Sift Science

What makes Sift Science different

Sift distinguishes itself from hyperscalers by offering a specialized, verticalized fraud prevention platform rather than general-purpose infrastructure. While AWS, GCP, and Azure provide raw compute and storage, Sift delivers a pre-trained machine learning engine specifically optimized for digital trust and safety. Its core differentiator is the “global data network,” which aggregates signals from over 700 enterprise customers to identify emerging fraud patterns in real-time. This cross-industry learning capability allows Sift to detect sophisticated attacks like account takeover (ATO) and synthetic identity fraud faster than generic rule-based systems or custom-built ML models hosted on public clouds.

The platform is designed for speed and ease of integration, primarily through its Sift Score API. Instead of requiring data scientists to build and maintain complex fraud models from scratch, Sift provides a hosted solution that processes transaction data in milliseconds. It combines behavioral biometrics, device intelligence, and historical event data to assign a risk score to every user action. This “security-as-a-service” approach reduces the operational burden on engineering teams, allowing them to focus on product development while Sift handles the continuous model updates and threat intelligence.

Pricing model

Sift does not publish transparent, tiered pricing on its website. The model is custom and typically based on usage volume (e.g., number of API calls or transactions processed) and the specific solutions deployed (Payment Protection, Account Defense, etc.). Enterprises generally engage with sales to negotiate rates based on their scale and fraud loss reduction targets. This contrasts with the pay-as-you-go transparency of AWS or GCP, requiring direct sales engagement for cost estimation.

When it fits

• High-volume digital commerce: E-commerce platforms processing thousands of transactions daily need real-time fraud scoring to minimize chargebacks without blocking legitimate customers.
• Fintech and banking: Institutions requiring robust Account Takeover (ATO) prevention and compliance with regulations like PSD2/SCA.
• Marketplaces and gig economy: Platforms like Patreon or Hertz that need to balance user growth with protection against first-party abuse and payment fraud.
• Teams lacking specialized ML resources: Organizations that want enterprise-grade fraud prevention without hiring a dedicated team of data scientists to build and maintain models.

When it doesn’t

• General-purpose cloud workloads: Sift is not a substitute for compute, storage, or networking infrastructure.
• Non-fraud security needs: It does not provide general cybersecurity services like DDoS protection, identity management (IAM), or network security groups.

Inclusion criteria

• Self-service signup: Available via the Sift Console (console.sift.com).
• Public SLA/status page: A system status page is available at status.sift.com.
• Transparent pricing: Not publicly disclosed; requires sales contact.