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Antigravity Agent
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id: wiki-2026-0508-saas
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title: SaaS (Software as a Service)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Software as a Service, Cloud Software, Multi-tenant SaaS]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [saas, multi-tenancy, subscription, ai-native, vertical-saas]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: TypeScript
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framework: Next.js / Stripe / Postgres RLS
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---
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# SaaS (Software as a Service)
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## 매 한 줄
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> **"매 multi-tenant, subscription-based, browser-delivered software — 매 install 의 X, 매 always-latest"**. 매 1999 Salesforce ("End of Software") 로 출발 → 매 2010s SaaS 1.0 (horizontal CRM/HR) → 매 2020s vertical SaaS (Toast, Procore, Veeva) → 매 2026 AI-native SaaS (Glean, Harvey, Cursor) 가 매 outcome-based pricing 으로 매 seat-based 모델 을 흔드는 시기.
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## 매 핵심
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### 매 Pillars
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- **Multi-tenancy**: tenant-isolated data on shared infra (RLS, schema-per-tenant, DB-per-tenant).
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- **Subscription**: MRR/ARR, Stripe billing, dunning, proration.
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- **Self-serve onboarding**: PLG funnel, time-to-value 분 단위.
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- **Continuous delivery**: weekly/daily ship, no version skew.
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- **Observability**: per-tenant SLO, usage analytics, churn signals.
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### 매 SaaS metrics
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- **ARR/MRR**, **NRR** (Net Revenue Retention) — best signal of product fit.
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- **CAC payback** — months to recoup acquisition.
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- **Magic Number** = (ΔARR × 4) / S&M spend.
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- **Gross margin** — typical SaaS 70-85%, AI-SaaS 50-70% (inference cost).
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- **LTV/CAC** ≥ 3, payback ≤ 12 months.
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### 매 Pricing models
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- **Per-seat**: classic Salesforce/Slack — saturating in AI era.
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- **Usage-based**: Snowflake, Twilio, OpenAI — aligns to value.
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- **Outcome/agent-based**: 2026 AI-native — pay per resolved ticket, per qualified lead.
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- **Hybrid**: platform fee + usage overage.
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### 매 응용
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1. Horizontal SaaS (CRM, HRIS, ITSM, comms).
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2. Vertical SaaS (legal, dental, construction, hospitality).
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3. Developer SaaS (GitHub, Vercel, Sentry).
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4. AI-native SaaS (Cursor, Glean, Harvey, Decagon).
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5. Embedded SaaS (in-app commerce, fintech).
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## 💻 패턴
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### Multi-tenant Postgres RLS
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```sql
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ALTER TABLE documents ENABLE ROW LEVEL SECURITY;
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CREATE POLICY tenant_isolation ON documents
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USING (tenant_id = current_setting('app.tenant_id', true)::uuid);
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-- App connection middleware
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SET LOCAL app.tenant_id = '7f3a...';
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```
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### Stripe subscription with proration
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```typescript
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import Stripe from 'stripe'
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const stripe = new Stripe(process.env.STRIPE_SECRET!)
