Kavra LabPOST /checkoutAllowedReturning customer on a trusted device
- Trusted device for this account
- Home broadband, matches history
- Human input, no automation
Risk6
Comparison
Sift alternative: explained request decisions, no event pipeline
Sift is a fraud platform that scores users and transactions with machine learning, based on events you send it (accounts, logins, orders, payments) and its network across many brands. Kavra assesses each request as it happens (a bot, a spoofed device or a real customer) and returns an explained recommendation from one script and one API call.
- Kavra approach
- Explained assessment of each request from device, network and behavior
- Sift approach
- ML score from events you send, plus a cross-brand data network
- Visitor friction
- Both invisible by default; both support step-up
- Best for
- Kavra: bots and spoofed devices. Sift: payment and dispute operations
What Sift does
Sift is a fraud prevention platform built around machine learning scores. Its platform page lists Payment Protection (real-time transaction decisions against stolen cards, card testing and alternative payment abuse), Account Defense (account takeover, fake accounts and identity fraud), the Sift Score API for teams that want Sift's intelligence inside their own models, and Expert Services for fraud strategy support. Sift has also published materials on Dispute Management for chargeback responses and on stopping fake listings and seller abuse on marketplaces.
Sift describes a global data network of more than 1 trillion events a year across 700+ brands, with the point that a user new to you is often not new to Sift. Payment Protection, according to Sift, returns a score in under 150 milliseconds from an ensemble of models retrained through online learning, with score explainability. Account Defense combines device, behavioral, velocity and network signals, including what Sift calls durable device fingerprinting, and helps decide when to allow a login, when to trigger MFA, and when to act more strongly.
Integration follows an event model. A JavaScript snippet and iOS, Android and React Native SDKs collect device data. Your backend sends events such as account creation, logins, orders and transactions through the Events API, including user IDs, emails, payment method details and order data. The Score API returns a 0 to 100 score, the Decisions API records what you did, and Workflows route users to approve, block or review queues.
How Kavra approaches the problem differently
Sift learns who a user is from the business events you stream to it. Kavra looks at the request itself at the moment it arrives. You add one script and make one API call at the action you care about. Kavra analyzes 3,000+ data points across network, device and environment, browser integrity, behavior, and identity and history, then returns a plain-language headline, the findings, risk by domain (automation, impersonation, network, tampering, abuse) and a recommended action. No order, payment or email data is required.
That makes Kavra strong where the attacker is a tool rather than a customer profile: credential stuffing bots, card testing scripts, scrapers, scalpers and signup farms using antidetect browsers or emulators.
- Bots across layers. Automation frameworks, headless browsers and HTTP clients imitating browsers are caught by contradictions between what they claim and how they behave.
- The real connection. Kavra's own edge network sees how a visitor actually connects, not only what the browser reports.
- Own proxy intelligence. Real exit IPs of commercial residential and mobile proxy networks, measured continuously, on top of 30+ public feeds.
- Rotation kept as one actor. A device that keeps changing its fingerprint stays one actor with N rotations.
Kavra vs Sift: capability comparison
Both products leave the final call to your systems. They differ in what they look at and what you have to send them.
| Kavra | Sift | |
|---|---|---|
| Unit of analysis | Each request, at the moment it happens | Users and events streamed through the Events API |
| Main output | Headline, findings, domain risk, recommended action | 0 to 100 Sift Score with explainability, plus Workflows |
| Data you send | A signed token from the page; no names or emails required | User IDs, emails, payment details, orders, device data |
| Bot and automation detection | Across network, device, browser integrity and behavior | Part of device, behavioral and velocity signals; bot-specific methods not publicly detailed |
| Antidetect, proxy, emulator | Detected through layer contradictions and own proxy intelligence | Not publicly documented as separate detections |
| Cross-customer data | None; strict tenant isolation | Global data network across 700+ brands, per Sift |
| Payment fraud | Card testing and payment fraud signals from the request | Dedicated Payment Protection product |
| Chargeback disputes | Not offered | Dispute Management for chargeback responses |
| Step-up | Background checks only, nothing for users to solve | Native email and SMS step-up in Account Defense |
| Review tooling | Investigate view with evidence per assessment | Console, review queues, workflows, Expert Services |
When Sift may be the better fit
Sift covers parts of the fraud stack that Kavra does not try to cover. It may be the better choice if:
- Your main loss is payment fraud and you want one vendor scoring transactions using order and payment data.
