# Business Model: lambdagentpaas Commercialization Strategy

> **Date**: 2026-04-04
> **Core Positioning**: We sell insurance, not a framework. Pricing anchored to money saved, not features delivered.

---

## 1. Model: Open Core + Usage-Based

```
Open Source (Free)       → User discovers value
    ↓
Pro Subscription ($29)   → Individual/small team pays for runtime guards
    ↓
Enterprise ($199+)       → Organization pays for team-wide cost control
```

---

## 2. Three-Tier Pricing

### Free — Open Source (Acquisition)

| Feature | Details |
|---------|---------|
| CLI `lambdagent lint` | 26 rules, runs locally, unlimited |
| MCP Server (local mode) | 4 tools, runs locally, no network required |
| VS Code extension (basic) | Real-time lint, inline diagnostics |
| GitHub Action (public repos) | PR checks, unlimited |
| Python package `lambdagent` | Core DSL + compiler + type checker |
| T-Compose type checking | First 2 pipeline levels only |
| Cost prediction | Total cost only (no per-stage breakdown) |
| Parallel safety check | Up to 2 parallel agents |

**Why free**: This is the acquisition funnel. A developer runs lint, discovers 3 ERRORs, avoids a $5 failed run — and remembers the tool.

### Pro — $29/month/developer (Individuals & Small Teams)

| Feature | Free | Pro |
|---------|------|-----|
| Lint (26 rules) | ✅ | ✅ |
| T-Compose type checking | 2-level pipelines | **Unlimited depth** |
| Cost prediction | Total cost only | **Per-stage breakdown + optimization suggestions** |
| Parallel safety | 2 agents | **Unlimited** |
| `lambdagent-guard` middleware | ❌ | **✅ LangChain / CrewAI / AutoGen** |
| Runtime cost circuit breaker | ❌ | **✅ Set budget ceiling, auto-pause** |
| Runtime loop detection | ❌ | **✅ Repeated state → alert** |
| Cost anomaly detection | ❌ | **✅ Actual vs expected >2x → alert** |
| GitHub Action (private repos) | ❌ | **✅ Unlimited** |
| Historical analysis reports | ❌ | **✅ Weekly/monthly cost trends** |
| Priority bug fixes | ❌ | **✅** |

**Pricing logic**: $29/month ≈ cost of one failed agent run. Claude Code #38029's $342 incident pays for 11 months of Pro.

### Enterprise — $199/month/team (up to 10) or contact sales

| Feature | Pro | Enterprise |
|---------|-----|-----------|
| All Pro features | ✅ | ✅ |
| SSO / SAML | ❌ | **✅** |
| Team dashboard | ❌ | **✅ Org-wide agent cost/failure overview** |
| Custom lint rules | ❌ | **✅ Enforce internal standards** |
| On-premise deployment | ❌ | **✅ Code stays in your network** |
| SLA + priority support | ❌ | **✅ 48h response** |
| Audit log | ❌ | **✅ Who changed what config, when** |
| Org-level cost ceiling | ❌ | **✅ Total agent budget for entire org** |
| API calls | — | **Unlimited** |
| Custom extractors | ❌ | **✅ Support internal frameworks** |

---

## 3. Pricing Anchor: Money Saved for the User

Never price by features. Price by losses avoided.

| Real Incident | Loss | lambdagent Saves | We Charge |
|---------------|------|-------------------|-----------|
| Claude Code #38029 (auto-generate 652K tokens) | $342/session | $342 | $29/month |
| Claude Code #34629 (cache bug 28 days) | Thousands of dollars | Thousands | $29/month |
| Claude Code #4095 (1.67B tokens) | $1,000+ | $1,000+ | $29/month |
| AutoGen #108 (blank message loop) | API quota exhausted | Full quota | $29/month |
| Enterprise: 100 agents × $1.74/run × 10 runs/day | $522/day | 40% savings = $209/day | $199/month |

**Sales pitch**: lambdagent Pro costs $29/month — the price of a single ReAct loop run. It catches all type errors, predicts cost ceilings, and detects infinite loops before execution. A single prompt-cache bug in Claude Code cost users 10-20x for 28 days. lambdagent's cost anomaly detection catches this on the first call.

