Observed Signal · Apr 5, 2026 · Technical Article · Source: DEV Community · Impact: 1/5 · Sentiment: Neutral
One Developer’s AI Stack Choices
A developer describes architecture and tooling decisions for a self-hosted AI/LLM system: FastAPI for an async API backend with hand-written SQL via asyncpg (no ORM); PostgreSQL for relational storage using LISTEN/NOTIFY and DB constraints instead of additional queues; n8n for visual, self-hosted workflows despite production fragility; Ollama for local LLM model serving on macOS; ChromaDB initially for vector search later migrated to Elasticsearch to enable hybrid vector + keyword queries. The post lists trade-offs, operational pain points (deployment, schedule concurrency, sandboxed code nodes), and areas the author would change (CI/CD, Linux hosts, automated deploys).
Practical developer case study of AI/LLM infrastructure and tooling choices; useful as operational guidance but has limited direct, industry‑wide impact on AdTech/MarTech.
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Key Takeaways & Evidence Grounding
- Backend implemented with FastAPI using async I/O and hand-written SQL via asyncpg (author deliberately avoided an ORM).
- PostgreSQL is used as the primary database; LISTEN/NOTIFY and CHECK constraints are used for event notifications and data validation instead of adding a message queue.
- n8n (self-hosted) is the workflow engine; the author reports several production issues (concurrent scheduled workflows, API truncation of long SQL, sandboxed code-node environment).
- Local LLM serving is provided by Ollama (localhost:11434) chosen for simplicity over vLLM for a single-user Mac mini setup.
- Author migrated from ChromaDB to Elasticsearch to support hybrid search (BM25 keyword matching + kNN vector similarity) in a single system.
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