Key takeaways
- Vector databases differ in indexing, filtering, hybrid search, scaling, hosting and licensing.
- Dozens exist, from dedicated databases to vector features in PostgreSQL, Elasticsearch and others.
- They store vectors with metadata and build indexes such as HNSW, often with quantization.
- With LLMs, they power RAG, semantic caching and agent memory.
- Evaluate recall, filtered latency, ingestion, cost and operations on your own data.
Vector databases store embeddings and find the most similar ones quickly, which makes them central to RAG, semantic search and agent memory. Dozens now exist, and general databases keep adding vector features. RAG.NextUpgrad combines dense vector retrieval with BM25 and reranking, so this page covers how vector databases differ, how many exist, examples, how they store vectors, use with LLMs and LangChain, comparisons with SQL and LLMs, how to evaluate them and what is changing in 2026.
How do vector databases differ from each other?
Vector databases differ in index types, filtering performance, hybrid keyword and vector search, scaling and distribution, managed versus self-hosted options, multi-tenancy, consistency guarantees, quantization support, APIs and licensing.
For example, Qdrant emphasises fast filtered search and a Rust core, Milvus targets very large distributed deployments, Weaviate offers built-in modules and hybrid search, Pinecone is fully managed and serverless, Chroma prioritises simplicity for local development, and pgvector keeps vectors inside PostgreSQL. Some store data in memory, others on disk or object storage, affecting cost and latency. These differences matter more as data and traffic grow, so match the database to your workload rather than popularity. Read each database's documented limits before committing.
How many vector databases are there?
There are dozens. Database rankings list many systems with vector support, spanning dedicated vector databases, general databases with vector extensions, and libraries for vector search, and new options appear every year.
Dedicated databases include Pinecone, Qdrant, Milvus, Weaviate, Chroma, LanceDB, Vespa and turbopuffer. General databases with vector features include PostgreSQL through pgvector, MongoDB Atlas, Elasticsearch, OpenSearch, Redis, Cassandra-based services, ClickHouse, Oracle and SQL Server. Libraries such as FAISS, hnswlib and ScaNN provide search without database features. Because the field is crowded, many projects succeed with whatever database the team already runs, adding a specialised one only when requirements demand it.
What are some examples of vector databases?
Popular examples include Pinecone, a managed service; Qdrant, Milvus and Weaviate, open-source databases with managed clouds; Chroma and LanceDB for local and embedded use; and pgvector, which adds vector search to PostgreSQL.
Also asked as: what are vector databases give few examples
Each suits different needs. A student project might use Chroma for simplicity. A startup already on PostgreSQL might use pgvector to avoid another service. A company with billions of vectors might choose Milvus or a managed option. Teams wanting strong filtering and hybrid search often try Qdrant or Weaviate. Elasticsearch and OpenSearch users can add vectors to existing search clusters. Try two candidates on a sample of your data before deciding.
How do vector databases store vectors?
Vector databases store each vector as an array of numbers, usually 32-bit floats, alongside an ID and metadata, then build an index such as an HNSW graph over them so similar vectors can be found quickly without scanning everything.
To save memory, many compress vectors with quantization, storing 8-bit integers, binary codes or product-quantized codes, sometimes keeping full vectors on disk for re-scoring. Data is organised into segments, with write-ahead logs for durability and memory-mapped files for fast access. Newer serverless designs keep data in cheap object storage with caching. Metadata, called payloads in some systems, is indexed separately to support fast filtering. Higher dimensions increase storage and slow search, so choose dimensions deliberately.
How are vector databases used with LLMs?
Vector databases give LLMs relevant knowledge and memory. The most common use is retrieval-augmented generation, where similar document chunks are retrieved and added to the prompt. Others include semantic caching, agent memory and selecting few-shot examples.
In RAG, documents are embedded and stored; each question is embedded and matched, and the top passages, often reranked, ground the answer with citations. Semantic caching returns stored answers for similar repeated questions, saving cost. Agents store memories and retrieve relevant past interactions. Vector search also deduplicates data and powers recommendations around LLM features. RAG.NextUpgrad shows that combining vector results with keyword search improves retrieval for mixed queries.
What is LangChain's role with vector databases?
LangChain provides a standard VectorStore interface with integrations for most vector databases, so you can add documents, run similarity searches and turn any store into a retriever for RAG chains and agents with consistent code.
Also asked as: what is langchain and vector databases
You pair a vector store with an embeddings object, call methods to add documents and search, and use as_retriever to plug it into a chain. This makes switching databases easier, such as moving from Chroma in development to Qdrant or pgvector in production. LangChain also offers loaders, splitters and retrievers for hybrid and multi-query retrieval. Advanced features of specific databases may need their native clients, so check the integration's documentation.
What is the difference between a vector database and SQL?
SQL databases store structured data in tables and answer exact queries, such as filtering orders by date. Vector databases store embeddings and answer similarity queries, such as finding documents related to a question by meaning.
Also asked as: difference between vector databases · difference between vector database and sql
SQL excels at transactions, joins, aggregations and precise filters; vector databases excel at approximate nearest neighbour search over high-dimensional vectors. The boundary is fading: PostgreSQL with pgvector, Oracle, SQL Server and others now support vector types, letting one SQL query combine similarity search with relational filters. Many applications keep business data in SQL and add vectors there, avoiding synchronisation between two systems until scale demands a specialised vector database.
What is the difference between a vector database and an LLM?
A vector database stores and searches embeddings; it does not generate text. An LLM generates text from prompts; it does not store your documents in a searchable way. RAG systems combine them: the database retrieves, and the LLM answers.
Also asked as: difference between vector database and llm
LLMs hold general knowledge in their weights, fixed at training time, and cannot cite your private documents reliably. Vector databases hold your current data, updated instantly, and return exact passages. An embedding model connects them by converting text into vectors. Thinking of the database as long-term searchable memory and the LLM as the reasoning and writing engine clarifies their roles. Neither replaces the other in production knowledge applications.
How do you evaluate vector databases in 2026?
Measure recall against exact search, latency at p95 and p99 with realistic filters, throughput, ingestion and update speed, memory and storage cost, hybrid search support, multi-tenancy, operational effort and security features, using your own data.
Also asked as: how to evaluate vector databases in 2026
Public benchmarks such as ANN-Benchmarks and VectorDBBench help shortlist, but filtered queries, concurrent updates and your vector dimensions change results significantly. Build a test with production-like data volumes, query patterns and filters, and run it on each candidate's recommended configuration. Include failure and recovery tests, backups and upgrade paths. Consider total cost of ownership, including engineering time. The best database is the one that meets requirements with the least operational burden.
What is changing in vector databases in 2026?
Key trends include general databases adding strong vector support, hybrid dense and sparse search becoming standard, aggressive quantization and disk-based indexes cutting costs, serverless designs built on object storage, and growing use as memory for AI agents.
Also asked as: what's changing in vector databases in 2026
Multi-vector retrieval, such as ColBERT-style late interaction, is gaining support, improving accuracy for complex documents. GPU-accelerated indexing speeds up building large indexes. Many teams now question whether they need a separate vector database at all, choosing pgvector or their existing search engine. Meanwhile, dedicated databases compete on filtering, scale and developer experience. Expect continued consolidation through acquisitions and feature overlap, which favours flexible designs that let you switch databases.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
