Rupt vs. Sift
Sift is an enterprise fraud suite sold by quote. Rupt is account protection you can turn on today at a public price.
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Book a 20 minute demo. We walk through your use case on real evaluations and price out your volume.
The short version
Updated August 2026
- Sift is an enterprise ML fraud decisioning platform focused on payment fraud, with ML models trained and optimized for case reviews and fraud ops teams.
- Pricing is not public and buying starts with a sales cycle. Adopting it means instrumenting Sift's event schema, and staffing a review team queues for the structure it assumes. Product abuse like scraping, bots, and account sharing sits outside its payment and content focus.
- Rupt is a full fraud engine. It includes fingerprinting, email and phone intelligence, a rules engine, hosted challenges, and an AI agent that investigates accounts and monitors for new patterns.
- Rupt's pricing is public and starts at $99 a month with 20,000 evaluations included. Sift is quote only, with a median contract of $150,000 a year in Vendr's data.
Capabilities.
Checked against both products' public pricing and docs, August 2026. Sift publishes no pricing, so contract figures come from Vendr.
Why choose Rupt.
A full fraud engine, end to end
Identification, intelligence, decisioning, and enforcement are one product, not four you wire together. One evaluation returns the fingerprint, the signals, the scored risks, and a verdict, and Rupt acts on that verdict. Nothing in the middle is left for you to build and then maintain.
100ms at p99
Not an average that hides a long tail. 99 out of 100 evaluations come back inside 100ms, which is what lets you put Rupt directly in the login and signup path instead of running it after the fact.
Risks you can actually build
Rupt ships with its own risks ready to go, and you can compose your own from individual indicators with your own weights and your own actions. That is real customization, not tuning a threshold on somebody else's score.
Unmatched support
Integration setup help, developer meetings, and direct Slack and email access, with replies in hours, on every plan. Most vendors reserve that for their top tier. Don't take our word for it though, just ask our customers.
State of the art AI
The agent protects and detects. It watches your traffic for new and emerging fraud patterns as they form, then tells you what it found and what to do about it, down to the policy changes and updates it recommends you make.
A challenge engine that's a whole journey
Not a single screen bolted onto a verdict. Challenges are customizable and built around an end goal, whether that's adding friction, converting a sharer into their own account, or stopping a takeover, with cooldowns and velocity controls shaping how and when someone gets challenged.
Single-vendor pricing
Fingerprinting, email and phone intelligence, rules, and challenges usually mean a vendor and an invoice each. Because Rupt ships them together, the bundle costs less than the sum of the point tools.
Adopting Rupt alongside Sift.
For Sift customers this usually isn't a rip-and-replace migration. The two products meet different layers, so the sane path is to run them together and let the data decide what consolidates.
- 01
Turn Rupt on at signup and login while Sift keeps payments. Load the client SDK, call evaluate() on the actions you care about, and data starts flowing the same day. Nothing about your Sift integration changes.
- 02
Read evaluations server-side. One GET request returns the verdict, scored risks, fingerprint, and device ID for every login and signup.
- 03
Run policies in observe mode for two to four weeks. Compare what Rupt flags on account traffic against what reaches Sift's queues, and tune before enforcing anything.
- 04
Consolidate where the numbers point. Plenty of teams keep Sift on payments and let Rupt own the account layer. If account fraud turns out to be the bigger bleed, the six-figure line item is the one to revisit.
Frequently asked questions.
See Rupt on your traffic.
Book a demo and we'll walk through your use case, show you the signals on real evaluations, and price out your volume.
- A walkthrough tailored to your use case.
- How detection, the rules engine, and challenges fit your stack.
- Pricing and a rollout plan for your volume.