# dd0c/route — Product Brief

**Product:** dd0c/route — LLM Cost Router & Optimization Dashboard
**Brand:** 0xDD0C — "All signal. Zero chaos."
**Author:** Product Brief synthesized from BMad Creative Intelligence Suite (Phases 1–4)
**Date:** February 28, 2026
**Status:** Investor-Ready Draft

---

## 1. EXECUTIVE SUMMARY

### Elevator Pitch

dd0c/route is an OpenAI-compatible proxy that sits between your application and LLM providers, intelligently routing each API request to the cheapest model that meets quality requirements — saving engineering teams 30–50% on AI costs with a single environment variable change. It pairs this routing engine with an attribution dashboard that answers the question no existing tool can: "Who is spending our AI budget, on what, and is it worth it?"

### Problem Statement

Enterprise and startup LLM spending is exploding — the global LLM market is projected to reach $36.1B by 2030 (Straits Research), with inference costs representing the fastest-growing line item on engineering budgets. Yet the tooling for managing this spend is stuck in 2022:

- **60%+ of LLM API calls use overqualified models.** Teams default to GPT-4o for everything — including trivial tasks like JSON formatting, classification, and extraction — because benchmarking alternatives takes days nobody has.
- **Zero cost attribution exists at the feature level.** Engineering managers receive a single monthly invoice from OpenAI ("$14,000") with no breakdown by feature, team, or environment. Cloud cost tooling solved this for AWS a decade ago. AI cost tooling hasn't caught up.
- **Multi-provider billing is a manual nightmare.** Teams using OpenAI + Anthropic + Google get three separate bills with three different billing models. Reconciliation is a monthly spreadsheet exercise that takes 3–4 hours.
- **Cost spikes are invisible until the invoice arrives.** A retry storm, a prompt engineering experiment gone wrong, or a new feature launch can burn $3K in an hour with no alert.

The result: engineering managers present estimated pie charts to CFOs, ML engineers feel guilty about costs they can't measure, and platform engineers maintain hand-rolled proxy scripts that started as 200 lines and grew to 2,000.

### Solution Overview

dd0c/route is a drop-in proxy (change one environment variable: `OPENAI_BASE_URL`) that provides:

1. **Intelligent Routing:** A complexity classifier analyzes each request and routes it to the cheapest adequate model — GPT-4o requests that are simple extractions get silently downgraded to GPT-4o-mini or Claude Haiku, saving 80–95% per request with negligible quality impact.
2. **Cost Attribution Dashboard:** Real-time treemap visualization showing spend by team → feature → endpoint → model. The "CFO slide deck" that writes itself.
3. **Weekly Savings Digest:** An automated Monday morning email showing exactly how much dd0c/route saved, broken down by category — the retention mechanism and viral loop (managers forward it to leadership).
4. **Budget Guardrails & Anomaly Alerts:** Per-team, per-feature spending limits with Slack/PagerDuty integration. Catches the $3K retry storm before it becomes a $3K invoice.

### Target Customer Profile

**Primary Beachhead:** Series A–B SaaS startups with 10–50 engineers, spending $2K–$15K/month on LLM APIs, with no dedicated ML infrastructure team. The CTO or VP Engineering can approve a $49/month tool via expense report — no procurement process, no 6-month evaluation cycle.

**Why this segment:** They feel the pain acutely ($5K–$15K/month hurts but doesn't justify hiring ML ops), they're technically sophisticated enough to adopt in minutes (they understand API proxies and environment variables), and they talk to each other (startup CTOs share tools in the same Slack communities, meetups, and newsletters).

### Key Differentiators

1. **5-Minute Setup, Zero Code Changes.** Change one environment variable. No SDK migration, no code refactor, no YAML configuration marathon. The fastest time-to-value in the category.
2. **Attribution-First Design.** Competitors focus on observability (what happened). dd0c/route focuses on attribution (who spent what, on which feature, and was it worth it). The treemap dashboard is the product's signature.
3. **"Shadow Audit" Pre-Sale Wedge.** A CLI tool (`npx dd0c-scan`) and passive log analysis mode that proves savings potential *before* asking the customer to route traffic. Value before trust. Evidence before commitment.
4. **Transparent, Flat Pricing.** $49/month Pro tier — an expense-report purchase. No per-seat fees that punish adoption, no usage-based billing that recreates the unpredictability problem we're solving.
5. **Open-Source Proxy Core.** The routing engine is open-source and self-hostable. The SaaS monetizes the intelligence layer (dashboard, analytics, digest, recommendations). Trust through transparency.

