Argus: AI Architecture for Scale (1 to 10 Crore Users)¶
The Problem We're Solving¶
Traditional testing tools are: 1. Human-dependent - Someone must write/record tests 2. Reactive - Fix bugs after they happen 3. Isolated - Each company learns alone 4. Expensive - Pay per seat, not per value
Argus is: 1. AI-native - Tests generated from production errors 2. Predictive - Prevent bugs before they manifest 3. Collective - Cross-company pattern intelligence 4. Cost-optimized - Free tier infrastructure
Infrastructure Stack (Maximizing Free Tier)¶
┌─────────────────────────────────────────────────────────────────────────┐
│ CLOUDFLARE EDGE (Global, 300+ PoPs) │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Workers │ │ Browser │ │ Workers AI │ │ Vectorize │ │
│ │ (Free) │ │ Rendering │ │ (FREE!) │ │ (Free 5M) │ │
│ │ 100k req/day │ │ (Free) │ │ Llama 3.1 │ │ vectors │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ KV │ │ R2 │ │ D1 │ │ Queues │ │
│ │ (Cache) │ │ (Storage) │ │ (SQLite) │ │ (Async) │ │
│ │ 100k reads │ │ 10GB free │ │ 5GB free │ │ 100k msgs │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ DATA LAYER (PostgreSQL) │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────────┐ ┌──────────────────────┐ │
│ │ Supabase │ │ Neon │ │
│ │ (Primary DB) │ │ (Scale-out) │ │
│ │ 500MB free │ │ Serverless PG │ │
│ │ Realtime subs │ │ Branch per PR │ │
│ └──────────────────────┘ └──────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────┘
Total Free Tier Value: ~$500/month equivalent¶
AI Cost Optimization Strategy¶
The Tiered Model Approach¶
┌─────────────────────────────────────────────────────────────┐
│ AI MODEL ROUTER │
├─────────────────────────────────────────────────────────────┤
│ │
│ Request → Classify Complexity → Route to Optimal Model │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ TIER 1 │ │ TIER 2 │ │ TIER 3 │ │
│ │ Workers AI │ │ Claude │ │ Claude │ │
│ │ Llama 3.1 │ │ Haiku │ │ Sonnet │ │
│ │ │ │ │ │ │ │
│ │ COST: $0 │ │ $0.25/1M tok│ │ $3/1M tok │ │
│ │ │ │ │ │ │ │
│ │ USE FOR: │ │ USE FOR: │ │ USE FOR: │ │
│ │ - Simple │ │ - Medium │ │ - Complex │ │
│ │ errors │ │ tests │ │ debug │ │
│ │ - Pattern │ │ - Code │ │ - Multi- │ │
│ │ matching │ │ analysis │ │ file │ │
│ │ - Caching │ │ │ │ fixes │ │
│ │ lookups │ │ │ │ │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
│ Distribution Target: 70% / 25% / 5% │
│ Effective Cost: $0.008/test (vs $0.10 naive) │
│ │
└─────────────────────────────────────────────────────────────┘
Caching Strategy (70% AI Cost Reduction)¶
// Pattern-based caching system
interface CachedTest {
pattern_hash: string; // Normalized error signature
embedding_hash: string; // Semantic similarity key
generated_test: string; // Cached test code
success_rate: number; // How often this test works
use_count: number; // Times reused
}
// Flow:
// 1. Error comes in
// 2. Generate pattern hash + embedding
// 3. Check Vectorize for similar patterns (cosine > 0.85)
// 4. If found: Return cached test (FREE!)
// 5. If not: Generate new test, cache it
// At scale: 70% cache hit rate = 70% cost savings
Fine-Tuned Small Models (Future)¶
Phase 1 (Now): Use Workers AI + Claude fallback
Phase 2 (6 months): Collect training data from successful generations
Phase 3 (12 months): Fine-tune Llama 3.1 on our test generation patterns
Phase 4 (18 months): Deploy custom model on Workers AI (FREE inference!)
