Databases that Support AI Search/Features

Vector search has become a core requirement for modern applications — semantic search, RAG pipelines, recommendation engines, and multimodal retrieval all depend on it. Every major database now offers vector capabilities, but each takes a different approach to indexing, filtering, scalability, and deployment. This guide breaks down ten databases with AI search features and helps you decide which one fits your use case.

What Makes a Database AI-Ready?

An AI-capable database goes beyond storing embeddings. Key differentiators include:

• Vector indexing methods — IVF, HNSW, disk-ann, or brute force
• Hybrid search — combining vector similarity with keyword/full-text search
• Metadata filtering — pre-filtering or post-filtering vectors by structured fields
• Scalability — horizontal scaling, sharding, replication
• Ecosystem integration — LangChain, LlamaIndex, OpenAI, Hugging Face
• Deployment model — managed cloud, self-hosted, embedded

Database-by-Database Breakdown

1. pgvector (PostgreSQL)

pgvector is an open-source extension that adds vector support directly into PostgreSQL. It lets you keep structured data and embeddings in the same database without managing a separate vector store.

• Indexing: IVFFlat (fixed-size lists) and HNSW (hierarchical navigable small world graphs). HNSW offers faster search at the cost of slower index build and more memory.
• Hybrid search: Use standard SQL WHERE clauses alongside ORDER BY vector distance. PostgreSQL full-text search (tsvector) works in the same query.
• Filtering: Pre-filtering — PostgreSQL applies metadata filters before the vector index scan. This works well when filters are selective.
• Scalability: Scales vertically. Read replicas help, but sharding requires Citus or pg_partman.
• Pricing: Free (open source). Managed options: Supabase, Neon, Crunchy Bridge, Azure Cosmos DB for PostgreSQL.
• Use cases: Apps that already use PostgreSQL and need basic vector search without adding infrastructure.

-- Enable the extension
CREATE EXTENSION vector;

-- Create a table with an embedding column
CREATE documents (
    id SERIAL PRIMARY KEY,
    title TEXT,
    content TEXT,
    embedding VECTOR(384)
);

-- Create an HNSW index for fast search
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

-- Search for the 5 most similar documents
SELECT title, content, 1 - (embedding <=> '[0.12, 0.45, ...]') AS similarity
FROM documents
WHERE title ILIKE '%rag%'
ORDER BY embedding <=> '[0.12, 0.45, ...]'
LIMIT 5;

2. Pinecone

Pinecone is a fully managed vector database built for production-scale similarity search. You upload embeddings and query them via a REST API — no infrastructure to manage.

• Indexing: Uses its own proprietary disk-ann algorithm optimized for high-dimensional vectors. Supports pod-based and serverless indexes.
• Hybrid search: Supports sparse-dense hybrid search via its sparse-vector API. You provide both dense embeddings and sparse (BM25-style) vectors.
• Filtering: Metadata filtering with support for equality, range, and IN clauses. Filters apply during the search, not as a post-processing step.
• Scalability: Serverless indexes scale to billions of vectors. Pod-based indexes let you control capacity manually.
• Pricing: Pay-per-use (serverless) or hourly pod pricing. Free tier: 100K vectors with 1 pod.
• Use cases: Production RAG pipelines, recommendation systems, semantic search where you want zero operational overhead.

import pinecone

pc = pinecone.Pinecone(api_key="your-api-key")
index = pc.Index("semantic-search")

# Upsert vectors with metadata
index.upsert([
    ("id1", [0.12, 0.45, 0.78], {"category": "database", "rating": 5}),
    ("id2", [0.34, 0.56, 0.90], {"category": "search", "rating": 4}),
])

# Query with metadata filter
results = index.query(
    vector=[0.15, 0.48, 0.80],
    filter={"category": {"$eq": "database"}},
    top_k=5,
    include_metadata=True
)

# curl equivalent
curl -X POST https://your-index.svc.pinecone.io/query \
  -H "Api-Key: your-api-key" \
  -d '{"vector": [0.15, 0.48, 0.80], "topK": 5, "includeMetadata": true}'

3. Weaviate

Weaviate is an open-source vector database with built-in vectorization modules. You can pass raw text or images and let Weaviate generate embeddings using its integrated ML models.

