Architecting Zero-Cost Local AI Agents: From Prototype to Production (FastAPI, Ollama & ChromaDB)

Description and use

Recipe for deploying zero‑cost local AI agents using FastAPI, Ollama, and ChromaDB, achieving predictable pricing and data sovereignty.

Problem

High token costs, latency, and data‑privacy risks of commercial LLM APIs make large‑scale production of RAG agents unsustainable.

Outcome

The recipe enables zero‑cost inference, predictable linear pricing, and full data sovereignty for RAG agents at scale.

Workflow

• 1. Deploy FastAPI as async API gateway and orchestrator.
• 2. Configure ChromaDB as persistent vector store with local embeddings.
• 3. Run Ollama locally with a quantized Llama‑3‑8B model (Q4_K_M).
• 4. In FastAPI endpoint, query ChromaDB for top‑k relevant documents.
• 5. Build system prompt with retrieved context and send to Ollama via httpx async client.
• 6. Return response and source list to client.

Evidence

Assumiamo uno scenario tipico: 100.000 esecuzioni complesse al mese, con una media di 1.500 token di input (incluso contesto RAG) e 500 token di output per interazione.

Source

Stack Self‑Hosted (Ollama + Llama‑3‑8B) – Costo Annuo Stimato ~ $1,440.00

Source

FastAPI (Control Plane & Orchestration) – Gestisce il ciclo di vita delle richieste in modo non bloccante.

Source