---

## 4. Four Revenue Lines

### Revenue Line 1: SaaS Subscription (Primary)

```
Free → Pro → Enterprise funnel

Conversion estimates (benchmarked against Snyk/SonarCloud):
  Free users:      10,000
  Pro conversion:  3-5%  → 300-500 × $29/month = $8,700-$14,500/month
  Enterprise:      Contact sales → 10-20 teams × $199/month = $1,990-$3,980/month

Estimated ARR (mature): $120K - $220K
```

### Revenue Line 2: API Usage-Based (Supplementary)

Pay-per-call REST API for users who prefer not to subscribe:

```
POST /api/v1/analyze/full

Pricing:
  Lint:               $0.001/call (nearly free — lead gen)
  Type checking:      $0.005/call
  Cost prediction:    $0.005/call
  Parallel safety:    $0.005/call
  Full analysis (4-in-1): $0.01/call

Example: CI/CD calls once per PR
  100 PRs/month × $0.01 = $1/month (too cheap to matter)
  → API is an on-ramp to Enterprise, not a revenue driver
```

### Revenue Line 3: Marketplace (Channel)

```
GitHub Marketplace:  lambdagent/agent-lint-action
  Free tier:  Public repos
  Paid tier:  Private repos, $9/month/repo (GitHub takes 30%)

VS Code Marketplace: lambdagent.agent-lint
  Free tier:  Basic lint
  Paid tier:  Pro features (type check + cost prediction), $9/month
```

### Revenue Line 4: Consulting & Training (High Margin, Low Volume)

```
Agent Architecture Audit:  $2,000/engagement
  - Full λA analysis of customer's existing agent pipelines
  - Deliverable: Type safety report + cost optimization + parallel safety report
  - Real-world example: Finding hidden cost issues like Claude Code #34629

Enterprise Training:       $5,000/day
  - Agent programming paradigm + formal methods in practice
  - Target: AI teams of 10-20 engineers
```

---

## 5. Free vs Paid Split Principle

```
Free = runs locally, results stay local
Paid = server-side computation, continuous monitoring, team collaboration

Free:                               Paid:
  "This config has 3 ERRORs"         "This config costs $1.74/run.
                                      Reduce to 3 parallel scanners to
                                      save 40%. Here's the optimized config."

  "Type mismatch"                    "Type mismatch between stage 2-3.
                                      Add a Json→Str adapter.
                                      Here's the auto-generated fix diff."

  "Parallel conflict detected"       "Over the past 7 days your agents
                                      triggered 12 store conflicts.
                                      Most common key: shared_doc.
                                      Here's the trend chart."
```

**Principle: Diagnosis is free, prescription is paid.**

---

## 6. Competitive Pricing Benchmarks

| Product | Analogy | Free Tier | Paid Tier |
|---------|---------|-----------|-----------|
| **Snyk** (security scanning) | Similar: scan code for vulnerabilities | Open source free | $25/month/developer |
| **SonarCloud** (code quality) | Similar: static analysis | Public repos free | $14/month+ |
| **Datadog** (observability) | Similar: runtime monitoring | Limited free | $15/month/host |
| **Sentry** (error tracking) | Similar: runtime error detection | 5K events free | $26/month |
| **LangSmith** (LLM observability) | Direct competitor | 5K traces/month | $39/month |

lambdagent Pro at $29/month is below LangSmith ($39) while offering capabilities LangSmith lacks entirely: static type checking, compile-time cost prediction, parallel safety verification, and algebraic law-based optimization.

### Differentiation from LangSmith

| Capability | LangSmith | lambdagent |
|-----------|-----------|------------|
| Runtime tracing | ✅ | ✅ (via TraceHandler) |
| Runtime cost tracking | ✅ | ✅ |
| **Static type checking** | ❌ | **✅** |
| **Compile-time cost prediction** | ❌ | **✅** |
| **Parallel safety verification** | ❌ | **✅** |
| **Dead loop detection (compile-time)** | ❌ | **✅** |
| **Cost anomaly detection** | ❌ | **✅** |
| **Algebraic optimization** | ❌ | **✅** |
| Framework support | LangChain only | **LangChain + CrewAI + AutoGen** |

LangSmith tells you what happened after you spent the money. lambdagent tells you what will happen before you spend anything.