---

## 2. MARKET OPPORTUNITY

### TAM / SAM / SOM

| Metric | Value | Basis |
|--------|-------|-------|
| **TAM** | $36.1B by 2030 | Global LLM market (Straits Research). Inference costs are the fastest-growing segment. |
| **SAM** | ~$5.4B | LLM API spend by companies with $1K–$100K/month bills — the segment where third-party cost optimization is viable (not too small to care, not large enough to build in-house). Estimated at ~15% of TAM. |
| **SOM (Year 1)** | $1.8M–$3.6M | 300–600 paying customers at $49–$199/month average. Achievable via PLG in the Series A–B SaaS beachhead. |

The FinOps Foundation's 2026 report identifies AI workload cost management as the #1 emerging challenge. Cloud FinOps is a mature $3B+ category; AI FinOps is its greenfield successor with no dominant player.

### Competitive Landscape

| Competitor | Positioning | Strengths | Weaknesses | dd0c/route Advantage |
|-----------|-------------|-----------|------------|---------------------|
| **LiteLLM** | Open-source LLM proxy framework | 15K+ GitHub stars, broad model support (1,600+), active community | No intelligence layer, no attribution dashboard, no SaaS product — it's a framework, not a solution | Product completeness: proxy + dashboard + digest + attribution |
| **Portkey** | Enterprise AI gateway | $3M funding, enterprise features, broad provider support | Enterprise sales motion, complex setup, overkill for small teams | 5-minute PLG setup vs. enterprise procurement cycle |
| **Helicone** | LLM observability platform | YC-backed, strong developer brand, good logging/tracing | Observability-focused (what happened), not optimization-focused (what to do). No intelligent routing. | Attribution + routing + actionable recommendations vs. passive logging |
| **Martian** | AI model router | Smart routing technology, usage-based pricing | Opaque pricing, routing-only (no dashboard/attribution), limited transparency | Transparent routing + full cost attribution dashboard |
| **OpenRouter** | Multi-model API gateway | Simple unified API, broad model access | 5% markup on all requests, no cost optimization intelligence, no attribution | Flat pricing + intelligent routing that reduces spend |

### Timing Thesis

Three converging forces make Q1 2026 the optimal launch window:

1. **The "AI in Production" Transition.** Companies are moving from experimentation to production deployment. Production AI costs are operational expenses that demand optimization tooling — creating the "tooling gap" dd0c/route fills.

2. **Multi-Model Reality.** The era of "just use OpenAI" is ending. Teams now use OpenAI + Anthropic + Google + open-source models. Multi-provider complexity creates demand for a unified routing and attribution layer.

3. **Agentic AI Volume Explosion.** Agentic workflows make 10–100x more API calls than simple chat. Even as per-token prices drop, total spend increases. The bill isn't going away — it's shifting from "expensive models" to "massive volume."

### Market Trends

- LLM inference costs dropped ~90% in 2024–2025, but total enterprise AI spend increased 3x due to volume growth
- "AI FinOps" is an emerging category with no category leader — the FinOps discipline is expanding from cloud infrastructure to AI workloads
- Developer tooling is consolidating around PLG motions — enterprise sales cycles are shortening for sub-$500/month tools
- Open-source AI infrastructure (LiteLLM, vLLM, Ollama) has normalized the concept of proxy layers between applications and LLM providers

---

## 3. PRODUCT DEFINITION

### Core Value Proposition

> Change one environment variable. See where every AI dollar goes. Start saving automatically.

dd0c/route transforms AI cost management from a monthly guessing game into a real-time, automated discipline. It's the "Linear for AI FinOps" — fast, opinionated, and built for practitioners, not procurement committees.

### User Personas

**Persona 1: Priya Sharma — The ML Engineer (Age 29, Series B fintech)**
- Defaults to GPT-4o for everything because benchmarking alternatives takes days she doesn't have
- Feels guilty about costs but isn't empowered to fix them — no per-call cost visibility exists
- Needs: automatic model selection without workflow disruption, cost feedback at the code level
- dd0c/route value: "I keep writing `model='gpt-4o'` and the router quietly downgrades when it's safe. I stopped feeling guilty."

