ollama VS pgvector

Download: Go to https://ollama.com/ and click on the download link for your operating system.

Most AI resume tools call out to OpenAI or Anthropic and charge you for every request. Persona supports Ollama — which means you can run the AI model locally on your own hardware, with zero API costs and zero data leaving your machine.

To solve these, we built a stack that prioritizes integrity over ease. The centerpiece is Ollama, running the mxbai-embed-large model locally. This is the engine that translates human thought into high-dimensional coordinates.

Ollama for model serving

Fully offline: Point it at Ollama or LM Studio. Zero cost, nothing leaves your network.

Recommended hardware: The RTX 3060 with 12 GB VRAM is the budget king here — all these models fit with room to spare for KV cache overhead, even Gemma 4:12B (which needs ~8.5–9 GB with overhead). An RTX 4060 Ti 16 GB gives you more headroom. On the Apple side, any M2 or M3 MacBook with 16 GB unified memory handles these models comfortably via Ollama's Metal backend.

This guide shows how to swap out every cloud API with a local Ollama server running qwen3-coder:30b. Same tools, same workflows, no data leaving your network.

Related: 35B MoE on 2× 1080 Ti · Ollama

The sample is a tiny console app running entirely against a local Ollama model — no cloud keys, and every HTTP call is traced so I can see exactly what goes over the wire (complete sample code). There's a single skill on disk:

irm https://ollama.com/install.ps1 | iex

pgvector: github.com/pgvector/pgvector

Plain's BYOA (Bring Your Own Agent) capability decouples the knowledge retrieval layer from the inference layer entirely. Your knowledge articles remain accessible via the content API, and you route queries through any agent you choose: a fine-tuned model, a Claude instance with product-specific context, or a custom retrieval pipeline built on pgvector or Pinecone. The platform holds the knowledge layer while you own the inference. Mintlify runs third-party AI agents directly on Plain's knowledge infrastructure, which is exactly how this architecture pattern looks in production.

pgai, pgvector, pgvectorscale: the database-side AI stack.

pgai and pgvector: the database side, built and maintained by Tiger Data (formerly Timescale) and Andrew Kane respectively.

Vector Search in PostgreSQL: pgvector Official GitHub

Experiment with Vector Databases: Set up a local instance of Chroma or pgvector to get hands-on experience, or explore managed services like Pinecone for scalability.

Start with Elasticsearch earlier. I started with ChromaDB for vector search because it's lighter. But once I needed hybrid search (keyword + semantic in the same query), I had to migrate anyway. If your data has both structured metadata and unstructured text (and you know you'll need to search both), start with something that handles both natively. That said, if you only need vector similarity for a smaller dataset, ChromaDB or pgvector will save you 2GB of RAM and a lot of query DSL.

pgvector on GitHub

If you're running PostgreSQL locally, install the pgvector extension from the pgvector GitHub repo and run CREATE EXTENSION vector;. If you're using a managed PostgreSQL service, the extension is typically pre-installed — on Rivestack, it's enabled by default on every database.

By using pgvector, you can turn your relational database into a powerful vector store. This is especially seamless if you use Supabase, which can host small databases for free or provide a "Pro" tier for a reasonable price.