Four privacy-first SaaS products. 95 SEO pages.
Autonomous content + reply pipelines. All on a single Mac Mini M4
Pro in India. Here's the architecture, the stack, the cost, and
why it works.
Live snapshot
Products live
4
Services running
85
Uptime
22:25
RAM free
18.1 GB
Disk free
104Gi
Blog posts
10
Changelog entries
47
Memory episodes
132430
This snapshot regenerates on every page request. The numbers above
are the actual values reported by `vm_stat`, `df`, `launchctl list`,
and the SaaS registry SQLite — not synthetic placeholders.
Each of the four SaaS frontends is a Next.js 15 + React 19 app on
Vercel. None of them run any LLM logic in their own runtime. Every AI
call (Whisper transcription, Llama-3.1 generation, sentence embedding,
Playwright PDF render) is a fetch over the Cloudflare Tunnel to the
Mac Mini, which has the model files, the GPU access, the persistent
state.
Stack — exact versions
Component
Implementation
Hardware
Mac Mini M4 Pro · 64 GB unified · ARM64 · 460 GB SSD
OS
macOS 25 · launchd-managed daemons
Python
3.11 (FastAPI · Uvicorn · pydantic)
Node
22 LTS (Next.js 15 + React 19)
Local LLM
gpt-oss:20b, llama3.1:8b
Speech-to-text
OpenAI Whisper (base / small)
Embeddings
sentence-transformers all-MiniLM-L6-v2 (22 MB)
Vector store
ChromaDB persistent (per-doc collection)
HTML→PDF
Playwright Chromium · Inter font
HTML→MP4
HyperFrames (HeyGen, Apache-2.0)
Pub-sub / queues
Redis local (no RDS)
SQL
SQLite (WAL mode) · per-feature DB
Egress
Cloudflare Tunnel → anantatrade.com
Frontend hosting
Vercel (free tier × 4 projects)
Billing
Razorpay live (INR + UPI native)
Email
Telegram bot for ops alerts (cheaper, faster)
Memory layer
5-tier (Redis L1, SQLite L2, Graph L3, Wiki L4, Character L5)
What runs autonomously
Daily 08:00 IST — morning briefing Telegram (jobs digest +
affiliate activity + Razorpay events + Mac Mini health)
Daily 09:00 IST — YouTube Shorts variant drip (industry +
Indian-language content, 6 days queued)
Daily 11:00 IST — blog→Bluesky thread mirror (3-post
thread per new article)
Daily 14:00 IST — blog→YouTube Short (HyperFrames key-
points reveal)
Daily 23:55 IST — auto-generate the day's changelog entry,
IndexNow ping
Every 30 min — Bluesky reply-bot polls 14+ tracked posts
for new comments, drafts replies via local Llama
Every 2 h — affiliate orchestrator picks deals, posts via
HyperFrames price-drop video
Sunday 18:00 IST — Worldmonitor weekly digest video →
Bluesky × 2 + YouTube
Comparing this Mac Mini stack to the equivalent provisioned in
AWS / OpenAI / Vercel Pro:
Component
Cloud price (US)
This stack (one-time + recurring)
4-product SaaS hosting
Vercel Pro 4 × $20/mo = $80/mo
Vercel free × 4 = $0
LLM API (Llama-3.1 70B equiv)
OpenAI GPT-4o · ~$0.50/M tokens · realistically $200-500/mo
at moderate use
Local Ollama on M4 Pro · electricity ~$3/mo
Speech-to-text (Whisper-equiv hours)
Deepgram $0.43/hr · 100 hr/mo = $43/mo
Local Whisper · $0
Vector DB (10k vectors, 4 products)
Pinecone Standard $70/mo
ChromaDB persistent · $0
Hardware amortisation
n/a
Mac Mini M4 Pro $1,599 / 36 mo = $44/mo
Egress + DNS
$15-30/mo
Cloudflare Tunnel free · $0
Email (transactional)
Resend / Mailgun $30+/mo
Telegram bot · $0
Total
~$438-758 /mo
~$47 /mo (mostly hardware amortisation)
Operating margin at ₹399/mo per Notes customer:
customer pays ~$5, this stack costs ~$0.50/customer-month
marginal compute. ~90% gross margin, sustainable from
customer #1.
Privacy — what we don't do
Resume + JD never sent to OpenAI / Anthropic / Google
(ATS Tailor uses local Llama-3.1 only)
Audio file deleted within minutes of processing
(ANANTA Notes — temp directory auto-cleanup)
Documents indexed only in a per-doc ChromaDB collection on
the Mac Mini (ANANTA Docs — no cloud vector DB)
Slide topic + outline never fed to a third-party LLM
(ANANTA Slides — local Llama outline + Playwright PDF)
Free-tier outputs auto-purged on a strict TTL
(1 hour for Slides decks, 24 hours for Docs)
No web analytics on the apex (no Google Analytics,
no Meta pixel, no third-party tracker)
Why this works for a solo operator
Three structural advantages over the typical SaaS founder:
One stack, four products. Same Llama-3.1, same Whisper,
same Playwright, same ChromaDB. Adding a 5th SaaS is mostly a
new prompt + frontend, not a new infra layer.
Indian-tier pricing on Razorpay. ₹299–2,400/mo plans are
impulsive purchases for the target buyer; the same value at
US-tier $20+/mo would have 10× higher friction.
Privacy as a moat. Cloud-LLM SaaSes literally cannot
match the "your data never leaves the box" claim. They built on
OpenAI/Anthropic; we built on hardware we own.
Try the products
ATS Resume Tailor
— Llama-3.1 rewrites your resume against any JD in 3 seconds