· Vector Databases & Embeddings
Vector Databases & Embeddings

Vector Databases & Embeddings

Vector databases enable semantic search, similarity matching, and retrieval-augmented generation by storing and querying high-dimensional embeddings, becoming essential infrastructure for LLM applications. Pinecone leads dedicated vector database adoption with >5% prevalence in LLM/AI Application Development positions, offering managed vector search. Faiss provides Facebook's open-source similarity search library (>5% in LLM/AI roles), enabling efficient nearest-neighbor searches. Weaviate and pgvector represent alternative approaches: cloud-native vector databases and PostgreSQL extensions respectively. The landscape reflects the explosion of RAG (Retrieval-Augmented Generation) architectures, where vector databases retrieve relevant context for LLM queries. These technologies appear almost exclusively in LLM/AI Application Development roles, representing highly specialized infrastructure for AI applications. Entry-level accessibility is moderate for core vector database concepts (>5% prevalence in entry-level LLM developer positions), with Pinecone and Faiss being most commonly mentioned. Vector database expertise is rapidly becoming essential for building production LLM applications, particularly those requiring semantic search over proprietary data, long-term memory systems, and context-aware AI agents. The field is nascent but growing quickly as organizations operationalize LLM applications.

All Skills

Pinecone

Moderate Demand
Rank: #1
Entry-Level: Low
Managed vector database in LLM/AI Application Development (>5%). Lower entry-level accessibility but growing. SaaS vector search. Used for semantic search applications, retrieval-augmented generation (RAG), storing and querying embeddings, similarity matching at scale, LLM long-term memory, recommendation systems, and building AI applications requiring fast vector similarity search without infrastructure management.

Faiss

Moderate Demand
Rank: #2
Entry-Level: Low
Facebook's similarity search library in LLM/AI Application Development (>5%), Machine Learning Engineering (>5%), and MLOps. Lower entry-level presence. Efficient similarity search. Used for nearest neighbor search, clustering dense vectors, indexing large-scale embeddings, building custom vector search solutions, similarity matching in ML pipelines, and high-performance vector operations without managed service costs.

Weaviate

Low Demand
Rank: #3
Entry-Level: Low
Open-source vector database in LLM/AI Application Development (>5%). Limited entry-level opportunities. Cloud-native vector search engine. Used for semantic search with GraphQL API, hybrid search (vector + keyword), multi-modal search, storing objects with vectors, building knowledge graphs, RESTful vector operations, and AI applications requiring flexible vector database with strong typing.

pgvector

Low Demand
Rank: #4
Entry-Level: Low
PostgreSQL extension for vectors in LLM/AI Application Development (>5%). Limited explicit mention. Extends existing databases. Used for adding vector similarity search to PostgreSQL, storing embeddings alongside relational data, leveraging existing Postgres infrastructure, semantic search without separate vector DB, applications requiring both traditional queries and vector search in unified database.

Chroma

Low Demand
Rank: #5
Entry-Level: Low
Open-source embedding database with minimal market presence (<5% prevalence). Very limited entry-level demand. AI-native database. Used for storing embeddings with metadata, building LLM applications, developer-friendly vector storage, integrating with Langchain, prototyping RAG systems, and Python-first vector database for AI developers seeking simple embedding storage.

Milvus

Low Demand
Rank: #6
Entry-Level: Low
Cloud-native vector database with minimal explicit presence (<5% prevalence). Very rare in job postings. Scalable vector search. Used for large-scale vector similarity search, AI and deep learning applications, image and video search, recommendation engines, drug discovery, and applications requiring trillion-scale vector search with cloud-native architecture.