Kasada vs WebDecoy

Kasada vs WebDecoy. Adversarial ML challenges vs multi-signal detection with honeypots, TLS fingerprinting, and vision AI detection.

Kasada vs WebDecoy

Kasada and WebDecoy take fundamentally different approaches to bot mitigation. Kasada uses adversarial ML with dynamic challenges—effective but creates user friction. WebDecoy uses multi-signal detection including honeypots, TLS fingerprinting, and vision AI detection—zero friction for legitimate users.

Detection Philosophy

Kasada: Challenge-Based Detection

User Request

Kasada Edge
    ├── Initial Challenge
    │   └── Proof-of-work computation
    ├── Behavioral Analysis
    ├── Device Fingerprinting
    └── Adversarial ML
        └── Adapts to evasion attempts

Challenge / Allow / Block

Kasada’s philosophy: Force clients to prove they’re legitimate through computational challenges. Continuously adapt challenges based on evasion attempts.

Target market: High-value targets (financial services, gaming, ticketing) where sophisticated attackers justify user friction.

WebDecoy: Frictionless Multi-Signal Detection

User Request

WebDecoy Detection Stack
    ├── TLS Fingerprinting (JA3/JA4)
    ├── IP Enrichment (AbuseIPDB, GreyNoise, IPQS)
    ├── Geographic Consistency
    ├── Honeypot Detection
    │   ├── Decoy Links
    │   └── Endpoint Decoys
    ├── Behavioral Analysis (Bot Scanner)
    └── Vision AI Detection (FCaptcha)

Threat Score (0-100) → Allow / Challenge / Block

WebDecoy’s philosophy: Layer multiple detection signals that work invisibly. Honeypots provide high-confidence signals; other layers catch what honeypots miss.

Target market: Organizations wanting effective bot detection without user friction.

Detection Method Comparison

CapabilityKasadaWebDecoy
Primary MethodAdversarial challengesMulti-signal detection
User FrictionYes (challenges)No (invisible)
TLS FingerprintingIncludedJA3 + JA4 + JA4H
HoneypotsNoDecoy Links + Endpoint Decoys
IP IntelligenceKasada networkAbuseIPDB + GreyNoise + IPQS
Proof-of-WorkYes (core feature)Optional (FCaptcha)
Vision AI DetectionNoFCaptcha (GPT-4V, Claude Computer Use)
AI Crawler DetectionVia challengesPurpose-built (15+ crawlers)
SIEM IntegrationEnterpriseAll tiers
JavaScript RequiredYesCore detection: No

Key Differences

Kasada’s Strengths

Adversarial ML

Kasada’s challenges adapt to evasion:

  • Dynamic challenge generation
  • Proof-of-work requirements increase with suspicion
  • Continuous learning from attack patterns
  • Effective against well-funded attackers who solve static challenges

Pre-Interaction Protection

Challenges happen before bots interact with your application:

  • Protection at the edge
  • Bots can’t even access content without passing challenges
  • Catches reconnaissance attempts

High-Security Focus

Built for industries with sophisticated threats:

  • Financial services
  • Gaming and ticketing
  • Licensed content protection

WebDecoy’s Strengths

Zero User Friction

Honeypots and multi-signal detection are completely invisible:

  • No challenges for legitimate users
  • No abandonment from frustrated users
  • Works without JavaScript for core detection

Honeypot Detection (Kasada doesn’t have this)

// Endpoint Decoy catches attackers
{
  "endpoint_decoy": {
    "path": "/api/admin/config",
    "attack_patterns": [
      { "type": "sql_injection", "severity": "critical" }
    ],
    "score_impact": +50
  }
}

No legitimate user accesses honeypots—high-confidence signals.

Vision AI Detection (Kasada doesn’t have this)

// FCaptcha detects vision AI agents
{
  "vision_ai": {
    "screenshot_loop_timing": true,
    "pixel_perfect_clicks": true,
    "movement_entropy": 0.01,
    "classification": "vision_ai_agent"
  }
}

Kasada’s challenges are designed for traditional bots. Vision AI agents that control real browsers need different detection methods.