**Persona 2: Marcus Chen — The Engineering Manager (Age 36, same fintech)**
- Gets one opaque bill from OpenAI ("$14,000") with zero breakdown by feature, team, or environment
- Spends 3–4 hours monthly on manual spreadsheet reconciliation across providers
- Presents estimated pie charts to the CFO and feels like a fraud
- dd0c/route value: "The attribution treemap IS my slide deck. Monday morning digest goes straight to the CFO."

**Persona 3: Jordan Okafor — The Platform Engineer (Age 32, mid-stage SaaS)**
- Maintains a hand-rolled Node.js LLM proxy that started as 200 lines and grew to 2,000
- Gets paged when the proxy breaks; paranoid about it being a single point of failure
- Wants a Helm chart, OTel export, and config-as-code — then to never think about it again
- dd0c/route value: "I deployed it with Helm, pointed the env var, and went back to my actual job."

### Key Features by Release

#### MVP (Month 1–3)
- OpenAI-compatible proxy (Rust, <10ms overhead at p99)
- Rule-based routing with heuristic complexity classifier (token count + keyword patterns)
- Cascading try-cheap-first routing (cheap model → escalate on low confidence)
- Cost attribution dashboard: real-time ticker, treemap by feature/team/model
- Request inspector (tokens, cost, latency, routing decision per call)
- Weekly Savings Digest email (automated Monday morning)
- Budget guardrails with threshold-based anomaly alerts (Slack integration)
- OpenAI + Anthropic support only
- SaaS-hosted proxy

#### V2 (Month 4–6)
- Self-hosted data plane (Rust proxy in customer VPC, only telemetry to SaaS)
- Semantic response cache (exact-match V1, semantic similarity V2)
- A/B model testing (split traffic, measure cost/quality/latency, recommend winner)
- OTel export (Datadog, Grafana, Honeycomb integration)
- Google Gemini + Mistral provider support
- Quality threshold profiles ("customer-facing" vs. "internal-tool" vs. "batch-job")
- Prompt efficiency heatmap and optimization recommendations

#### V3 (Month 7–12)
- ML-based complexity classifier (trained on routing data flywheel)
- GitHub Action: cost impact comments on PRs
- Spend forecasting with confidence intervals
- VS Code extension with inline cost annotations
- SOC 2 Type II certification
- Enterprise features: SSO, RBAC, role-based dashboard views
- Model distillation recommendations ("hosting Llama 3 on a $2K/month GPU would save you $8K/month")

### User Journey

```
AWARENESS                    ACTIVATION                   RETENTION                    EXPANSION
─────────────────────────────────────────────────────────────────────────────────────────────────
                                                          
npx dd0c-scan ./src          Change OPENAI_BASE_URL       Weekly Savings Digest        Team-wide rollout
  ↓                            ↓                            ↓                            ↓
"You're wasting $4K/mo"      First request routed         Marcus forwards to CFO       Budget guardrails
  ↓                            ↓                            ↓                            ↓
Show HN / blog post          Dashboard shows savings      Routing rules refined        Pro → Business tier
  ↓                            ↓                            ↓                            ↓
Free signup                  "Aha" in <5 minutes          Attribution data compounds   dd0c/cost cross-sell
```

### Pricing Model

| Tier | Price | Includes | Target |
|------|-------|----------|--------|
| **Free** | $0/month | Up to $500/month LLM spend routed, basic dashboard, 7-day data retention | Individual devs, evaluation |
| **Pro** | $49/month | Up to $15K/month LLM spend, full attribution treemap, weekly digest, 90-day retention, Slack alerts | Series A–B startups (beachhead) |
| **Business** | $199/month | Unlimited spend, self-hosted proxy option, OTel export, RBAC, 1-year retention, priority support | Growth-stage companies |
| **Enterprise** | Custom | SSO, SOC 2 compliance, dedicated support, SLA, custom integrations | Large organizations (V3+) |

**Pricing rationale (resolving Party Mode debate):** The Bootstrap Founder panelist argued for $49 flat; the VC argued for enterprise contracts. Resolution: $49 Pro tier captures the beachhead via expense-report purchases. $199 Business tier captures expansion revenue as teams grow. Enterprise tier deferred to V3 — closing enterprise deals takes 9 months and requires SOC 2, which a solo founder can't prioritize in Year 1. The Contrarian's suggestion to charge $99 for pure analytics (no proxy) is captured in the Free tier's shadow audit mode — prove value first, convert to routing later.

