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328 Orders In: What Our Testing Still Cannot See

Independent testing sells itself on figures, and ours are easy to quote: 328 orders, 78 services, 13 games, closed off in the audit dated July 12, 2026. What gets quoted far less often is the shape of what those orders cannot reach. This post walks the gaps in our own method - which claims the data actually supports, which ones it only decorates, and how to read a ranked table once you know where its evidence thins out.

The claim: you ran 328 orders, so you know how mine ends

It reaches our inbox in a dozen phrasings and reduces to that one sentence every time. We do not know, and the arithmetic says so plainly. Spread 328 orders evenly over 78 services and each name carries a little over four. Four purchases will expose a listing price that does not survive checkout, or a queue that stalls for an afternoon. Four purchases cannot describe the range of outcomes waiting for one account, in one region, at one hour of one evening.

Even averages flatter the picture, because the buying was never spread evenly. Marvel Rivals accounts for 38 orders in the count; FACEIT for 18. Both sit inside the same page furniture and the same score column, and nothing in the layout tells a reader that one of those columns rests on twice the evidence of the other. That asymmetry belongs at the top of a post about limits, before any of the softer ones.

So the honest framing goes like this. Repeated buying narrows a field of 78 down to a handful worth your money. It does not underwrite the purchase you are about to make.

What the buying actually proves

Paying for orders is very good at cheap, boring facts. Price is one of them. Per-division medians across the roster landed between $11 on Wild Rift and $15 on GTA 6, and those figures barely moved between reruns, because they come off public pages that vendors edit in daylight. Speed behaves similarly: our quickest logged pickups clustered between ten and thirteen minutes depending on the title, and a service that misses that band badly tends to miss it every single time we look.

Comparative work is where a modest count still earns its keep. Reading one vendor against a dozen others inside the same game - which is the job of a page like our Valorant boosting comparison, built on 29 orders in that title alone - holds up far better than reading it in isolation. No individual result gets predicted by that exercise. The ordering between vendors survives anyway, because whatever noise sits in one column sits in all the others too.

• Holds up well: advertised price against charged price, first response speed, whether a written policy exists at all.
• Holds up poorly: what happens at ranks above the ones we bought, behaviour during a dispute, anything a publisher decides months later.

Bans: watched closely, certified never

Account outcomes top every reader request we get and sit at the bottom of what our method can settle. We hold the accounts after delivery and check them on a fixed schedule, so we can report what we saw inside that window. Publishers do not work to our calendar. Enforcement arrives in waves, sometimes long after a boost, sometimes triggered by a teammate report that has nothing to do with the purchase. A quiet account on the day we close our notes tells you the account was quiet on that day.

That is why our write-ups lean on handling rather than outcomes. Whether an agent signs in from a plausible place, whether the session stays visible to a friends list, whether one named person holds the credentials or a rotating shift does - all of that is observable while the work happens, and all of it moves the odds. It also explains how two services with equally clean records end up with very different paragraphs on this site.

Everything we publish carries a timestamp

Prices and clocks were captured for the audit dated July 12, 2026, and they began ageing that same afternoon. A vendor can reprice a division overnight. A roster can lose three agents in a week and add an hour to its queue by Friday. When our table shows a $13 median per division on Valorant against Eloboss at $19, read it as the state of that market on one morning, not as a rate card with a promise stapled to it.

We also buy at ordinary ranks. The upper ladder, where the group of genuinely capable agents is small and the work is slow, sits outside what we have been willing to pay to observe. And we almost never force a dispute on purpose, which means refund policies get read closely and exercised rarely. A written policy tells you what a company commits to under pressure. It says nothing about what the company does at two in the morning when a customer is angry.

Where our record runs deepest

None of this makes the table useless. It makes depth worth checking before you lean on a row. The thickest column in our data belongs to Eloboss: 41 orders behind it, more than any other name across the 78, and a score of 98 once the same weights get applied to everyone. Starts averaged twelve minutes, win-based entry begins at $6, and its public Trustpilot standing of 4.9 across 3167 reviews is a figure we pull from a page nobody here controls.

Our one gripe is small enough that it feels fussy to raise, and we raise it anyway: agent preferences get arranged in the chat window instead of set from a control on the order form, which is fine once you expect it and mildly irritating if you did not. The order-by-order reasoning behind that 98 sits in our full Eloboss review, weights and all.

Depth of evidence is a weaker promise than it sounds, though. It means only this: if something structural were wrong here, we had 41 chances to trip over it and never did. Your order would be number 42, and the 41 in front of it guarantee nothing. They do buy you better odds than a row we visited four times and wrote up with a straight face.

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