Multi-Signal Detection

Multiple independent layers catch different threats:

{
  "tls": { "mismatch": true, "score": +40 },
  "ip": { "datacenter": true, "score": +25 },
  "honeypot": { "triggered": true, "score": +50 },
  "behavioral": { "mouse_entropy": 1.2, "score": +20 }
}

Real-World Scenarios

Scenario 1: Sophisticated Bot with Challenge Solving

Threat: Bot service with CAPTCHA-solving capability.

Kasada’s Detection:

  • Proof-of-work: Bot solves challenges (some cost)
  • Adversarial ML: May adapt to detect patterns
  • Result: Cat-and-mouse game, effectiveness varies

WebDecoy’s Detection:

  • Challenge not required for detection
  • Decoy Link: Bot follows hidden link ✅
  • TLS fingerprint: Automation signature ✅
  • Result: Blocked via multiple signals, no challenge required

Scenario 2: Vision AI Agent (OpenAI Operator)

Threat: AI agent using screenshots and vision models to navigate.

Kasada’s Detection:

  • Challenges: Agent can read and describe challenges
  • Proof-of-work: Agent’s browser can compute
  • Result: Vision AI may solve challenges designed for traditional bots

WebDecoy’s Detection:

  • FCaptcha: Pixel-perfect click patterns ✅
  • FCaptcha: Screenshot loop timing (2-3s intervals) ✅
  • FCaptcha: Zero movement during “thinking” ✅
  • Result: Classified as vision AI agent

Scenario 3: User Experience Impact

Scenario: Legitimate user visiting your site.

Kasada:

  • User sees challenge
  • Must wait for proof-of-work computation
  • Some users abandon

WebDecoy:

  • User sees nothing
  • Detection happens invisibly
  • Zero friction

Pricing Comparison

Kasada

  • Enterprise pricing with custom quotes
  • Typically thousands to tens of thousands per year
  • Custom implementation required
  • Enterprise support included

WebDecoy

PlanPriceDomainsDetectionsKey Features
Starter$59/mo15,000/moBot Scanner, Decoy Links, FCaptcha
Pro$149/mo5100,000/mo+ Endpoint Decoys, TLS fingerprinting
Agency$449/mo50500,000/mo+ All SIEM integrations

When to Choose Each

Choose Kasada If:

  • You face sophisticated, well-funded attackers
  • Challenge-based friction is acceptable
  • You protect high-value assets (financial, gaming)
  • Pre-interaction blocking is required
  • You have enterprise budget

Choose WebDecoy If:

  • User experience is a priority (zero friction)
  • You want multi-signal detection
  • You need vision AI agent detection
  • You prefer honeypot-based detection
  • You have budget constraints
  • You need SIEM integration without enterprise pricing

What WebDecoy Provides

  1. Zero Friction - Invisible detection, no challenges for users
  2. Multi-Signal Detection - TLS + IP + Geo + Behavioral + Honeypots
  3. Vision AI Detection - FCaptcha catches GPT-4V, Claude Computer Use, Operator
  4. Honeypot Technology - Decoy Links and Endpoint Decoys
  5. IP Enrichment - AbuseIPDB, GreyNoise, IPQualityScore
  6. Transparent Detection - See exactly which signals triggered
  7. SIEM Integration - All tiers include Splunk, Elastic, CrowdStrike
  8. Accessible Pricing - $59-449/month vs enterprise quotes

Get Started

Try WebDecoy: Start Your Free Trial and see frictionless bot detection.

Questions? Contact us to discuss your threat model.

Frequently Asked Questions

What is Kasada's approach to bot detection?

Kasada uses adversarial ML with dynamic challenges. Challenges adapt based on evasion attempts, requiring computational proof-of-work. Designed for high-security environments with sophisticated attackers.

How does WebDecoy differ from Kasada?

WebDecoy uses multi-signal detection (honeypots, TLS fingerprinting, behavioral analysis, IP enrichment) without requiring user-facing challenges. Zero friction for legitimate users while catching sophisticated bots through multiple detection layers.

Which creates more user friction?

Kasada's challenge-based approach creates friction (users must complete challenges). WebDecoy's approach is frictionless—honeypots and multi-signal detection work invisibly in the background.

Can WebDecoy detect vision AI agents?

Yes. WebDecoy's FCaptcha detects vision AI agents (GPT-4V, Claude Computer Use, OpenAI Operator) by analyzing screenshot loop timing and pixel-perfect click patterns. Kasada's challenges are designed for traditional bots, not vision AI.

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