Result: 95% of requests handled by FREE fine-tuned model
Scalability: 1 User to 10 Crore Users¶
Architecture Evolution¶
┌─────────────────────────────────────────────────────────────────────────┐
│ SCALE LEVELS │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ LEVEL 1: 1-1,000 Users (Startup) │
│ ├── Single Cloudflare Worker │
│ ├── Supabase (500MB free) │
│ ├── Workers AI only │
│ └── Cost: ~$0/month │
│ │
│ LEVEL 2: 1,000-100,000 Users (Growth) │
│ ├── Multiple Workers (regional) │
│ ├── Supabase Pro ($25/month) │
│ ├── Workers AI + Haiku fallback │
│ ├── Vectorize for pattern caching │
│ └── Cost: ~$100-500/month │
│ │
│ LEVEL 3: 100,000-1,000,000 Users (Scale) │
│ ├── Cloudflare Workers (unlimited) │
│ ├── Neon serverless (auto-scaling) │
│ ├── Full AI model tiering │
│ ├── Regional data residency │
│ └── Cost: ~$2,000-10,000/month │
│ │
│ LEVEL 4: 1,000,000-10,000,000 Users (Enterprise) │
│ ├── Multi-region Workers │
│ ├── Dedicated database clusters │
│ ├── Custom fine-tuned models │
│ ├── On-premise option for enterprises │
│ └── Cost: ~$20,000-100,000/month │
│ │
│ LEVEL 5: 10,000,000+ Users (Global) │
│ ├── Cloudflare Enterprise │
│ ├── Globally distributed data │
│ ├── Self-hosted model inference │
│ ├── White-label solutions │
│ └── Cost: Variable (profitable at this scale) │
│ │
└─────────────────────────────────────────────────────────────────────────┘
Key Insight: Cloudflare = Infinite Scale at Near-Zero Marginal Cost¶
Traditional SaaS:
├── AWS Lambda: $0.20 per 1M requests
├── + API Gateway: $3.50 per 1M requests
├── + Data transfer: $0.09/GB
└── Total: ~$4/1M requests
Cloudflare Workers:
├── First 100k requests/day: FREE
├── Beyond: $0.50 per 1M requests
├── + No data transfer costs (edge)
└── Total: ~$0.50/1M requests (8x cheaper!)
The Data Flywheel (Our True Moat)¶
┌─────────────────────────────────────────────────────────────────────────┐
│ DATA FLYWHEEL │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────┐ │
│ │ More Users │ │
│ └────────┬─────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────┐ │
│ │ More Error Patterns Learned │ │
│ └───────────────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────┐ │
│ │ Better Test Generation │ │
│ │ (Higher cache hit rate) │ │
│ └───────────────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────┐ │
│ │ Fewer Production Bugs │ │
│ └───────────────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────┐ │
│ │ Higher Trust / NPS │ │
│ └───────────────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────┐ │
│ │ More Users │◄────── FLYWHEEL REPEATS │
│ └──────────────────┘ │
│ │
│ KEY METRIC: Pattern Library Size │
│ ├── 1,000 patterns = Useful │
│ ├── 10,000 patterns = Competitive advantage │
│ ├── 100,000 patterns = Defensible moat │
│ └── 1,000,000 patterns = Industry standard │
│ │
└─────────────────────────────────────────────────────────────────────────┘
Pricing Strategy¶
Free Forever (Loss Leader)¶
├── 100 test runs/month
├── Workers AI only (FREE for us)
├── 1 project
├── 3 integrations
├── Community support
├── Pattern learning ON (they contribute data)
└── OUR COST: ~$0
Pro - $49/month (Cash Cow)¶
├── 1,000 test runs/month
├── AI model tiering (Haiku + Workers AI)
├── 5 projects
├── All integrations
├── Email support
├── Predictive quality
├── AI Quality Score
└── OUR COST: ~$5-10/month → 80% margin
Team - $199/month (Growth Driver)¶
├── 10,000 test runs/month
├── Full AI access (Sonnet for complex)
├── Unlimited projects
├── Slack support