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await stripe.subscriptions.update(subId, {
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items: [{ id: itemId, price: 'price_pro_monthly', quantity: 25 }],
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proration_behavior: 'create_prorations',
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billing_cycle_anchor: 'unchanged',
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})
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```
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### Usage-based metering (Stripe meters, 2026)
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```typescript
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await stripe.billing.meterEvents.create({
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event_name: 'api_calls',
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payload: {
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stripe_customer_id: customer.id,
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value: '1',
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},
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})
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// Subscribe customer to metered price; Stripe aggregates and bills monthly
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```
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### Tenant context middleware (Next.js)
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```typescript
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// app/middleware.ts
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import { NextResponse } from 'next/server'
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export async function middleware(req: Request) {
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const session = await getSession(req)
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const tenantId = session?.tenantId
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if (!tenantId) return NextResponse.redirect('/login')
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const res = NextResponse.next()
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res.headers.set('x-tenant-id', tenantId)
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return res
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}
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```
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### Per-tenant rate limit (Upstash Redis)
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```typescript
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import { Ratelimit } from '@upstash/ratelimit'
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import { Redis } from '@upstash/redis'
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const limiters = new Map<string, Ratelimit>()
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function tenantLimiter(plan: 'free' | 'pro' | 'enterprise') {
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const limits = { free: 100, pro: 1000, enterprise: 10000 }
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return new Ratelimit({
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redis: Redis.fromEnv(),
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limiter: Ratelimit.slidingWindow(limits[plan], '1 m'),
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})
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}
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```
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### PLG signup with magic-link (no password)
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```typescript
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// Send magic link, no password friction
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const token = jwt.sign({ email, tenantId: nanoid() }, SECRET, { expiresIn: '15m' })
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await sendEmail(email, `https://app.example.com/auth?t=${token}`)
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// On click: provision trial tenant, redirect to onboarding wizard
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```
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### AI-native SaaS — usage-aware inference cost
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```typescript
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async function chat(tenantId: string, msgs: Message[]) {
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const tier = await getTier(tenantId)
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const model = tier === 'enterprise' ? 'claude-opus-4-7' : 'claude-haiku-4-5'
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const resp = await anthropic.messages.create({
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model, max_tokens: 1024, messages: msgs,
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metadata: { user_id: tenantId },
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})
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await meterUsage(tenantId, {
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input_tokens: resp.usage.input_tokens,
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output_tokens: resp.usage.output_tokens,
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model,
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})
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return resp
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}
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```
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### Outcome pricing instrumentation
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```typescript
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// Charge only when AI agent successfully resolved
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async function recordResolution(tenantId: string, ticketId: string, resolved: boolean) {
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if (resolved) {
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await stripe.billing.meterEvents.create({
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event_name: 'resolved_ticket',
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payload: { stripe_customer_id: tenantId, value: '1' },
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})
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}
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}
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```
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### Tenant-isolated S3 (per-prefix IAM)
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```python
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# Generate scoped STS token per tenant request
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sts = boto3.client("sts")
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policy = {"Version": "2012-10-17", "Statement": [{
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"Effect": "Allow",
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"Action": ["s3:GetObject", "s3:PutObject"],
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"Resource": [f"arn:aws:s3:::tenants-bucket/{tenant_id}/*"],
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}]}
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creds = sts.assume_role(
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RoleArn=ROLE, RoleSessionName=f"tenant-{tenant_id}",
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Policy=json.dumps(policy), DurationSeconds=900,
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)
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Early-stage horizontal | Shared schema + RLS, Stripe per-seat |
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| Vertical w/ compliance | Schema-per-tenant or DB-per-tenant |
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| AI-native, variable cost | Usage-based + cap to protect margin |
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| Enterprise w/ SOC2/HIPAA | Single-tenant deploy option, BYOC |
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| PLG self-serve | Magic-link + provisioned trial in <60s |
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**기본값**: shared-DB RLS + hybrid pricing (platform fee + usage) + Stripe meters + PLG onboarding.
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## 🔗 Graph
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## 🤖 LLM 활용
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**언제**: in-product copilot, customer support deflection, churn prediction from usage signals, content/email generation, dynamic onboarding.
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**언제 X**: pricing/billing computation — must be deterministic for audit and revenue recognition.
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## ❌ 안티패턴
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- **No tenant isolation**: WHERE tenant_id checked only in app layer → IDOR breach.
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- **Per-seat pricing for AI**: high-usage user breaks margin; need usage cap or tier.
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- **Free tier without limits**: abuse → infra cost spirals.
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- **Single-region SaaS for global**: latency + data residency violations (GDPR).
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- **No self-serve cancel**: regulatory risk (FTC click-to-cancel 2024) + churn spikes.
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- **Version skew**: different customers on different versions → support combinatorics explode.
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## 🧪 검증 / 중복
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- Verified (a16z SaaS metrics, OpenView PLG benchmarks, Stripe Billing docs, AWS SaaS Lens).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — multi-tenancy, pricing models, AI-native SaaS 2026 |
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