Sift competitors and alternatives

The alternatives, briefly

Kaidn compared with Sift

A machine-learning fraud platform covering payments, account takeover, content abuse and disputes, sold to and staffed for large teams.

Sift is a serious product with a decade of payment fraud behind it, and if you are the buyer they built for, this page is not going to talk you out of it. A model trained across a large merchant network sees patterns that a smaller vendor structurally cannot, and that is a real advantage that no amount of positioning erases.

The question worth asking is whether you are that buyer, because a lot of teams shop the enterprise platforms by reflex and then discover the cost was not the licence.

The cost that is not the price#

They do not publish pricing. That is a deliberate choice and a reasonable one for their market, but it tells you what the buying process looks like: a call, a scoping exercise, a proposal, a security review, a negotiation.

For a company with a risk team and a procurement function, that is a Tuesday. For a four-person team losing money to signup abuse this month, the calendar cost is often larger than the licence cost, and the fraud continues throughout.

We publish prices and you can be scoring events before lunch. That is not a claim to be better. It is a claim to be appropriate at a different size.

A score you can argue with#

Their model is the product, and it is strong. The trade is that the reasoning lives inside it, and you interrogate it through their console with the tooling and the training that implies.

We return the checks that fired and what each was worth. When a user emails asking why they were blocked, the answer is in the response body, and the engineer reading it does not need to have been on a training course. When a rule is wrong, you can see which one and change its weight.

That matters most when nobody at your company is employed to be a fraud analyst, because the alternative to a legible verdict is not analysis, it is guessing.

Different fraud#

Their centre of gravity is payments: stolen cards, chargebacks, disputes. Ours is what happens before and after money moves for operators who run signups, rewards and payouts. Multi-accounting, bonus abuse, farmed registrations, payout fraud.

There is overlap, but the shape of the loss is different. A chargeback is a transaction you can trace. A bonus-abuse ring is forty accounts that each look completely ordinary in isolation and are only visible in the relationships between them.

What "you can argue with it" means in practice#

A verdict you can interrogate is not a slogan, it is a field. When a blocked user writes in, this is what the person answering has:

// `checks` comes back on the scoring response, so store it with the decision.
const decision = await db.decisions.findOne({ event_id: eventId });

for (const c of decision.checks) {
  console.log(`${c.check}  +${c.weight}  ${c.reason}`);
  console.log(`   ${c.message}`);
  console.log(`   evidence: ${JSON.stringify(c.evidence)}`);
}

ipRisk        +45  datacenter_ip
   IP is a datacenter/hosting address, not a residential user
   evidence: {"asn":"amazon"}
emailRisk     +35  disposable_email
   Email domain is a known disposable provider
   evidence: {"domain":"mailinator.com","is_disposable":true}
deviceReuse   +15  device_reuse
   This device is linked to 3 accounts, on different networks
   evidence: {"accountCount":3,"sameAsnAccountCount":1}

If that verdict is wrong for your traffic, the fix is visible: the weight on ipRisk is too high for a business whose users legitimately arrive through corporate VPNs, and you change it. On a model you would raise a ticket.

Splitting the two by event, not by vendor#

The migration question is usually posed as one product or the other, which is the wrong shape. The loss centres are different, so split by event:

// transaction path: a decade of chargeback data is hard to beat
const sift = await siftScore(order);

// funnel path: the loss is relational, not transactional
const { verdict, reasons } = await kaidn.score({
  event: "payout",
  user_id: user.id,
  ip,
  email: user.email,
  device_id: fingerprint,
});

Run that for a quarter and the numbers will tell you whether either side is redundant. That is a better decision procedure than a comparison table, including the one above.

Where we would send you elsewhere#

If chargebacks are your loss centre, they or Kount are the right shelf. If you want enterprise breadth with a gentler on-ramp, SEON sits between us on both price and weight. If account takeover specifically is the problem, Castle is built for it. And if you already have your own decision logic and just want signals, IPQualityScore is cheaper than all of us.