---

## 4. GO-TO-MARKET PLAN

### Launch Strategy

**Phase 1: Engineering-as-Marketing (Days 1–30)**
- Build and ship `npx dd0c-scan` — the CLI that scans a codebase, estimates LLM spend, and shows savings potential. No account needed. No data leaves the machine. This is the top-of-funnel viral tool.
- Dogfood dd0c/route on Brian's own projects. If the founder doesn't use it daily, it's not ready.

**Phase 2: Private Beta (Days 31–60)**
- Invite 10–20 people from Brian's network: AWS colleagues, startup CTO friends, Twitter mutuals.
- Free access in exchange for 15 minutes of weekly feedback.
- Track: time to first route, first "aha" moment, first complaint.
- Milestone: 5+ beta users who say "I would pay for this" unprompted.

**Phase 3: Public Launch (Days 61–90)**
- Show HN post (Tuesday/Wednesday morning US time — highest traffic days)
- First comment: technical architecture, honest limitations, roadmap
- Simultaneous posts: Twitter/X, Reddit (r/MachineLearning, r/devops), relevant Slack communities
- "Why I Built dd0c/route" blog post (personal story, technical architecture, honest tradeoffs)
- "State of AI Costs Q1 2026" report (anonymized data from beta users)
- Target: 500+ signups in week 1, 10–20 paying customers by day 90

### Beachhead Market

Series A–B SaaS startups in the US, spending $2K–$15K/month on LLM APIs, with 10–50 engineers. Specifically:
- Companies building AI-powered features (chatbots, summarization, code review, RAG pipelines)
- No dedicated ML infrastructure team — the platform engineer or CTO manages LLM infrastructure as a side responsibility
- CTO/VP Eng can approve $49/month without procurement
- Active in developer communities (Hacker News, Twitter/X, Discord, Slack groups)

Estimated beachhead size: 5,000–10,000 companies in the US alone.

### Growth Loops & Viral Mechanics

1. **The Savings Digest Loop:** dd0c/route sends a Monday morning email → Marcus (eng manager) sees "$1,847 saved this week" → forwards to CFO → CFO mandates team-wide adoption → more teams onboard → more savings → bigger digest number → more forwards.

2. **The Shadow Audit Loop:** Developer runs `npx dd0c-scan` → sees "$4,200/month wasted" → shares screenshot on Twitter/Slack → other developers try it → some convert to paid.

3. **The "You Could Have Saved" Loop:** Free tier users see a persistent counter: "Estimated savings if you'd used dd0c routing: $X" → the number grows daily → conversion pressure increases naturally.

4. **The Open-Source Loop:** OSS proxy gets GitHub stars → developers discover the project → some self-host (free marketing) → power users convert to SaaS for the dashboard/digest/analytics.

### Content & Community Strategy

- **Weekly newsletter:** "This Week in AI Pricing" — model price changes, benchmark updates, cost optimization tips
- **Monthly report:** "State of AI Costs" — anonymized aggregate data from dd0c/route users. Becomes the industry reference.
- **SEO targets:** High-intent, low-competition keywords first ("LiteLLM alternative," "reduce OpenAI costs," "LLM cost attribution")
- **Guest posts:** The New Stack, Dev.to, InfoQ — backlinks + immediate traffic while SEO compounds
- **Community:** Discord server for users. The best first hire will come from this community.