• Indexing: HNSW with configurable efConstruction and M parameters. Supports product quantization (PQ) for memory reduction.
• Hybrid search: Native hybrid search using BM25 + vector similarity with alpha weighting. You control the blend between keyword and semantic results.
• Filtering: GraphQL-style where filters on any property. Supports geo-spatial, boolean, text, numeric, and date filters.
• Scalability: Shards across nodes using a hash-based consistent hashing scheme. Supports multi-tenancy through tenant isolation.
• Pricing: Open-source (self-hosted) or Weaviate Cloud (WCD) with a free sandbox tier.
• Use cases: RAG systems that benefit from built-in vectorization, knowledge graphs, multimodal search.

import weaviate

client = weaviate.Client("http://localhost:8080")

# Define a schema with auto-vectorization using OpenAI
class_obj = {
    "class": "Article",
    "vectorizer": "text2vec-openai",
    "properties": [
        {"name": "title", "dataType": ["text"]},
        {"name": "content", "dataType": ["text"]},
    ]
}
client.schema.create_class(class_obj)

# Import data — Weaviate vectorizes it automatically
client.data_object.create(
    data_object={
        "title": "PostgreSQL 17 New Features",
        "content": "PostgreSQL 17 introduces improvements to vacuuming and query parallelism..."
    },
    class_name="Article"
)

# Hybrid search: blend of keyword and vector
response = (
    client.query
    .get("Article", ["title", "content"])
    .with_hybrid(query="PostgreSQL performance improvements", alpha=0.5)
    .with_limit(5)
    .do()
)

4. Qdrant

Qdrant is a Rust-based vector database focused on performance and rich filtering. It runs as a standalone service or embedded.

• Indexing: HNSW with customizable ef and M values. Supports scalar quantization to reduce memory footprint by 4x with minimal accuracy loss.
• Hybrid search: You can run full-text and vector searches separately and fuse results, but there is no native combined scoring.
• Filtering: Qdrant’s filtering is its standout feature — supports must, must_not, should conditions with nested filters, geo-radius, and datetime range queries.
• Scalability: Sharding and replication out of the box. Nodes form a raft-based cluster.
• Pricing: Open-source (self-hosted) or Qdrant Cloud with a forever-free 1GB cluster.
• Use cases: High-throughput similarity search with complex filter logic, such as e-commerce product discovery.

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct, Filter, FieldCondition, Range

client = QdrantClient("localhost", port=6333)

# Create a collection
client.create_collection(
    collection_name="products",
    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
)

# Upsert vectors with payload
client.upsert(
    collection_name="products",
    points=[
        PointStruct(id=1, vector=[0.12, 0.45, 0.78], payload={"category": "electronics", "price": 299}),
        PointStruct(id=2, vector=[0.34, 0.56, 0.90], payload={"category": "books", "price": 19}),
    ]
)

# Search with filter
client.search(
    collection_name="products",
    query_vector=[0.15, 0.48, 0.80],
    query_filter=Filter(
        must=[
            FieldCondition(key="category", match={"value": "electronics"}),
            FieldCondition(key="price", range=Range(gte=100, lte=500)),
        ]
    ),
    limit=5
)

5. Milvus

Milvus is an open-source vector database designed for billion-scale vector search. It decouples storage and computation for independent scaling.

• Indexing: Multiple index types — IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, and DiskANN (for large-scale datasets that exceed RAM).
• Hybrid search: Milvus 2.4+ supports hybrid search via a hybrid query API that combines dense vector, sparse vector, and scalar filtering.
• Filtering: Bitmap indexing for scalar fields allows efficient filtering. You can apply boolean expressions alongside vector search.
• Scalability: Designed from the ground up for horizontal scaling. Worker nodes (query nodes, index nodes, data nodes) scale independently.
• Pricing: Open-source (self-hosted) or Zilliz Cloud (managed). Milvus is Apache 2.0 licensed.
• Use cases: Large-scale image search, recommendation engines, and any application requiring search over billions of vectors.

from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType

connections.connect("default", host="localhost", port="19530")

# Define schema with an indexed vector field
fields = [
    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True),
    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=384),
    FieldSchema(name="category", dtype=DataType.VARCHAR, max_length=50),
]
schema = CollectionSchema(fields)
collection = Collection("ai_docs", schema)

# Create index for the vector field
collection.create_index(
    field_name="embedding",
    index_params={"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 128}}
)
collection.load()

# Insert and search
collection.insert([[1, 2], [[0.12, 0.45, 0.78], [0.34, 0.56, 0.90]], ["dev", "ml"]])
results = collection.search(
    data=[[0.15, 0.48, 0.80]],
    anns_field="embedding",
    param={"metric_type": "L2", "params": {"nprobe": 10}},
    limit=5,
    expr="category == 'dev'"
)

6. Elasticsearch

Elasticsearch added native dense vector support in version 8.0, bringing vector search into its mature full-text search ecosystem.

