Compare

Sardine vs Sift

Both score fraud risk in real time; Sardine leads with behavioral biometrics and covers KYC/AML, Sift leads with its shared consortium network's scale.

Side by side

	Sardine	Sift
Vendor	SardineAI Corp.	Sift Science, Inc.
Pricing model	Quote only	Quote only
Free tier	—	—
Deployment	Cloud	Cloud
Open source	No	No
Best for	Fintechs and banks that need onboarding, payment fraud and AML monitoring in one platform, weighted toward behavioral-biometric detection.	Digital businesses that want fraud scoring backed by a large shared consortium network rather than building models from their own data alone.
Pricing	Enterprise pricing quoted per customer based on volume and modules used; no public rate card. Pricing has not been verified yet — see the vendor's site.	Usage-based enterprise pricing scaled to event/transaction volume; rates are negotiated per account and not published. Pricing has not been verified yet — see the vendor's site.
Features	Behavioral biometrics (typing, mouse, touch patterns) Device fingerprinting and emulator detection IP/location de-anonymization ('True Piercing') KYC/KYB identity verification and document checks Payment fraud detection across ACH, wires, cards and RTP On-chain/crypto transaction risk scoring Connections graph for network/link analysis Sanctions and PEP screening with case management	Device fingerprinting and identification Behavioral monitoring across signup, login and transaction events Cross-customer consortium data network Proprietary ML risk scoring with explainable signal breakdowns Customizable rules engine and automated decision workflows Analyst case queue and investigation workflows Chargeback and account-abuse protection Real-time scoring API

Verdict

Sardine and Sift both return a real-time risk score for a user or transaction, combining device fingerprinting with proprietary machine-learning models, for fintechs and digital businesses that need to catch fraud without a large in-house data-science team.

The primary signal each leans on is different. Sardine's differentiator is behavioral biometrics — analyzing typing, mouse and touch patterns during onboarding and transactions to catch social engineering, scripted bots and coerced "scam" behavior — plus a de-anonymization technique for masked IPs and locations, and it extends into KYC/KYB onboarding, payment fraud and on-chain crypto risk in one platform. Sift's differentiator is scale: its models are trained on events from its entire customer base, billions of devices and identities, on the theory that a fraud pattern seen at one merchant is often relevant to the next, and it exposes "clearbox" explanations of which signals drove a given score.

Choose Sardine if

You need onboarding KYC/KYB, payment fraud and AML monitoring in one platform rather than stitching several vendors together.
Behavioral-biometric detection of bots and coerced/scam behavior is central to your risk problem.
You handle crypto or on-chain activity and want that risk scoring included.

Choose Sift if

You want fraud scoring backed by a large shared consortium network rather than starting from your own transaction history alone.
Analysts need explainable, signal-level breakdowns of why a score was assigned, with a rules builder to act on it.
Your problem centers on payment fraud, account takeover and account abuse across signup, login and checkout, without a KYC/AML requirement.

The honest caveat

Both are quote-only with no published rate card, so cost comparisons have to happen in a sales conversation, not from the record. See how to choose a fraud detection tool for where narrower building blocks like Fingerprint fit alongside full platforms like these.

Last reviewed September 22, 2026

Install