├── Custom webhooks
├── API access
├── Team analytics
└── OUR COST: ~$30-50/month → 75% margin
Enterprise - Custom ($5,000+/month)¶
├── Unlimited everything
├── Dedicated support
├── SLA guarantees
├── On-premise option
├── Custom AI model training
├── White-label option
├── SSO/SAML
└── OUR COST: Variable → 60-70% margin
Competitive Moat Summary¶
What Makes Us Defensible¶
| Moat Type | Description | Strength |
|---|---|---|
| Network Effects | More users = better patterns = better for everyone | ★★★★★ |
| Data Flywheel | Error patterns are cumulative and defensible | ★★★★★ |
| Cost Structure | Cloudflare free tier = can undercut anyone | ★★★★☆ |
| Infrastructure Lock-in | Deep CF integration hard to replicate | ★★★★☆ |
| Switching Costs | Integrated into CI/CD, hard to remove | ★★★☆☆ |
What Others CAN'T Easily Copy¶
- Cross-Company Pattern Learning
- Legal complexity (privacy, data sharing)
- Requires scale to be useful
-
Network effects compound
-
Production-to-Test Loop
- Requires deep integrations (Sentry, Datadog, etc.)
- Existing players (Sentry) won't cannibalize their product
-
Testing tools (Mabl) don't have production data
-
Cloudflare-Native Architecture
- Browser Rendering is unique to CF
- Workers AI is free only on CF
- Vectorize integration is seamless
The 2050 Vision: Self-Evolving Quality¶
TODAY (2024):
├── Developers write code
├── Bugs go to production
├── Monitoring catches bugs
├── Someone writes a test
└── Repeat
NEAR FUTURE (2026):
├── Developers write code
├── AI predicts likely bugs before deploy
├── Auto-generated tests catch issues in CI
├── Production errors auto-generate tests
└── Tests self-heal when UI changes
FAR FUTURE (2030+):
├── AI assists in writing code
├── AI simultaneously writes tests
├── Quality is a continuous gradient, not pass/fail
├── Cross-company intelligence prevents known bugs
└── "Testing" as a separate activity disappears
ARGUS POSITION:
├── We're building the bridge from TODAY to FAR FUTURE
├── Each feature we ship moves the industry forward
├── The pattern library becomes the "immune system" of software
└── Eventually: "All software uses Argus patterns" (like how all search uses Google's index)
Implementation Roadmap¶
Phase 1: Foundation (Now - 3 months)¶
- Core testing engine
- Production event ingestion
- AI test generation
- Risk scoring
- Pattern learning
- Predictive quality
- Fine-tune caching for 70% hit rate
- Launch free tier
Phase 2: Growth (3-6 months)¶
- 10 more integrations (Linear, Jira, PagerDuty, etc.)
- VS Code extension
- GitHub App
- Pattern marketplace (share/discover patterns)
- Fine-tune small model on our data
Phase 3: Scale (6-12 months)¶
- Multi-region deployment
- Enterprise features (SSO, audit logs)
- On-premise option
- API for third-party integrations
- White-label program
Phase 4: Dominance (12-24 months)¶
- Industry-standard pattern library
- Acquisition targets or acquirer
- Platform play (let others build on Argus)
- Research publications (credibility)
Summary: Why Argus Wins¶
1. COST ADVANTAGE
└── Cloudflare free tier = near-zero infrastructure cost
2. NETWORK EFFECTS
└── More users = better patterns = more value for everyone
3. UNIQUE DATA
└── Production-to-test loop generates defensible data
4. AI-NATIVE
└── Not "AI-washed" - AI is core, not a feature
5. TIMING
└── LLMs just became good enough for reliable test generation
6. TEAM
└── [Your competitive advantage here]
The future of software quality is predictive, automated, and collective. Argus is building that future.