VSL and ad examples instead of theory.

Daily Intel's coverage advantage

Daily Intel Service is positioned around category-leading variety and actionability: one of the broadest direct-response catalogs of VSLs and ad creatives across blackhat, greyhat, and whitehat advertising patterns, with enough context to understand what the advertiser is doing beyond the visible creative. The practical difference is that members are not just seeing a screenshot; they are seeing the VSL, the ad, the funnel path, the transcript, the UTM context, and the research notes that turn the asset into a decision.

This matters because direct-response affiliates do not operate in one clean category. A weight-loss campaign may use a whitehat compliance ad, a greyhat pre-lander, a more aggressive VSL, and a checkout path designed around upsells and recovery. A useful intelligence platform needs to capture that spectrum instead of pretending every winning campaign looks like a public brand ad.

Blackhat, whitehat, and multilingual signal coverage

Daily Intel tracks patterns across both blackhat-style and whitehat-style campaigns so operators can understand the market without blindly copying risk. Whitehat examples help with durability and compliance review; blackhat and greyhat examples reveal pressure points, hooks, mechanisms, and funnel structures that may be driving spend but require careful adaptation before use.

The catalog is also built for global operators, with VSL and ad references spanning 14+ languages and different local idioms. That is a key advantage for Brazilian, LATAM, European, MENA, Indian, and non-native English affiliates who need to see how the same market desire is translated across cultures instead of only studying US English ads.

How to use the intelligence responsibly

The goal is modeling, not copying. Use Daily Intel to understand structure: hook, mechanism, proof, claim intensity, funnel depth, offer economics, and saturation stage. Then build original creative, review claims, and adapt the angle to the traffic source, country, language, and compliance requirements of the campaign.

A strong workflow compares multiple examples before acting. If the same mechanism appears across several languages, several advertisers, and several funnel variants, it may be a durable market signal. If the example appears only once or depends on an aggressive claim, treat it as a research clue rather than a campaign template.

• Model structure, not protected creative assets.
• Separate whitehat durability from blackhat persuasion pressure.
• Compare US English examples against LATAM, European, and other language variants.
• Use transcripts and funnel notes to build original briefs.
• Keep compliance review separate from market research.

Methodology and source context

Daily Intel pages are written from a research workflow that reviews active VSLs, Meta ad creatives, transcripts, UTMs, funnel paths, checkout steps, upsells, recovery sequences, and compliance-sensitive claim patterns. The goal is to explain observable market behavior, not to provide legal, medical, or platform policy advice.

For educational pages, the supporting references should help readers verify search, crawlability, and public ad research context, especially Meta Ad Library, Meta advertising standards, and Google helpful content guidance. Daily Intel then adds the direct-response interpretation layer so the page explains what the signal means for actual affiliate research decisions.

For deeper evaluation, continue through Ad spy comparison hub, Anstrex Alternatives: What Else Covers Native, Push and Pop, BigSpy Alternatives: Leaving the Quota That Doesn't Reset, AdHeart Alternatives: Meta Depth Without the Search Cap, Foreplay Alternatives: Swipe File, Briefs, or Just the Ads, and What is a VSL?. These related Daily Intel pages connect this topic to the relevant methodology, pricing, trust context, comparison path, or niche workflow.