---

## 7. Go-To-Market (GTM) Path

### Phase 1: Open Source Acquisition (Month 0-3)

```
1. Release lambdagent CLI + MCP Server (all free)
2. Write 3 blog posts:
   - "A Claude Code bug silently inflated costs 10-20x for 28 days
     — here's the tool that catches it on day 1"
   - "94% of GitHub agent configs have structural defects
     — check yours with this free tool"
   - "Add a safety layer to your LangChain agent in 2 lines of code"
3. Post on Reddit r/LangChain, r/LocalLLaMA, Hacker News
4. Submit to awesome-langchain, awesome-llm-agents lists
5. Target: 1,000 GitHub stars, 500 CLI installs
```

### Phase 2: Paid Validation (Month 3-6)

```
1. Launch Pro ($29/month)
2. Target: 100 Free users, 10 Pro users
3. Key metrics:
   - Monthly active users (retention)
   - Free → Pro conversion rate
   - User feedback: is Pro worth $29?
4. Adjust Free/Pro feature split based on feedback
5. Key learning: what feature makes users upgrade?
```

### Phase 3: Enterprise Expansion (Month 6-12)

```
1. Launch Enterprise ($199/month)
2. Find 3-5 enterprise pilots (free pilot → paid conversion)
3. Build case studies:
   "Company X deployed lambdagent. Agent failure rate dropped 70%.
    Monthly LLM cost reduced 35%. ROI: 12x in first quarter."
4. Hire first sales person (or partner with AI consulting firms)
```

### Phase 4: Platform (Month 12+)

```
1. Cloud-hosted dashboard (SaaS)
   - Org-wide agent inventory
   - Cost trending and alerting
   - Compliance reporting
2. Marketplace for custom lint rules
3. Integration partnerships (Vercel, AWS, Azure)
4. Consider Series A if metrics support it
```

---

## 8. Key Metrics to Track

| Metric | Phase 1 Target | Phase 2 Target | Phase 3 Target |
|--------|---------------|----------------|----------------|
| GitHub stars | 1,000 | 3,000 | 10,000 |
| CLI monthly installs | 500 | 2,000 | 10,000 |
| MCP Server users | 100 | 500 | 2,000 |
| Free users (MAU) | 500 | 2,000 | 10,000 |
| Pro subscribers | 0 | 50 | 300-500 |
| Enterprise teams | 0 | 0 | 10-20 |
| MRR | $0 | $1,450 | $10K-$15K |
| ARR | $0 | $17K | $120K-$180K |
| Churn (monthly) | — | <10% | <5% |
| NPS | — | >40 | >50 |

---

## 9. Risk Mitigation

| Risk | Mitigation |
|------|-----------|
| LangChain/CrewAI add their own type checking | Our formal basis (λA calculus) is deeper; they'd need years to match. Also we're framework-agnostic — we check ALL frameworks, not just one. |
| Users don't want to pay for static analysis | Anchor to real incidents ($342 session, 10-20x cost bug). Free tier ensures we never lose users who won't pay — they still spread the word. |
| Enterprise sales cycle too long | Start with bottom-up adoption: developers install free CLI → tell their team → team asks for Enterprise. Not top-down sales. |
| Open source competitors fork our code | Our moat is the theoretical foundation (3 papers) + framework extractors (reverse engineering each framework). Hard to replicate without the λA expertise. |
| LLM costs drop, making cost prediction less valuable | Shift emphasis to safety (type checking, loop detection, parallel safety). Cost is one of four value propositions, not the only one. |

---

## 10. One-Line Summary

**Free lint is the hook, paid runtime guard is the line, enterprise dashboard is the net.** Let developers experience "knowing a pipeline will crash before spending a dollar" for free, then charge for "catch problems at runtime" and "team-wide cost control."
