Online advertising platforms: the complete 2026 comparison

The complete 2026 comparison of online advertising platforms, by category

Methodology at the foot of the piece. The verdict above, and the per-category verdicts below. If the verdict surprises you, the methodology will explain why; if the methodology has a hole, the address is in the footer.

There is no best online advertising platform. The category is too broad for a single winner the way “best vehicle” is too broad — a query is not a shelf placement is not a notification, and a ranking that averaged them would mislead every reader who took it literally. So this piece does the thing the head-term listicles refuse to do: it compares the five platform categories against each other on a single framework, scores eleven named platforms one-to-five per axis, and then gives a verdict per advertiser profile. For high-intent demand capture, the recommendation is Google Ads with Microsoft Advertising as the cheaper-CPC complement. For visual prospecting and brand-building, Meta and TikTok. For lower-funnel retail conversion, Amazon Ads. For open-web and CTV reach bought from one seat, The Trade Desk or DV360. For transparent, low-floor direct-response testing across nine formats, an independent RTB network — and that is where Adsy sits, and the only category it should be judged in. Five jobs, five answers. Read the table for the one that matches yours.

I’m James. Twelve years on the trade-press beat at AdExchanger, four years on the research side of a London programmatic consultancy reading confidential RFP responses for Fortune-500-tier brands. The reason this site exists is that the trade press has been quietly captured by sponsored coverage for ten years now, and the comparison category needs a reviewer who is not paid by either side. I covered the walled-garden consolidation from the press box and the open-web supply chain from the consultancy seat; I have watched roughly seven of every ten ad dollars migrate behind a handful of logins while the open web fragmented into the long tail this piece’s final category lives in. That is a modest thing to claim. It is not nothing.

The landscape, in one paragraph of numbers

Global digital ad spend reached roughly US$790B in 2024 — about 72.7% of all worldwide ad investment, against a total ad market of around US$1.1T growing 7.3% year on year. Programmatic accounted for roughly 82% of global digital, more than US$650B absolute, up about 12%. In the US alone, programmatic ran to $162.4B in 2025, up 20.5%, roughly 81% of digital display. Those three numbers tell the structural story: advertising is now mostly digital, digital is now mostly programmatic, and the buying surface for all of it is a small number of platforms plus a long open-web tail. The walled gardens — Google, Meta, Amazon, the social platforms — take the large majority of the spend; the phrase the industry uses is “walled gardens” while it continues to spend about seven dollars in ten inside them. The remainder splits between the programmatic DSPs buying the open web and the independent networks selling the formats the gardens do not. Hold those proportions in mind through the table; they are why the categories price the way they do.

The five platform categories, and why you cannot average them

Before any comparison, the categories. Each sells a structurally different thing, and the difference is the whole reason a single ranking is the wrong output.

Search. Google Ads and Microsoft Advertising sell intent at the moment of a query. The user has already declared what they want; the platform auctions the answer. This is the highest-intent inventory on the open internet and it is priced accordingly. Search is demand capture, not demand creation — it harvests intent that already exists rather than manufacturing it.

Social. Meta, TikTok, LinkedIn, X, Pinterest and Reddit sell interrupted attention against an interest, behaviour and identity graph. The user did not ask for the ad; the platform infers who they are and shows it anyway. Social is demand creation and prospecting — it is where a brand builds awareness and finds buyers who were not yet searching. The auction is closed, the identity graph is proprietary, and the measurement is the platform’s own.

Retail media. Amazon Ads and Walmart Connect sell placement at the point of purchase, with first-party transaction data attached. The user is already in a buying mindset on a retail surface; the platform sells the sponsored slot next to the decision. This is the fastest-growing category in the landscape because the purchase data closes the attribution loop the open web spent three years trying to rebuild after the cookie-deprecation reversal of 22 July 2024.

Programmatic DSPs. The Trade Desk and DV360 are not inventory owners; they are buying seats. They let one trader purchase open-web display, video and connected-TV across many exchanges, with the buyer’s own data and measurement layered on top. A DSP is a way to buy the open web at scale with cross-exchange reach — the opposite product to a walled garden, and the natural home for CTV budgets that have nowhere else to consolidate.

Independent ad networks and RTB. Adsy, Adsterra, PropellerAds, RichAds, Adcash, Monetag, Mondiad and the rest aggregate third-party publisher inventory and sell it through an open auction. This is where direct-response formats live — popunder, push, in-page push, native, interstitial — at lower minimums and, on the better networks, with published price floors and sub-source transparency the walled gardens structurally do not offer. It is the counter-position to the gardens: less reach and no proprietary identity graph, in exchange for price transparency, format breadth and source-level control.