- You need chargeback dispute handling, with evidence assembly and responses, next to fraud scoring.
- You value a cross-brand network that has seen a user at other companies before they reach you.
- Your review team runs on queues, workflows and rules managed in the fraud vendor's console, with optional expert services.
- You want native email and SMS step-up built into account protection.
When Kavra is the better fit
- Most of your pain is automated: bots at login, card testing at checkout, scraping, scalping or scripted signups.
- You want an answer from the request alone, without building an event pipeline of orders and payments first.
- You need to see fraud tools directly: antidetect profiles, emulators, virtual machines, residential proxies.
- You prefer that no customer names, emails or payment details leave your systems for fraud scoring.
- You want to be live the same day with one script and one API call, starting in observe-only mode.
Kavra also recognizes verified search crawlers and AI agents through cryptographic signatures and their operators' published IP ranges, so you can let good automation through while stopping the rest. A declared bot that arrives from outside its operator's ranges is flagged as unverified.
Two layers that can work together
Kavra at the edge of each action
- Is this a real person on a real device?
- Which tool or network is it hiding behind?
- Is this the same actor as earlier accounts?
- Allow, verify or block, with reasons
Sift across the customer lifecycle
- How risky is this user and order overall?
- Has the network seen this user elsewhere?
- Which queue or workflow handles it?
- How do we respond to the chargeback?
Sources
Sift is a trademark of its respective owner. This comparison is based on public information as of September 2026 and may change. Kavra Lab is not affiliated with Sift.
How Kavra helps
Why teams choose Kavra
Explained answers on every request, with no data pipeline to build first.
Reasons you can read
Headline, findings, domain risk and a recommended action on every assessment.
Bots across every layer
Automation, headless browsers and fake browsers caught by contradictions.
Own proxy intelligence
Commercial residential and mobile proxy exits measured continuously.
One script, one call
First results the same day, webhooks and native mobile SDKs.
Your backend decides
Kavra recommends and never blocks on its own. Start in observe-only mode.
FAQ
Frequently asked questions
Something else? Talk to our team.
What is Sift used for?
Sift is used to score payments and accounts for fraud with machine learning. Businesses send it events such as signups, logins, orders and transactions, then act on a 0 to 100 score through rules and workflows. Its products cover payment protection and account defense, with dispute management and marketplace abuse covered in its published materials.
Does Kavra need order or payment data like Sift?
No. Kavra assesses each request from the page token and what it observes about the device, network, browser and behavior. You do not send orders, payment details, emails or names. You can mark sessions and devices as good or bad through the API so outcomes improve linking, but that is optional.
Can Kavra stop card testing without a payment fraud platform?
Card testing is mostly automated, so the strongest evidence is the tool: scripts, headless browsers, rotating proxies and repeated devices. Kavra flags those at the checkout or payment endpoint before the card reaches your processor. For broader payment fraud with stolen cards used by real people, many teams also keep a transaction scoring vendor.
Can I run Sift and Kavra at the same time?
Yes. They look at different evidence and fit different points in your flow. Kavra judges the request at login, signup, checkout or API call. Sift scores the user and transaction from the events you send. Your backend can combine both, for example stepping up when either one raises risk.
Does Kavra share data across customers like a consortium network?
No. Kavra keeps strict tenant isolation, so one customer's data is never shared with another. Detection relies on what Kavra observes on each request, its own proxy intelligence, public reputation feeds and models that keep learning from new fraud patterns.
See who is really on your site.
Run Kavra on your own traffic in observe-only mode. No risk to your customers, and a clear report of the fraud it finds.