### Partnership Opportunities

- **Framework integrations:** Official LangChain / LlamaIndex / Vercel AI SDK partner — "recommended cost optimization tool"
- **Cloud marketplaces:** AWS Marketplace listing (Brian's AWS expertise is an unfair advantage here)
- **FinOps community:** FinOps Foundation membership, conference talks, co-authored reports
- **Complementary tools:** Integrate with Datadog, Grafana, PagerDuty — be the AI cost data source that feeds existing observability stacks

### 90-Day Launch Timeline

| Week | Focus | Deliverable |
|------|-------|-------------|
| 1–2 | Build proxy | Working Rust proxy, OpenAI + Anthropic, <10ms overhead |
| 2–3 | Build dashboard | Cost overview, treemap, request inspector |
| 3–4 | Build digest | Automated Monday email with savings breakdown |
| 5–6 | Private beta | 10–20 users routing traffic, collecting feedback |
| 6–7 | Build CLI | `npx dd0c-scan` — the viral top-of-funnel tool |
| 7–8 | Iterate | Fix top 3 complaints, polish onboarding to <5 min |
| 9 | Pre-launch content | Blog post, AI costs report, Show HN draft, landing page |
| 10 | Show HN launch | All-day in comments. Simultaneous Twitter/Reddit/Slack |
| 11–12 | Post-launch | Analyze funnel, fix biggest drop-off, reach out to every paying customer |

---

## 5. BUSINESS MODEL

### Revenue Model & Unit Economics

| Metric | Value | Notes |
|--------|-------|-------|
| **Average Revenue Per Account (ARPA)** | $75/month (blended) | Mix of $49 Pro and $199 Business customers |
| **Gross Margin** | ~85% | Infrastructure cost is minimal — proxy + ClickHouse + API on AWS, ~$150/month total at scale |
| **Monthly infrastructure cost** | $65–$185/month | AWS (proxy + API + analytics), email (Resend), analytics (PostHog free tier) |
| **Marginal cost per customer** | ~$0.50–$2/month | Proxy compute + telemetry storage. Near-zero marginal cost. |

### CAC / LTV Projections

| Metric | Target | Basis |
|--------|--------|-------|
| **CAC (organic/PLG)** | <$50 | Content marketing + Show HN + CLI virality. No paid ads in Year 1. |
| **Average customer lifetime** | 10+ months | Weekly digest drives retention; savings are visible and ongoing |
| **LTV** | >$750 | $75 ARPA × 10 months |
| **LTV:CAC ratio** | >15:1 | Best-in-class for PLG SaaS |

### Path to Revenue Milestones

| Milestone | Customers Needed | Timeline | What It Means |
|-----------|-----------------|----------|---------------|
| **$1K MRR** | ~20 Pro | Month 3–4 | Product-market fit signal |
| **$5K MRR** | ~80 Pro + 5 Business | Month 6–9 | Sustainable side project. "Should I keep going?" → Yes. |
| **$10K MRR** | ~150 Pro + 10 Business | Month 9–12 | "Should I quit my day job?" territory |
| **$25K MRR** | ~300 Pro + 20 Business | Month 12–18 | Quit the day job. This is a business. |
| **$50K MRR** | ~500 Pro + 40 Business | Month 18–24 | Hire first engineer. |
| **$100K MRR** | ~800 Pro + 80 Business | Month 24–36 | Series A optionality (or stay bootstrapped and profitable) |

### Resource Requirements (Solo Founder Constraints)

**Time budget:** 15 hours/week maximum until $5K MRR. This is a side project until the numbers say otherwise.

| Phase | Product Dev | Content | Community | Customer |
|-------|------------|---------|-----------|----------|
| Months 1–3 | 10 hrs (67%) | 3 hrs (20%) | 1.5 hrs (10%) | 0.5 hrs (3%) |
| Months 4–6 | 7 hrs (47%) | 4 hrs (27%) | 2 hrs (13%) | 2 hrs (13%) |
| Months 7–12 | 5 hrs (33%) | 4 hrs (27%) | 3 hrs (20%) | 3 hrs (20%) |

**Infrastructure budget:** $65–$185/month. Brian's AWS expertise keeps this minimal. The burn rate is essentially zero — patience is a competitive advantage funded startups don't have.

### Key Assumptions & Dependencies

1. **Engineers will route production traffic through a third-party proxy if savings are visible, immediate, and undeniable.** This is the core bet. Probability: 60/40 favorable.
2. **The cost delta between "expensive" and "cheap" models persists.** Frontier models will always command premium pricing; the spread between frontier and commodity persists even as absolute prices drop.
3. **Agentic AI drives volume growth that offsets per-token price declines.** Total LLM spend continues to increase even as unit costs decrease.
4. **PLG distribution works for this category.** The $49 price point and 5-minute setup enable self-serve adoption without a sales team.
5. **Brian can sustain 15 hours/week for 9–12 months.** The discipline of time-boxing is critical to avoiding burnout.