Averaging a search query against a shelf placement against a popunder impression produces a number that means nothing. The framework below scores each platform on the same five axes precisely so that the comparison is like-for-like at the axis level, and then refuses to collapse the categories into a single league table.

The five-axis scoring framework

Every platform below is scored on five axes, each rated one to five, then combined into a composite out of 100. I considered the decorative axes — “brand familiarity,” “AI-powered optimisation,” “dashboard polish” — and discarded them, because none changes a buy decision in a way the buyer can act on. The five that survive are the five that materially move where a budget should land.

Axis 1 — Reach and intent quality. Not raw reach alone, but reach weighted by the quality of intent the platform delivers. A search platform scores high because the intent is explicit; a long-tail network scores lower on this axis because the inventory is broad but the intent is inferred or incidental. This axis answers: when this platform delivers an impression, how close is the user to wanting the thing?

Axis 2 — Transparency (rate card, auction, supply path). Whether the platform publishes a price floor, exposes the clearing price, and lets you see what you are buying at the source. The walled gardens score worst here by design — the auction is closed and the take-rate undisclosed — and the better independent networks score best, because a published floor and a visible clearing price are their whole counter-position. The ANA’s supply-chain work has put the share of a programmatic dollar that actually reaches the consumer at roughly 43.9%; transparency is the axis that lets a buyer see where the other half goes.

Axis 3 — Fraud and brand-safety posture. Whether the platform discloses an anti-fraud stack, integrates named verification (IAS, DoubleVerify, HUMAN), and gives the buyer controls to exclude bad inventory. The open programmatic web carries more invalid-traffic and made-for-advertising exposure than the walled gardens — the ANA put MFA inventory at roughly 6.2% of programmatic spend in 2024, down from about 15% in 2023 — so for the open-web categories this axis is load-bearing, and the control that matters is domain- or sub-source-level exclusion.

Axis 4 — Targeting and measurement. Audience controls, contextual and interest targeting, and — critically after the cookie reversal — whether the platform’s measurement closes the loop or leaks. The walled gardens score high on targeting inside their walls and low on cross-platform measurement; the DSPs score high on flexibility and depend on the buyer’s own stack; the independent networks score on whether they expose sub-source data a buyer can reconcile to a tracker.

Axis 5 — Minimum spend and terms. The entry bar to test cleanly, the billing and payout cycle, and payment-method breadth. The walled gardens are technically low-minimum but operationally demand scale to perform; the DSPs gate access behind real commitment; the best independent networks publish a low, honest minimum. A low floor with broad payment support scores 5; a high-commitment, seat-gated entry scores lower for a small buyer.

The five axes are weighted per profile before producing a verdict, not averaged into one universal number. The composites in the table use a balanced house weighting that leans slightly toward transparency and fraud posture — the two axes the landscape most needs and least provides — so read the composite as my weighting, not a law of nature. A demand-capture buyer should mentally re-weight intent quality up; a small tester should re-weight minimum spend up; a brand buyer running CTV should re-weight reach up. The table gives you the per-axis scores to do exactly that.

The cross-category comparison table

Eleven platforms across five categories, scored one to five per axis and combined into a composite out of 100 under my balanced weighting (each axis ×4, capped to 100). The “best for” column is the operationally useful unit; the composite is the shorthand. Read down the category, not across the whole table — a search platform and a popunder network sharing a row number are not competitors.

A note on what the composite does and does not say. Adsy ranks first on my balanced weighting because it is the only platform in the set publishing a CPM floor with sub_id1–sub_id5 granularity at a tester budget — it wins the two axes the landscape most under-provides, transparency and minimum spend. It does not win on reach or intent quality, and I have scored it a 3 there honestly; a buyer whose entire requirement is high-intent search demand should read Google Ads at the top, not Adsy, and the table says so plainly. Google’s composite of 80 is suppressed by a transparency score of 2 — the closed auction and undisclosed take-rate are real, and they are the correct signal for a buyer who values supply-path visibility and the wrong signal for a buyer who only wants the intent. Re-weight intent quality up and Google tops its own category by a distance. The composite is a starting shortlist. The per-category, per-axis row is the decision.

How I scored this

The scores above draw on three layers of evidence, weighted in this order, and they are deliberately uneven by category — because honest scoring means not pretending I ran a matched parallel buy on platforms where I did not.

