Vector database(Vector DB)

A vector database stores text (or images, or audio) as numerical arrays called embeddings and lets you search by similarity rather than by exact-keyword match.

In practice: you’d use a vector database to give an AI system a “memory” of your business documents, so when someone asks a question, the system retrieves the most-relevant chunks and feeds them to the model as context.

What it’s used for

The dominant use case in 2026 is RAG (retrieval-augmented generation). The pattern:

1. Take your business documents (PDFs, Confluence pages, customer support tickets, product manuals).
2. Split into chunks of ~500 words.
3. For each chunk, generate an embedding (a 1024-dimensional numerical array) using an embedding model.
4. Store chunks + embeddings in a vector database.
5. When a user asks a question, embed the question, search the database for the most-similar chunks, feed those chunks to the AI as context.
6. AI answers using the retrieved context.

Result: AI can answer questions about your specific business data without needing the entire data in its training set or context window.

Popular vector databases in 2026

Database	Hosted?	Cost (rough)	Best for
Pinecone	Hosted	Starts at $0; $70+ USD/month at scale	Production apps, easy setup
Weaviate	Self + hosted	Free self-hosted; $25+ USD/month hosted	Open-source preference
Qdrant	Self + hosted	Free self-hosted	Performance-focused
Postgres + pgvector	Self-hosted	Free (just Postgres)	Already-on-Postgres teams
Chroma	Self-hosted	Free	Local dev, small projects
Turbopuffer	Hosted	Pay-per-use	Modern serverless

For most Australian SMB use cases, Postgres + pgvector is plenty. Already-running database + a free extension.

Do you need one?

Probably not, if:

• You have under 100 documents (just paste them into Claude Projects / Custom GPTs)
• Your context fits in Claude’s 1M-token window
• You’re prototyping, not building production

You likely do, if:

• You have thousands of documents to search over
• You’re building an AI product for customers, not internal use
• You need sub-second retrieval at scale

The simpler alternative

For most Australian SMBs, Claude Projects + uploading 20 files gives you 80% of what RAG provides at 5% of the complexity. You’re not running a startup; you don’t need Pinecone.

We’ve shipped exactly one client deployment with a real vector database in 2026. The other 47 clients are happy with Claude Projects + Custom GPTs.

See also

• Embedding for the underlying concept.
• Retrieval-augmented generation (RAG) for the broader pattern.
• Context window for what determines when you outgrow simple approaches.