Zero‑Cost AI Stack: 33 Agents for Under $10 a Month

---

Hook

AI infrastructure is usually a gold‑mine in the cloud. The average startup spends $3 k a month on inference, $2 k on storage and $1 k on ops.
We tore the bill down to less than $10 and built 33 self‑serving agents that stay awake 24/7.
No credits, no hidden fees, no “you’re paying for capacity you’re not using.”

---

1. The Anatomy of a Cheap Agent

An agent is just a line of code that pulls data, calls a model, and writes back a response.
To keep a single agent alive for a few cents a month we split the bill into three buckets:

| Bucket | Typical Rate | Example in Our Stack |
|--------|--------------|----------------------|
| Compute | $0.00010 per 1 k tokens | Groq’s Llama‑3.1 8B ($0.066 per 1 k tokens) |
| Store | $0.25 per GB per month | Supabase Postgres (≈$0.30/GB/mo) |
| Ops | $0.00002 per hourly invocation | Vercel Edge Functions (https://vercel.com/docs/concepts/functions/edge-functions) |

If an agent processes 10 m tokens a month, its compute cost is 10 m × $0.066 / 1 k = $660. But we batch calls, terminate idle workers, and share a single Groq instance across 33 agents. That brings token cost down to ~$4.50. Adding 0.10 GB of post‑processing storage and 30 us‑seconds of function runtime gives a total of $6.20 per month for an entire squad of agents.

---

2. Layered Tech – The Minimal Stack

| Layer | Tool | Why It Fits |
|-------|------|-------------|
| Front‑end | Next.js & Vercel | Server‑less rendering, instant deploys, built‑in edge functions for micro‑seconds workers. |
| Data & Auth | Supabase | Open‑source Postgres + real‑time, free up to 1 GB, fits our 0.1 GB per team. |
| Compute | Groq | $0.066/1 k tokens, 4 k parallel throughput, low latency. |
| LLM | Gemini (optional tier) | When we need multimodal context, we pay $0.001/1 k tokens. |
| Queue | Redis (self‑hosted on a micro‑VM) | 0.20 GB for 33 agents, 30 us per message. |

How it behaves:
1. The agent UI posts a request via an Edge Function (Vercel).
2. Edge Function writes a message to Redis; the worker queue pulls it.
3. Worker invokes Groq, receives the answer, writes back to Supabase.
4. The front‑end polls Supabase for changes (real‑time).

Each square on the diagram costs less than $0.01/month. Shoelace‑size hardware turns into a 33‑agent farm.

---

3. Real‑World Demo: The Hive’s Knowledge Bot

Our team built a knowledge bot for a crypto‑exchange that had 10k+ FAQ requests per day.
We divided the bot into 33 micro‑agents, each focused on a domain: KYC, staking, withdrawals, trading fees, governance, drops, compliance, marketing, bug‑hunting, data‑privacy.

Performance:

Cost:
| Item | Cost per month |
|------|----------------|
| Groq (Llama‑3.1 8B) | $4.90 |
| Gemini (JSON schema, 10k calls/day) | $2.10 |
| Supabase storage | $0.36 |
| Vercel Edge | $1.00 |
| Redis on a 0.5 GB VM | $0.30 |
| Total | $8.66 |

The clue: we didn’t run a separate function per request; we kept a pool of 10