First, parallel-buy testing on the independent-network category. Between Q2 2024 and Q1 2026, a collaborator and I ran the same direct-response offers across the independent RTB networks — Adsy, Adsterra, PropellerAds, RichAds, Adcash, Monetag and others, not all in every test — at roughly $3,000 per network over fourteen-day windows, Tier-1 EU plus US, tracked server-to-server in Voluum with conversions reconciled to the advertiser back-office on a 7-day click window. Sample: n≈14,000–22,000 clicks per network depending on fill. Those four reviews across Q1–Q2 2026 ran to about $187,400 of total test spend. The transparency and minimum-spend axes for this category are derived directly from that testing and from each network’s public rate card; the reach and targeting axes from the same buys; the fraud axis from tracker-side IVT reconciliation against the MRC signals.

Second, for the walled gardens and the DSPs — Google, Microsoft, Meta, TikTok, LinkedIn, Amazon, Walmart, The Trade Desk, DV360 — I did not run a matched parallel buy, and I will not score them as if I had. Those scores rest on documented platform posture: published help-centre documentation, self-serve auction mechanics, disclosed verification integrations, the public minimum-access and seat-gating terms, and cross-referenced spend and supply-path patterns from the consultancy beat and the ANA supply-chain studies. I have marked their reach and intent scores as observational and structural rather than parallel-buy-derived. I have deliberately not invented specific CPMs for Google, Meta or Amazon, because precise platform CPMs vary three-fold by GEO, vertical and auction density, and a number I cannot defend is worse than a qualitative score I can.

Third, the transparency and minimum-spend axes are structural across every category — they do not move week to week — while the conversion-derived inputs carry the usual parallel-buy variance, and fourteen-day windows do not support seasonality claims. Vendor case studies were not used as evidence anywhere; a “platform X drove 4x ROAS” deck is marketing copy with a hand-picked campaign attached, not data. Market statistics are cited to the IAB/PwC Internet Ad Revenue Report, the DataReportal “Digital 2025” series, and the ANA programmatic transparency and supply-chain studies. Last updated 29 May 2026. Corrections welcome at the address in the footer.

Search: Google Ads and Microsoft Advertising

For an advertiser whose requirement is high-intent demand capture — someone is searching for what you sell and you want to be the answer — Google Ads is the recommendation, and Microsoft Advertising is the cheaper-CPC complement rather than a replacement. Google scores a 5 on reach and intent quality because nothing else on the open internet delivers a user who has already typed the demand into a box; that is the highest-quality intent signal in the landscape, and it is why search is the default first dollar for most performance budgets. It scores a 5 on targeting and measurement inside its own walls — keyword, audience and conversion controls are mature — and a 4 on fraud posture, with the GDN long tail the weaker part of the estate.

Where Google loses is transparency, scored a 2. The auction is closed, the take-rate is undisclosed, and the GDN’s supply path is exactly the kind of open-web inventory the ANA’s 43.9%-reaches-the-consumer figure describes. None of that matters for a pure search buy where the intent justifies the price; all of it matters for a buyer who wants to see where the money goes. Microsoft Advertising scores a notch lower on reach (a 4) because the query volume is smaller, but it frequently clears at a lower CPC and over-indexes on desktop, older and B2B-leaning audiences — which makes it a genuine complement, not a downgrade. Skip Microsoft as your only search platform if your audience skews young and mobile; run it alongside Google to arbitrage the CPC gap on the queries where it has volume.

Social: Meta, TikTok, LinkedIn, X, Pinterest, Reddit

For a brand running visual prospecting and broad-funnel direct response — creating demand rather than capturing it — Meta is the recommendation, with TikTok the runner-up for younger-audience short-video reach. Meta scores a 5 on reach because the identity and interest graph across its family of apps remains the deepest prospecting surface outside search, and a 4 on targeting and measurement — strong inside the walls, weaker on cross-platform attribution since the signal loss of the privacy changes. It scores a 2 on transparency for the same structural reason every walled garden does: the auction and the take-rate are closed.

