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Vector Databases

Specialized databases for AI embedding search and similarity matching

⬢ LIVELLO 2Tecniche
+$25k-
Impatto sullo stipendio
3 mesi
Tempo di apprendimento
Medio
Difficoltà
5
Carriere
In sintesi

Specialized infrastructure for storing and querying high-dimensional embeddings. Essential for RAG pipelines, semantic search, and AI-driven recommendation systems. Vector database expertise commands $130-220k salaries; 2026's hottest ML/AI engineering skill for production systems handling billions of similarity queries.

Cos'è Vector Databases

Vector databases store and query high-dimensional embeddings, enabling semantic search, recommendation systems, and RAG architectures. Unlike traditional databases that match exact values, vector databases find semantically similar items using distance metrics (cosine similarity, dot product). With AI applications exploding, vector database knowledge is essential for building search, recommendation, and conversational AI features. Options range from dedicated solutions (Pinecone, Weaviate, Qdrant) to extensions on existing databases (pgvector).

🔧 STRUMENTI ED ECOSISTEMA
PineconeQdrantWeaviateChromaMilvuspgvectorFAISSembeddingsANN algorithmshybrid search

💰 Stipendio per regione

RegioneLivello baseMidLivello esperto
USA$95k$165k$220k
UK£70k£120k£160k
EU€60k€105k€145k
CANADAC$85kC$150kC$200k

❓ Domande frequenti

What's the difference between pgvector and Pinecone?
pgvector is a PostgreSQL extension, use it if you already have Postgres and want to avoid another database. Pinecone is a managed SaaS vector database with built-in scaling, filtering, and multi-tenancy. Choose pgvector for simplicity and cost; Pinecone for enterprise-grade availability and zero ops overhead.
Do I need a vector database or can I just use embeddings in JSON?
JSON storage works for tiny datasets (<10k vectors). As soon as you need to scale, filtering, or multi-tenant isolation, a proper vector database becomes essential. ANN indexing (HNSW, IVF) makes similarity queries 100-1000× faster.
How does hybrid search work?
Hybrid search combines vector similarity with metadata filters. Query a vector database for top-k semantic matches, then apply exact/range filters (e.g., date range, category) to narrow results. This avoids the cold-start problem and improves relevance.
What embedding model should I use?
Model quality matters more than database choice. Use OpenAI's text-embedding-3-large for English text, Cohere embed-english-v3.0 for enterprise, or open-source options (sentence-transformers) for privacy. Benchmark your domain-specific queries before picking.
Can I use vector databases for non-AI applications?
Yes, any high-dimensional data (image features, audio fingerprints, user behavior vectors) can be stored and searched. But the ROI is highest for NLP-driven features like semantic search, chatbots, and recommendation engines.
How do I evaluate vector database performance?
Measure recall (% of true top-k neighbors found), latency (query time), and throughput (qps). Benchmark on your actual data size and embedding model. Dataset and index type (HNSW vs IVF vs Product Quantization) have outsized impact.
What's the cost to run a vector database in production?
pgvector: PostgreSQL hosting cost only (~$100-500/mo). Pinecone: $0.40/month per 1M vectors + $0.50/month per 1M queries. Weaviate self-hosted: ~$500-2000/mo for HA setup. Costs scale with data size and query volume.

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