---

## 6. RISKS & MITIGATIONS

### Top 5 Risks

| # | Risk | Severity | Probability | Source |
|---|------|----------|-------------|--------|
| 1 | **OpenAI builds native smart routing** — "Smart Tier" that auto-routes within their models | 8/10 | Medium | VC + Innovation Strategy |
| 2 | **Trust barrier blocks adoption** — Security/compliance teams refuse to route prompts through a startup's proxy | 9/10 | Medium-High | CTO + DevOps panelists |
| 3 | **LLM price race-to-zero** — Cost delta between models shrinks to the point where optimization saves <$100/month | 8/10 | Low-Medium | Contrarian panelist |
| 4 | **Solo founder burnout** — 15 hrs/week + day job + support burden exceeds sustainable capacity | 7/10 | Medium | Bootstrap Founder panelist |
| 5 | **Well-funded competitor copies features** — Helicone/Portkey builds Shadow Audit + Attribution Treemap with a 10-engineer team | 6/10 | Medium | VC panelist |

**Mitigations:**

1. **OpenAI routing:** OpenAI's incentive is to sell the MOST expensive model, not the cheapest — smart routing cannibalizes their revenue. Even if they add it, dd0c/route routes ACROSS providers (OpenAI won't route you to Anthropic). Worst case: pivot to pure "AI FinOps analytics" — the attribution dashboard is valuable even without the proxy.

2. **Trust barrier:** V1 accepts this limitation — stick to the beachhead (startups without compliance teams). V1.5 (month 4–5): self-hosted data plane where the Rust proxy runs in the customer's VPC and only telemetry leaves their environment. Open-source the proxy core so customers can read every line of code. *Resolution note: The Party Mode CTO demanded VPC-deployable from Day 1. The Bootstrap Founder argued SaaS-only for V1 to reduce scope. Resolution: SaaS-only V1 for the beachhead, self-hosted V1.5 for expansion. The beachhead doesn't have compliance teams; the expansion market does.*

3. **Price race-to-zero:** Reposition from "use cheaper models" to "optimize AI spend" — framing survives price changes. Build semantic caching (saves money regardless of per-token pricing). Build prompt optimization features ("your average prompt is 40% longer than necessary"). The attribution dashboard remains valuable even if tokens cost a penny — "who is running these 100K token prompts?" is a latency and efficiency question, not just a cost question.

4. **Solo founder burnout:** Hard rule: no more than 15 hours/week until $5K MRR. Automate everything — zero-ops proxy, static dashboard, Discord community for support (not a ticket queue). The $5K MRR milestone is the "quit or don't" decision point. Build in public — the community becomes unpaid QA, feature prioritization, and emotional support.

5. **Competitor copies:** Rely on GTM speed, not feature moats. If Portkey builds a treemap but still requires a 30-minute sales call, Brian wins the developer who just wants to run `npx dd0c-scan` on a Saturday night. Double down on PLG friction advantage and community trust. Incumbents move slower than expected and over-complicate simple features.

### Kill Criteria

| Criterion | Threshold | Timeline |
|-----------|-----------|----------|
| No product-market fit signal | <50 free signups after Show HN launch | Month 1 |
| No conversion | <5 paying customers after 3 months of availability | Month 4 |
| Revenue plateau | <$2K MRR after 6 months | Month 7 |
| Churn exceeds growth | Net revenue retention <80% for 3 consecutive months | Month 6+ |
| Existential competitor launches | OpenAI/AWS launches free native routing covering 80%+ of dd0c/route's value | Any time |
| Burnout | >20 hrs/week AND below $5K MRR AND affecting day job/health | Month 6+ |
| Market thesis invalidated | Optimization saves <$100/month for the average customer | Any time |

**Walk-away rule:** If 2+ kill criteria are met simultaneously, stop. Not pivot. Stop. Pivoting a side project is how founders waste years.

**Exception:** If qualitative signals are strong (NPS >50, organic word-of-mouth) but quantitative metrics are below threshold, extend by 3 months.

