Rupt vs. SEON
SEON scores transactions and asks whether the person behind them is real. Rupt watches what happens to the account itself.
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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
- SEON is built around payment fraud: digital footprinting that enriches emails, phones, and IPs, ML scoring trained on historical transactions, and AML screening.
- Decisions come back as a fixed approve, review, or decline, and the ML improves by sending labeled outcomes back through a separate Label API. Challenges, account sharing detection, and enforcement not included.
- 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.
- SEON's Starter is $699 a month for 2,500 checks, about $0.28 each. Rupt starts at $99 for 20,000 evaluations, then $0.005.
Capabilities.
Checked against both products' public pricing pages and docs, August 2026.
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.
Migrating from SEON.
- 01
Swap the client snippet. Where you called SEON's init() and getSession(), load the Rupt SDK and call evaluate() on login, signup, and the actions you care about. Data starts flowing the same day.
- 02
Read evaluations server-side. Where you forwarded SEON's session payload to the Fraud API, one GET request now returns the verdict, risks, fingerprint, and device ID.
- 03
Rebuild only the rules that matter. SEON rules written against footprint signals won't map one to one, so recreate your device, IP, email, and velocity rules as Rupt policies and run them in observe mode against real traffic first.
- 04
Decide what stays. If AML screening is a compliance requirement, keep that module or a dedicated vendor, and let Rupt take the account abuse workload. The document verification step moves to a Rupt KYC challenge, selfie liveness or government ID plus selfie with deepfake detection built in. Cut over the fraud checks once observe mode agrees with your expectations.
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.