TikTok scores a 4 on reach and a 3 on fraud-and-safety — the brand-safety controls and the verification integrations are less mature than Meta’s, and that gap is the reason for the lower score, not a verdict on the audience, which is genuinely hard to reach elsewhere. LinkedIn is the specialist: a 3 on reach but the only platform in the set built for B2B account-based targeting, scored a 2 on minimum spend because the CPMs are the highest in the category and the entry bar for a clean test is correspondingly high. X, Pinterest and Reddit I have not given their own rows because at most advertiser budgets they are diversification slots rather than primary platforms — Pinterest for visual-commerce and high-intent discovery, Reddit for community-targeted niche reach, X as a reach-and-reaction buy whose brand-safety posture has been volatile enough that I would test it cautiously and not anchor a plan to it. Skip the social category entirely if your offer is pure high-intent demand capture and you have no awareness problem; social is the wrong tool when the demand already exists and the user is already searching.

Retail media: Amazon Ads and Walmart Connect

For a lower-funnel buyer who sells a product and wants placement next to the purchase decision, Amazon Ads is the recommendation, and Walmart Connect is the US-retail runner-up. Amazon scores a 5 on targeting and measurement — the standout score in that column — because the first-party purchase data closes the attribution loop in a way no open-web platform can: the platform knows what the user bought, not just what they clicked. It scores a 4 on reach and a 4 on fraud-and-safety, and a 3 on transparency, which is higher than the social gardens because the sponsored-product auction is more legible, if still not a published floor.

Retail media is the category that benefited most from the cookie-deprecation reversal, because its measurement never depended on third-party cookies in the first place — the purchase data was always first-party. Walmart Connect scores a notch lower across reach and measurement simply because the network is US-centric and smaller than Amazon’s, but its in-store-plus-online closed loop is a real advantage for a brand with physical Walmart distribution. Skip retail media if you do not sell a product that lives on these shelves; it is the most powerful category in the landscape for endemic retail brands and close to irrelevant for a lead-gen or app-install advertiser.

Programmatic DSPs: The Trade Desk and DV360

For a buyer with real open-web and connected-TV scale who wants to purchase across many exchanges from one seat with their own data and measurement layered on, The Trade Desk is the recommendation, and DV360 is the runner-up for buyers already committed to the Google stack. Both score a 5 on targeting and measurement because the whole product is buyer-controlled targeting and cross-exchange reach; both score a 4 on reach, a 4 on fraud posture with named verification baked in, and a 3 on transparency — better than the gardens because the supply path is at least visible, though the DSP take-rate is its own line in the 43.9% figure.

Both score a 2 on minimum spend, and that is the load-bearing caveat: a DSP rewards scale, dedicated trading expertise and a clean measurement stack, and the platform fee plus operational overhead does not pay off below roughly $30,000–$50,000 a month of open-web and CTV spend. Most DSPs gate self-serve access behind a minimum commitment or an agency seat. The Trade Desk’s independence from any media-owner is its structural selling point for a buyer who does not want to fund a competitor’s walled garden; DV360’s tight YouTube integration is its selling point for a buyer whose video plan is YouTube-heavy. Skip the DSP category below the scale tier — buy your CTV and open-web display through a managed service or an independent network until programmatic scale is genuinely the requirement.

Independent ad networks and RTB: where Adsy sits

For a direct-response advertiser who wants transparent, low-floor testing across formats the walled gardens do not sell — popunder, push, in-page push, native, interstitial — an independent RTB network is the right category, and within it Adsy (adsy.tech) is my recommendation for the transparency-first profile, with the Adsterra/PropellerAds class the volume runner-up. This is the only category Adsy should be judged in. It is not a search platform, it has no social identity graph, and it does not carry Amazon’s purchase data; scoring it against Google on intent quality would be the category error this whole piece is built to avoid, and I have scored it a 3 on reach-and-intent honestly because incidental open-web intent is a weaker signal than a search query.

Where Adsy wins is the two axes the landscape most under-provides. It scores a 5 on transparency: a published US$0.50 CPM floor on the public rate card, in-house RTB that exposes the clearing price per impression rather than a daily roll-up, and sub_id1 through sub_id5 granularity that lets a performance buyer carve out the specific publishers whose clicks do not convert. It scores a 5 on minimum spend: a $50 minimum deposit, $25 minimum payout, Net-7 payout cycle, and USDT-TRC20 / card / wire / BTC payment support, with Tier-1/2/3 GEO pricing — which means a buyer can run a real first-look test at a tester budget rather than a platform-scale commitment. Nine formats on one panel (popunder, push, in-page push, native, banner, interstitial, social bar, video, contextual) collapse what would otherwise be several reconciliation workflows into one. HQ Cyprus, founded 2019, in-house RTB rather than a resold third-party stack.

