What Fake Signups Actually Cost You

For a mid-size SaaS taking 20,000 signups a month with an 8 percent disposable rate, the cost of fake signups comes to at least $14,528 a year on the lines you can price directly: wasted sending, burned support time, and abused trial infrastructure. That figure deliberately leaves the two most expensive lines, corrupted metrics and your standing with your email service provider, unpriced, because pretending to put a dollar figure on those would be fiction. This article builds the model line by line, walks the worked example to its total, and then hands you the calculator to run it with your own numbers.

How many of your signups are fake?

Across real signup forms, the disposable share typically runs from about 3 percent on a plain newsletter form to 12 percent or more when something valuable sits behind the submit button: a free trial, credits, a coupon, a gated download. Fifteen years of looking at other people's lists has taught me the rate tracks the incentive, not the industry. The supply side explains why the range never drops to zero. As of the September 2026 State of Disposable Email report, the dataset behind this article tracked 217,847 disposable domains, with 10,223 genuinely new ones arriving in a single 36 day window. And the detail that matters most for cost modeling: 9,971 of those 10,223 new domains still passed live MX validation on report day. Fake signups are not malformed addresses that bounce at the form. They are working mailboxes on the day they sign up, which is exactly why they get in, and why the costs below accrue quietly instead of announcing themselves.

For the rest of this piece, the worked example uses 20,000 signups a month at 8 percent disposable: 1,600 fake signups a month, 19,200 a year. Adjust everything to your own scale at the end.

What does wasted email spend actually cost?

Most email platforms bill by contacts, sends, or both, and a fake signup consumes each of them without ever becoming a reader. The dead ones hard bounce and, on a well-configured platform, get suppressed quickly. The quietly expensive ones are the disposables that were alive at signup: they sit on your list as permanently unengaged contacts, aging into your billing tier, receiving your welcome sequence and your campaigns, until an engagement-based cleaning finally catches them. Assume each fake contact survives on the list for six months on average before hygiene removes it, which is generous if you clean quarterly and optimistic if you do not. At 1,600 new fakes a month, that is a standing population of about 9,600 phantom contacts, and at a blended platform cost of $0.015 per contact per month, the disposable email cost on this line alone is $1,728 a year. Small next to the lines below, which is worth noticing: the sending waste everyone thinks of first is usually the cheapest item on the bill.

How much ops and support time do fake signups burn?

Most fake signups cost zero human minutes individually, which is how this line hides. The cost arrives in batches: bounce triage after a campaign, list hygiene sessions, reviewing flagged accounts, investigating the referral program that suddenly minted fifty new users from one browser, deleting review spam. Model it conservatively: 5 percent of fake signups eventually consume about five minutes of someone's time. On 19,200 fakes a year, that is 4,800 minutes, 80 working hours, and at a loaded cost of $40 an hour, $3,200 a year. That is two full working weeks of a real person's year spent janitoring signups that were never people, and if your product involves any manual review, KYC, or onboarding touch, your multiple is higher than this model's.

What do trial abusers cost in infrastructure?

This is the line where fake signups saas economics get expensive, because a disposable address attached to a free trial is not an empty row, it is a resource consumer with no accountability. Every trial that activates provisions something: compute, storage, sandbox environments, third-party API calls, and increasingly, metered AI features whose unit costs are very real. One person with a throwaway provider is an unlimited supply of fresh identities, so the same abuser returns for a new trial, new credits, new coupon, as many times as your form lets them. Model it modestly: 25 percent of fake signups activate a trial and consume $2 of infrastructure and credits before expiring. That is $9,600 a year in the worked example, the largest priced line in the model, and for products with generous compute or credit grants I have seen the true per-abuser figure land at ten times that assumption.

How do fake signups corrupt your metrics?

This line stays unpriced in the model, not because it is small but because its cost compounds through every decision you make downstream of the form. The mechanics are simple arithmetic. If 8 percent of signups are fake, your signup conversion rate is overstated, your cohort retention curves carry a phantom cliff of users who were never going to return, and your acquisition math bends: a reported $25 cost per signup is really $27.17 per human signup, because the spend divides by 92 percent of the denominator you thought you had. Every channel comparison, every landing page test, and every growth report inherits the distortion, and the channels most polluted by fakes will look like your best performers, which quietly steers budget toward exactly the wrong places. You are optimizing a funnel that is partly fiction, and no dollar figure I could invent would capture what that misallocation costs over a year of decisions.

Where does the deliverability risk really sit?

Let me name this one precisely, because it is the most misdiagnosed cost in the whole model. Fake signups do not damage your sending reputation at Gmail, Outlook, or Yahoo. Mailbox providers judge you on how their own users receive and engage with your mail, and a disposable domain has no users at any of them. The risk sits one layer closer to home, at your email service provider. When the throwaway domains you accepted go dark and your next campaign hard bounces against them in a batch, that bounce spike is one of the strongest list quality signals platforms like Mailchimp and SendGrid police. The consequences escalate from warnings to throttles to forced list cleanings to a suspended account, and a suspension that lands mid-launch costs you the send, the revenue behind it, and days of remediation. This line also stays unpriced in the table, because it is a risk with a fat tail rather than a monthly invoice. That is not a reason to ignore it; it is the reason the priced total below should be read as a floor.

What does it add up to for a mid-size SaaS?

Here is the worked example in one place: 20,000 signups a month, 8 percent disposable, 19,200 fake signups a year, every assumption stated so you can argue with it.

Cost line	Assumption	Per fake signup	Annual cost
Wasted email spend	Fakes persist 6 months on the list at $0.015 per contact per month	$0.09	$1,728
Ops and support time	5% of fakes consume 5 minutes at $40 per hour loaded	$0.17	$3,200
Trial infrastructure burned	25% activate a trial consuming $2 of compute and credits	$0.50	$9,600
Corrupted metrics	8% inflation through conversion, CAC, and cohorts	not priced	not priced
ESP standing risk	Bounce spikes inviting compliance action	not priced	not priced
Priced total		$0.76	$14,528

Read the total for what it is: roughly 76 cents per fake signup, $14,528 a year, with the two worst lines left off the invoice on purpose. If your instinct is that some assumption is too high for your business, halve it and the total still clears $7,000 a year for a product this size. If your instinct is that the trial line is too low because your product hands out real compute, you are probably right, and that is the line to correct first.

How do you run these numbers for your own product?

With your own inputs, because the model above is honest but generic, and your signup volume, disposable rate, support costs, and trial economics are not mine to guess. The fake signup cost calculator at isitdisposable.com is the personalized version of this article: every line from the model is an input you can change, and every input is encoded into the page URL, so the result is a shareable link rather than a screenshot. Run your numbers, copy the address bar, and the exact scenario lands in your budget discussion or your Slack thread with the assumptions visible and adjustable, which is precisely how a cost claim should travel. Here is this article's scenario as a prefilled starting point: the volume and disposable rate are set, the cost lines start at the calculator's defaults, and every one of them is yours to correct. However the numbers land, the conclusion of the model is the same at every scale I have run it: the cost of fake signups is not one dramatic line item, it is five quiet ones, and four of them are billing you already.