Same Vertical, Four Networks: Where the Payout Actually Differs

How do you hold vertical constant and vary the network?

You filter by vertical first, then let the network column move. The catalogue holds 25 distinct verticals spread across all 11 active networks, and 15,582 of its 28,042 offers carry a numeric CPA or commission amount, so payout becomes a column you sort rather than a number you dig for inside a VSL. Pick a vertical, sort by network, and the spread sits on one screen.

No ad-intelligence vendor can run this same query. AdSpy lets you filter ads by affiliate network, affiliate ID and Offer ID, and AdSpy's own feature list shows how far that gets you, but it filters ad creatives, not a catalogue of offers with vertical and payout as first-class fields. AdPlexity Native goes further by naming the networks it covers, listing ClickBank, BuyGoods and MediaForce inside its affiliate-network filter, yet the object being filtered is still the ad.

That distinction matters more than it sounds. An ad-spy tool tells you who is running creative in a vertical right now; a payout comparison across networks tells you what each version of that vertical actually pays before you spend a dollar testing it. If you haven't settled the vertical itself, that decision carries its own tradeoffs around payout, risk and geo.

What does the payout spread inside one vertical typically look like?

The spread inside a single vertical is structural more than random; it tracks the network's business model more than the specific offer. Producer marketplaces such as Braip, Hotmart Affiliation, Kiwify, Hotmart and Monetizze typically pay a percentage of the sale price, so a $200 course and a $40 ebook in the same vertical throw off very different commissions even at an identical rate. CPA networks like CPALead, MyLead and dr.cash typically pay a flat amount per lead or install regardless of price, which compresses the spread at the low end and caps it at the high end.

Read the table below as a starting shape, not a price list. The actual dollar figures live on each offer record and move with the product's own pricing, not with the network itself, and Admitad sits in between, carrying both CPA and percentage-of-sale deals depending on the merchant.

Why does the highest payout not mean the highest earnings?

The highest number on screen is the payout per conversion, not your earnings per click, and the two only match if every network converts your traffic at the same rate, which they don't. A $150 flat CPA offer converting at 1-in-400 clicks earns less per hour of spend than a $60 offer converting at 1-in-90, and nothing in a payout column tells you which situation you're in until you've run traffic against it.

Two other levers pull the real number further from the listed one: refund and chargeback holds on the payout, and the reserve period a network sits on before releasing the balance. Producer networks selling info-products routinely carry both, since digital-product refund windows tend to run longer than a physical SKU's. Even the same product listed on two networks can pay differently once holds and reserve periods are netted out, worth checking before you assume the higher listed figure wins.

How do commission models differ between producer and CPA networks?

Producer networks tie the commission to the price the buyer actually paid, so your payout moves with the offer's own pricing and with any upsell or order-bump the funnel adds after the initial sale. CPA networks fix the number in advance; you get paid the same flat amount whether the lead converts into a small trial or a large backend sale, because the CPA network isn't paying you a cut of that backend at all.

This is also where threshold and hold rules start to matter as much as the headline rate. Digistore24 currently carries zero offers in this catalogue even though the network is configured, but where it does list offers elsewhere, its payout structure runs through specific thresholds and a 10% rule worth understanding before you commit a vertical to it. Most CPA networks publish no equivalent threshold rule at all; the flat rate is the whole contract.

How does geo restriction change the effective value of a payout?

Geo restriction changes what a listed payout is actually worth, because it changes whether you're even allowed to run the traffic that would earn it. Of the 28,042 active offers in the catalogue, 25,293 carry at least one geo target, which means the remainder are either geo-open or simply missing that field, a distinction worth confirming per offer before you commit budget to a country.

A $90 payout open to 40 countries and a $90 payout locked to three are not the same offer economically, even though the payout column reads identically. The catalogue spans 79 distinct geo codes, wide enough that the same vertical can show one network paying a flat rate globally and another paying an identical number but only inside a handful of markets where your traffic cost is also highest.

What does the catalogue not tell you about conversion rate?

It doesn't tell you the conversion rate at all. Payout, vertical and geo are structured fields in this catalogue, but live conversion data belongs to the network's own tracking dashboard, not to a listing. Treat every payout figure here as the ceiling on what an offer pays, never as a promise of what it will earn.

Two honest gaps compound that limit. Only 274 offers were re-confirmed by a scrape in the last 30 days, so a payout you're reading today may already have shifted on the network side. And of the 28,042 offers, 8,668 carry a long description over 300 characters while 18,742 carry none at all, meaning roughly two-thirds of the catalogue gives you the payout and the vertical but not the editorial context to judge fit before you test it.

Which checks close the gap between listed payout and real EPC?

Four checks close most of the gap, and none of them take longer than opening the network dashboard. Run the offer's own tracking link before committing spend, confirm the landing page still resolves, pull the network's own EPC or conversion stat where one exists, and re-verify the payout figure against the dashboard rather than the catalogue record once more than a few weeks have passed.

• Confirm the landing URL resolves and matches the vertical — 25,085 of 28,042 offers carry a landing URL, so its absence is itself a signal to check the network directly.
• Pull the network's own EPC or conversion-rate stat before running budget; payout tells you the ceiling, EPC tells you the reality.
• Check the last-seen or last-confirmed date on the offer record and treat anything untouched in 30-plus days as needing a manual re-check.
• Watch the creative side, not just the offer record — with 3,979 VSLs and 4,296 ads in the library, [the highest-payout offer in a vertical is not always the one with the strongest running creative](/learn/highest-paying-vsl-offers-in-2026-across-8-networks).

When is a lower payout on a deeper network the better trade?

A lower payout is the better trade whenever it sits on a network with meaningfully less competition chasing the same buyer. This is the argument most media buyers resist, because the instinct is to sort by payout and start at the top, but the top of a saturated network's payout column is also where the most experienced buyers in the niche are already spending.

The clearest evidence for this sits outside the payout column entirely. None of the eleven ad-intelligence competitors researched for this catalogue names Hotmart, Braip, Kiwify or Monetizze anywhere in their stated coverage, and those four Brazilian and LATAM producer networks account for 21,650 of the catalogue's 28,042 offers, more than three-quarters of it. A buyer who only ever compares ClickBank and ordinary CPA payouts is competing in the one corner of the market every spy tool already covers; the alternatives worth ranking by real payout sit mostly outside that corner.

None of that makes the lower number a guarantee. It makes it a bet with better odds, because fewer buyers have already tested the funnel to exhaustion, and odds, not the payout column, are what should decide the trade.

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 external context, readers should compare advertising and research decisions against authoritative primary references such as Meta Ad Library, Google helpful content guidance, and Google SEO link best practices. Daily Intel adds the proprietary direct-response layer: blackhat, greyhat, and whitehat campaign pattern comparison across VSL-heavy niches and 14+ language markets.

For deeper evaluation, continue through An Offer Database, Not an Ad Database: 28,042 Records You Can Filter, From 28,000 Offers to Five: A Filtering Order That Works, Show Me Only the Offers With a Number Attached, Sorting Offers by Payout When the Payout Is Actually a Number, Best ad spy tools for direct response affiliates, and Best $50/month affiliate tool stack. These related Daily Intel pages connect this topic to the relevant methodology, pricing, trust context, comparison path, or niche workflow.