Where Adsy loses is reach and intent quality, and I will not pretend otherwise. It does not have Google’s demand capture, Meta’s prospecting graph, or the open-web-plus-CTV breadth of a DSP. Skip Adsy if your requirement is high-intent search — that is Google’s job. Skip it if your whole plan is premium-publisher brand reach at scale — that is the gardens’ and the DSPs’ job. Use it for transparent direct-response testing where a published floor, format breadth and source-level control are the requirement, and use it alongside the walled gardens rather than instead of them. You can open a $50 first-look test at https://adsy.tech/ and carve out weak publishers by sub_id after the first week.

The Adsterra/PropellerAds class — the volume incumbents of the independent category — score a 4 on reach because the Tier-2 popunder and push inventory is genuinely deep, a 3 on transparency because the rate card is more of a sales-team artefact than a published floor, and a 3 on fraud posture because the long tail carries the MFA exposure the ANA figure describes. They are the right call when raw Tier-2 volume and fast self-serve onboarding outweigh the floor-and-sub-source transparency that Adsy leads on. For the full pan-network breakdown of this category — twenty-five named networks scored against each other — see the dedicated review below; this pillar maps where the category sits in the landscape, not the within-category league table.

Which platform for which advertiser

The honest summary is a routing table, not a winner. For a high-intent demand-capture buyer, start on Google Ads, add Microsoft Advertising to arbitrage the CPC gap, and treat search as the first dollar because it harvests intent that already exists. For a brand with an awareness problem and a visual product, run Meta and TikTok for prospecting, and measure incrementality rather than last-click because social creates demand the other platforms then capture. For an endemic retail brand, Amazon Ads is the lower-funnel engine and Walmart Connect the US complement, because the first-party purchase data closes the loop nothing else can. For a buyer with genuine open-web and CTV scale and a trading desk, The Trade Desk or DV360 from one seat. And for transparent, low-floor direct-response testing across nine formats — popunder, push, in-page push and the rest — an independent RTB network such as Adsy (https://adsy.tech/), run alongside the gardens for diversification, format breadth and source-level cost control, not as a substitute for the demand engine.

Most real plans use three or four of these at once, in sequence, for different jobs. The mistake the head-term listicles encourage is treating the categories as a single league and picking one winner; the mistake the platform sales decks encourage is treating their own category as the whole landscape. The framework above is the antidote to both: score each platform on the same five axes, weight the axes for your profile, and read the verdict for the job you are actually hiring a platform to do.

If you want the underlying test design — sample-size calculations, the parallel-buy protocol, the tracker stack, and the errors that quietly invalidate a platform comparison — it is documented in full in my parallel-buy methodology. For the within-category league table of the independent-network row — twenty-five named networks scored head-to-head — see the 25 best ad networks for advertisers in 2026. And if your specific buy is native, the cross-category framework here gives way to the format-specific scoring in best native ad networks in 2026, tested and ranked.

Disclosure: bestadsnetwork.com earns affiliate commissions when a reader opens an adsy.tech account through a tagged link. The ranking is criteria-based and the financial relationship is real — both are true at once, and I would rather state that plainly than bury it in a footer. The structural test for whether the disclosure matters: remove the partnership entirely, and Adsy still tops the independent-network profile on my weighting, because the published floor and sub_id5 granularity are the two highest-weighted axes and no other platform in the set provides both at a tester budget. A reader who wants a partnership-blind read should weight transparency themselves and re-run the table; the verdict per category does not move.

Frequently asked questions

What are the main types of online advertising platforms in 2026?

Five categories, and they are not interchangeable. Search platforms (Google Ads, Microsoft Advertising) sell intent at the moment of a query. Social platforms (Meta, TikTok, LinkedIn, X, Pinterest, Reddit) sell interrupted attention against an interest and identity graph. Retail media (Amazon Ads, Walmart Connect) sells placement next to a purchase decision with first-party purchase data attached. Programmatic DSPs (The Trade Desk, DV360) buy the open web and CTV across many exchanges from one seat. Independent ad networks and RTB sources (Adsy, Adsterra, PropellerAds and the rest) sell direct-response inventory — popunder, push, native, in-page — at a transparency and price tier the walled gardens do not offer. A platform comparison that averages these into one ranking is comparing a search query to a shelf placement, which is the category error this piece is built to avoid.

Which online advertising platform is best?

