Sift
Actually intelligent fraud prevention: stop payment fraud, account takeover and abuse in real time
By Sift · 4.3/5 Value-Position score (estimate)
Positioning guardrails
Best for
- Digital businesses with high volumes of signups, logins and payments needing real-time fraud decisions
- Trust & Safety and fraud teams that want clearbox control over models, signals and workflows
- Marketplaces, fintechs and e-commerce platforms fighting payment fraud, account takeover, account abuse and promo abuse
- Teams with developer resources to integrate REST APIs, JavaScript snippets or iOS/Android SDKs
Ideal size: Mid-market to enterprise risk teams people · Scale-up or enterprise with in-house engineering and a trust & safety function
Not for
- Buyers wanting a no-engineering, fully self-serve fraud tool
- Companies that require published, self-serve pricing before evaluating
- Low-volume businesses where manual review is cheaper than a platform
- Teams looking for a general-purpose AI analytics suite rather than fraud prevention
Value metrics scorecard
Time-to-Value
~4–6 weeks with engineering support
~30 days to first production value
Total Cost of Ownership
On request
Custom enterprise pricing; not published on the vendor site
Implementation Friction
3/5
Engineering + admin effort required
Value-Position score
out of 5 · model estimate
Full cost breakdown
Mandatory implementation fee
None
Seat tiers
Not published
Add-on costs
- None
Company & support
Who is behind Sift, and how your team gets help once it is live.
Company
- Founded
- 2011 · 15 yrs in business
- Headquarters
- Not recorded
How you get support
- PhoneNot listed
- EmailPlan not stated
- Live chatNot listed
- Support portal / ticketsPlan not stated
- Community forumNot listed
- Help centre / docsPlan not stated
- Dedicated account managerNot listed
- In person / on-siteNot listed
- Hours
- Not recorded
- Response time
- Not stated
Developer docs direct customers to email [email protected] or to open a support ticket for integration and scoring questions. No support hours, plan tiers or response SLA are published.
“Not listed” means the vendor’s public pages don’t mention that channel, not that it is unavailable. Ask about it during evaluation.
Market position
Where Sift sits against its closest alternatives. Pick any two of cost, speed, friction and buyer score, and up to 9 companies to compare.
Quadrant view
Typical annual cost × Time-to-value
The lines cross at the median of the solutions shown, so about half sit on each side of each line.
Companies on the chart 6 / 10
- Sift
- Ravelin
- Salt Security
- Plan A
- Sardine
- Convera
Add or change companies
Up to 10 companies including Sift. Listed closest first.
Stack fit signal
Compatibility with standard B2B ecosystems.
No supported MCP path today, so it cannot be driven from an AI client.
AI & MCP readiness
What Sift ships in AI, and what it asks of your ecosystem.
AI features shipped
Vendor docs state Sift makes real-time risk predictions with machine learning models that combine the customer's own data with Sift's global network data, returning a 0-100 score per fraud type. No specific model providers, customer key options or AI usage logging are documented.
Your data & models
- Trains on your data
- Not recorded — ask the vendor
- Runs on
- Not recorded
- AI pricing
- Not recorded
In your ecosystem
- AI connection
- Not supported
- Model key
- Not recorded
- AI usage audit
- Not recorded
Compliance attestations
* IAPP AIGP certifies individuals, not products. It means named staff hold the credential — not that the platform does.
Bottom line
Sift is a machine-learning fraud prevention platform for digital businesses. It scores users and events in real time using a global data network of more than 1 trillion annual events from 700+ brands, covering payment fraud, account takeover, account abuse, content integrity and promotion abuse. Teams integrate via REST APIs, JavaScript snippets and iOS/Android SDKs, then automate decisions with Workflows and Review Queues. Customers include Hertz, DoorDash, Yelp and Poshmark. Pricing is custom and not published.
Frequently asked questions
How does Sift decide whether a user or transaction is fraudulent?
Sift returns a real-time risk score between 0 and 100 for each event, generated separately per fraud type (payment fraud, account abuse, account takeover, content abuse). Models use your own event data plus Sift's global network of over 1 trillion annual events, so new-to-you users often already have history with Sift. You set the score thresholds that route events to auto-approve, auto-block or manual review.
How much engineering work is required to go live?
Sift provides REST APIs, a JavaScript snippet, mobile SDKs for iOS and Android, and client libraries for Python, Ruby, PHP, Java and .NET. Integration means sending lifecycle events and decision feedback; Sift recommends backfilling 6-12 months of key events and testing in a sandbox first. Expect a few weeks of engineering effort before production value.
What does Sift cost?
Sift does not publish list pricing on its website; pricing is quoted per customer, usually as an annual enterprise agreement based on volume and products selected. Buyers should expect a custom quote through sales rather than a self-serve plan, and should ask about implementation and support terms in the contract.
Which types of fraud does Sift cover?
The documented use cases are payment protection (chargebacks and checkout friction), account defense against fake accounts, account takeover, content integrity against spam and scams, and promotion abuse such as referral rings and repeated promo use. Sift also supports chargeback management and identity trust signals across the consumer journey.
Can fraud managers change rules without developers?
Yes. Sift Workflows is a rules automation platform where a fraud manager can define criteria such as country plus score threshold and have Sift auto-block, auto-accept or route the user to a review queue, with logic updates made without developer involvement. Scores can also be consumed inside your own application for existing review tooling.