Qyzar
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An anti api abuse project by Qyzar

 

Our goal: observe, learn, and evolve defenses against bots and automated attacks—so legitimate traffic stays fast and abuse gets harder over time.

Fingerprinting

Using 340+ data points we collect an initial fingerprint that forms the foundation of our anti-bot system.

Entropy

Data from users as they traverse your site is processed and run through models with a 99.6% success rate at flagging bot-like behavior.

Captcha

When we see suspicious activity we dispatch a secure, anti-AI captcha challenge to verify humans.

Anti-fraud AI

Signals cycle through our models and learn from real traffic to sharpen predictions and catch abuse earlier.

Our partners

Partner data widens the picture of each user so detections stay reliable across sessions and surfaces.

Scene entropy

Entropy is collected in the background during challenges, with high sensitivity to non-human interaction.

Data compounding

Signals from thousands of Qyzar-protected sites are aggregated and refined into a stronger view of each visitor.

Cross-site intelligence

Correlated activity across the network surfaces coordinated abuse and repeat offenders faster.

Adaptive models

The stack continuously retrains on new patterns so defenses improve as attackers evolve.

  1. Step 1

    Collecting Fingerprint

    Using over 360+ sources of information we fingerprint the user's network, hardware, and device using a series of tests and calculations—creating the base of our fingerprinting journey.

  2. Step 2

    Verification

    We check over 160 anomalies and tell-tale bot signs. We don't only rely on entropy—we score based on past browser data, IP geo data, ASN suspicion, and thousands of other sites' records of the user.

  3. Step 3

    Entropy

    We silently collect entropy, creating a larger image of what the user is doing and a summarization of their behaviour.

  4. Step 4

    Bot evasion & captchas

    When risk is high, we escalate with an in-house captcha system—no third-party widgets. Levels include depth-analysis captchas, image identification, and object-location tasks in the scene, ramping difficulty so bots face a steeper wall than real users.

Step 5

Data cycling

As fingerprinting is updated, each user's device information develops into more accurate, compounded data. Our merger bot walks every device, finds similarities that may not have been obvious before, and tightens the picture over time.

What your browser reveals

Tap when you're in view—we sample APIs locally. Charts and pills summarize what the raw numbers mean—same card chrome as the rest of the marketing pages.

Snapshot

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  1. Events

    Pointer, scroll, and key volume plus first-input delay—real sessions look uneven; bots and background tabs often skew ratios.

  2. Hardware

    Logical cores, RAM hint, touch capacity, WebGL GPU strings, and a silent audio render hash—together they cluster like a device family.

  3. Screen

    Display size, viewport, DPR, a paint hash—and a live mini snapshot of this page as your browser painted it when you tapped Ready.

  4. Locale

    Timezone, languages, platform, UA, and client hints—honest browsers line up; mismatches often mean privacy tools or spoofing.

  5. Storage / bot

    Cookies, storage, the webdriver flag, PDF viewer, and theme—cheap checks before heavier proof or friction.

Live read

Events

Pointer, scroll, and key volume plus first-input delay—real sessions look uneven; bots and background tabs often skew ratios.

Hardware

Logical cores, RAM hint, touch capacity, WebGL GPU strings, and a silent audio render hash—together they cluster like a device family.

Screen

Display size, viewport, DPR, a paint hash—and a live mini snapshot of this page as your browser painted it when you tapped Ready.

Locale

Timezone, languages, platform, UA, and client hints—honest browsers line up; mismatches often mean privacy tools or spoofing.

Storage / bot

Cookies, storage, the webdriver flag, PDF viewer, and theme—cheap checks before heavier proof or friction.

Same idea as the timeline and pricing blocks: many small fields, one scored picture—here you see the raw surface.

Plans & pricing

Efficient storage keeps our unit economics low. We apply advanced compression to fingerprint payloads so stored representations stay compact without sacrificing the fields our models need. Lower storage and transfer overhead means we can offer broad coverage at price points many competing services cannot match—without cutting corners on signal quality. Beyond price, tiers differ by how many concurrent sessions you can run and how deep the signal stack goes. Choose the level that matches your traffic and compliance posture, and upgrade when you outgrow the limits.

  • Free

    $0

    No credit card required

    Up to 10 active sessions

    Validate integration paths, run proofs of concept, and ship small pilots without committing to a subscription.

    • Full fingerprint capability—same core collectors as paid tiers
    • 1 traffic rule and up to 10 concurrent sessions
    • Designed for tests, staging, and low-traffic production checks
    Get started
  • Pro

    $7/ month

    Billed monthly · cancel anytime

    Up to 20,000 active sessions

    Scale to production traffic with a high session ceiling and more traffic rules for live apps.

    • Everything in Free, with up to 20,000 concurrent sessions
    • Up to 5 traffic rules for ASN, geo, and user-agent controls
    • Suited to live applications, SaaS, and customer-facing flows
    Subscribe
  • Enterprise

    $60/ month

    Billed monthly · cancel anytime

    Unlimited sessions

    Our deepest signal stack for teams that rely on entropy and long-horizon behavior for risk, fraud, and trust decisions.

    • Full fingerprinting pipeline, including entropy and extended signals
    • Built for higher scrutiny use cases and richer device intelligence
    • Unlimited sessions and traffic rules, with the full analytical depth
    Subscribe