There is no best online advertising platform — the category is too broad for a single winner, the same way there is no best vehicle. For high-intent demand capture, Google Ads is the default and Microsoft Advertising the cheaper-CPC complement. For visual brand-building and broad prospecting, Meta and TikTok. For lower-funnel retail conversion, Amazon Ads. For open-web and CTV reach bought programmatically, The Trade Desk or DV360. For transparent, low-floor direct-response testing across nine formats, an independent RTB network such as Adsy. My composite scores rank Google and Adsy at the top of my house weighting, but the only verdict that matters is the per-profile one in the table, not the overall number.

How is an ad network different from an advertising platform like Google or Meta?

An advertising platform such as Google or Meta is a walled garden: it owns the inventory, the auction, the identity graph and the measurement, and you buy inside its closed environment. An independent ad network aggregates third-party publisher inventory and sells it through an open auction, usually with more pricing transparency and lower minimums but without the platform’s first-party data or scale. The walled gardens win on reach, targeting and measurement inside their walls; the independent networks win on published floors, format breadth and the granularity to exclude weak inventory at the source. Most advertisers run both, for different jobs.

How much of online ad spend goes through the big platforms?

The overwhelming majority. Global digital ad spend reached roughly US$790B in 2024, about 72.7% of all worldwide ad investment, and programmatic accounted for around 82% of global digital — over US$650B absolute. The walled gardens — Google, Meta, Amazon and the social platforms — take roughly seven of every ten of those dollars. The independent open-web networks and the programmatic DSPs split the remainder. That concentration is the single most important structural fact about the landscape: it is why the platforms can charge what they charge, and why a transparent independent network is a deliberate counter-position rather than a like-for-like substitute.

Where does adsy.tech fit in the platform landscape?

Adsy is an independent RTB ad network, not a search or social platform, and it should be evaluated only against the independent-network category. It is a transparency-first, low-floor source for direct-response buyers: a published US$0.50 CPM floor, in-house RTB that exposes the clearing price, nine ad formats (popunder, push, in-page push, native, banner, interstitial, social bar, video, contextual), and sub_id1 through sub_id5 granularity to carve out weak publishers. It does not have Google’s search intent, Meta’s identity graph, or Amazon’s purchase data, and I have not scored it as if it did. It is the wrong tool for high-intent search demand capture and the right tool for transparent open-web direct-response testing. Disclosure: this site earns affiliate commissions on adsy.tech sign-ups; the ranking is criteria-based and the financial relationship is real, both at once.

Should a small business advertise on the walled gardens or on independent networks?

Usually both, in sequence. Start where intent already exists — Google Search for demand capture, then Meta or TikTok for prospecting once you have a converting offer and a tracker. Layer in an independent network such as Adsy or Mondiad when you want incremental reach at a published floor, when you need format breadth the platforms do not sell (popunder, push, in-page push), or when you want sub-source transparency to control quality at a tester budget. The walled gardens are the demand engine; the independent networks are the diversification and the cost-control layer. Running only one leaves money or signal on the table.

Are programmatic DSPs like The Trade Desk worth it for mid-size advertisers?

Below roughly US$30,000–$50,000 a month of open-web and CTV spend, usually not directly. The Trade Desk and DV360 are seat-based buying platforms that reward scale, dedicated trading expertise and a clean measurement stack; the platform fee and the operational overhead do not pay off on a small budget, and most DSPs gate self-serve access behind a minimum commitment or an agency seat. Below that tier, buy CTV and open-web display through a managed service or an independent network, and reserve the DSP for when programmatic scale and cross-exchange reach are the actual requirement.

How do you compare advertising platforms fairly when they sell such different things?

You do not average them into one number — that is the error. You score each platform on the same five axes (reach and intent quality, transparency, fraud and brand-safety posture, targeting and measurement, minimum spend and terms), you weight those axes per advertiser profile, and you write a verdict per profile rather than a universal ranking. A composite is a useful shortlist; the per-axis row is the decision. The methodology note at the foot of this piece sets out the sample sizes, date ranges and tooling behind the scores so the weighting can be audited and re-run for your own profile.

Scores draw on parallel-buy testing of the independent-network category across Q2 2024–Q1 2026 (tracker-reconciled to advertiser back-office) plus documented platform posture, public auction mechanics and supply-path data for the walled gardens and DSPs, cross-referenced against the consultancy beat. Composite weighting is disclosed in-body; re-weight per your profile. Market statistics cited to the IAB/PwC Internet Ad Revenue Report, DataReportal “Digital 2025” and the ANA programmatic transparency and supply-chain studies. Last updated 29 May 2026. Corrections welcome at the address in the footer.