### Pivot Options

1. **Pure AI FinOps Analytics (no proxy):** Ingest existing logs, provide attribution dashboard and CFO reports. Removes latency risk, proxy trust barrier, and API maintenance burden. Charge $99/month. (The Contrarian's recommendation.)
2. **Open-source everything, monetize consulting:** If the SaaS doesn't convert, release the full product as OSS and sell implementation consulting to enterprises at $200–$400/hour.
3. **Vertical specialization:** Instead of horizontal "all AI costs," specialize in one vertical (e.g., "AI cost optimization for healthcare" with HIPAA compliance built in). Smaller market, higher willingness to pay.

---

## 7. SUCCESS METRICS

### North Star Metric

**Monthly Recurring Revenue (MRR).** Everything else is a leading indicator. MRR is the truth.

### Leading Indicators (Track Weekly)

| Metric | Target | Why It Matters |
|--------|--------|---------------|
| Signups | 50+/week post-launch | Top of funnel health |
| Activation rate (signup → first routed request) | >40% | Onboarding quality |
| Time to first route | <5 minutes median | The core adoption thesis |
| Weekly active routers | Growing 10%+ week-over-week | Product engagement |
| Savings per customer per month | >$100 average | Value delivery (must exceed subscription cost) |
| Digest email open rate | >50% | Retention mechanism health |

### Lagging Indicators (Track Monthly)

| Metric | Target | Why It Matters |
|--------|--------|---------------|
| Logo churn | <5%/month | Retention health |
| Revenue churn | <3%/month | Revenue health (expansion offsets logo churn) |
| CAC | <$50 (organic) | Acquisition efficiency |
| LTV | >$500 (10+ month lifetime) | Business viability |
| LTV:CAC ratio | >10:1 | PLG efficiency |
| Net revenue retention | >100% | Expansion > churn |

### Milestones

**30 Days:**
- Working proxy + dashboard deployed and dogfooded on Brian's own projects
- `npx dd0c-scan` CLI shipped and tested
- 10–20 private beta users routing traffic

**60 Days:**
- 5+ beta users who would pay unprompted
- Top 3 beta complaints fixed
- Onboarding polished to <5 minutes
- All launch content written

**90 Days:**
- Show HN launched
- 500+ signups
- 10–20 paying customers
- $1K–$2K MRR
- Clear understanding of who converts and why

**Month 6:**
- $5K MRR (80 Pro + 5 Business customers)
- Self-hosted proxy option shipped (V1.5)
- Weekly newsletter established with growing subscriber base
- Content/SEO generating measurable organic traffic
- Data flywheel showing early signs (routing accuracy improving with scale)

**Month 12:**
- $10K–$15K MRR (200 Pro + 20 Business customers)
- Decision point: go full-time or maintain as profitable side project
- OTel integration, A/B testing, and semantic caching shipped
- "State of AI Costs" report established as industry reference
- Community of 500+ developers in Discord

---

## APPENDIX: The Unfair Bet

Every startup has one core belief that, if true, makes everything else work. If false, nothing else matters.

> **"Engineering teams will route production LLM traffic through a third-party proxy if the savings are visible, immediate, and undeniable."**

**Assessment: 60/40 favorable.**

Evidence FOR: Cloudflare/CDNs normalized third-party traffic routing. LiteLLM's 15K+ stars prove developers accept proxy layers. 30–50% savings are a powerful motivator. Multi-model usage makes a routing layer increasingly necessary.

Evidence AGAINST: LLM prompts contain proprietary data — more sensitive than typical web traffic. Security teams are increasingly paranoid about AI data flows. "Just use the cheap model" is free and requires zero trust.

**The mitigation that tips the odds:** The open-source, self-hosted proxy. If it runs in the customer's VPC and only telemetry leaves their environment, the trust barrier drops dramatically.

---

*This brief synthesizes insights from four BMad Creative Intelligence Suite phases: Brainstorming (Carson), Design Thinking (Maya), Innovation Strategy (Victor), and Party Mode Advisory Board Review. Contradictions between phases have been noted and resolved inline.*

*The LLM cost optimization market will produce a $100M+ company in the next 5 years. The question is whether a bootstrapped solo founder can capture enough of that market to build a meaningful business before funded players consolidate. This brief argues yes — if Brian moves fast, stays focused, and lets the savings numbers do the selling.*