• Indexing: HNSW with configurable m and ef_construction. Also supports int8 scalar quantization for memory efficiency.
• Hybrid search: Use knn query for vector similarity combined with bool query for full-text matching. Elasticsearch 8.12+ supports reciprocal rank fusion (RRF) for hybrid ranking.
• Filtering: Filters apply as a pre-filter before the kNN search, which works well for selective filters. Post-filtering is also available.
• Scalability: Proven at petabyte scale for text search. Vector search fits into the same distributed architecture with shards and replicas.
• Pricing: Open-source (Elastic License) or Elastic Cloud with a free trial.
• Use cases: Log analytics with semantic search, e-commerce with combined keyword and vector relevance, enterprise search.

# Hybrid search using RRF (Elasticsearch 8.12+)
POST products/_search
{
  "query": {
    "knn": {
      "field": "embedding",
      "query_vector": [0.15, 0.48, 0.80],
      "k": 10,
      "num_candidates": 100,
      "filter": {
        "term": { "status": "active" }
      }
    }
  },
  "rrf": {
    "rank_constant": 60,
    "window_size": 200
  },
  "retriever": {
    "standard": {
      "query": {
        "match": {
          "title": "PostgreSQL performance"
        }
      }
    }
  }
}

7. MongoDB Atlas Vector Search

MongoDB Atlas Vector Search integrates vector search directly into the document model. You store vectors as fields in your existing documents.

• Indexing: Uses a disk-based approximate nearest neighbor algorithm (based on Hierarchical Navigable Small World graphs).
• Hybrid search: Combine $vectorSearch with $search (Atlas Search) or $match aggregation stages in the same pipeline.
• Filtering: Pre-filtering using filter parameter in $vectorSearch. You can filter on any indexed field before the vector comparison runs.
• Scalability: Leverages MongoDB Atlas’s existing sharding architecture. Vector search indexes are built locally per shard.
• Pricing: Included in MongoDB Atlas tiers (M10+). Pricing based on RAM and data size.
• Use cases: Document-based applications that need to add semantic search without adding a new database.

from pymongo import MongoClient

client = MongoClient("mongodb+srv://user:pass@cluster.mongodb.net/")
db = client["knowledge_base"]
collection = db["articles"]

# Create a vector search index in Atlas UI or via API first, then query
pipeline = [
    {
        "$vectorSearch": {
            "index": "vector_index",
            "path": "embedding",
            "queryVector": [0.15, 0.48, 0.80],
            "numCandidates": 100,
            "limit": 5,
            "filter": {"category": {"$eq": "database"}}
        }
    },
    {
        "$project": {
            "title": 1,
            "content": 1,
            "score": {"$meta": "vectorSearchScore"}
        }
    }
]

results = collection.aggregate(pipeline)

8. Meilisearch

Meilisearch is a Rust-based search engine known for developer experience. It added vector search in version 1.3.

• Indexing: HNSW with automatic parameter tuning. You provide a vector per document.
• Hybrid search: Native hybrid search using hybrid semantics in search queries. Combines keyword ranking with vector similarity.
• Filtering: Document filters on numeric and string fields using filter syntax in search requests.
• Scalability: Single-node with multi-index support. Meilisearch Cloud offers managed horizontal scaling.
• Pricing: Open-source (MIT license). Meilisearch Cloud with a free tier.
• Use cases: Frontend search for content sites, documentation, e-commerce product search with semantic understanding.

import meilisearch

client = meilisearch.Client("http://localhost:7700", api_key="masterKey")
index = client.index("products")

# Add documents with vectors
index.add_documents([
    {
        "id": 1,
        "title": "PostgreSQL Performance Tuning",
        "category": "database",
        "_vectors": {"default": [0.12, 0.45, 0.78]}
    }
])

# Hybrid search
results = index.search(
    "database performance",
    {
        "hybrid": {"semanticRatio": 0.5, "embedder": "default"},
        "filter": "category = database"
    }
)

9. Chroma

Chroma is an open-source embedding database designed for AI applications. It is lightweight and focuses on developer simplicity.

• Indexing: Uses HNSW via the hnswlib library. Supports cosine, L2, and ip distance metrics.
• Hybrid search: No built-in hybrid search. You can combine with external keyword search.
• Filtering: Metadata filtering with where clauses. Supports equality, inequality, and logical operators.
• Scalability: Single-node by design. Chroma Cloud (managed) offers scaling. Not designed for billion-scale deployments.
• Pricing: Open-source (Apache 2.0). Chroma Cloud with a free tier.
• Use cases: Prototyping and small-to-medium RAG applications, local development, educational projects.

import chromadb

client = chromadb.Client()
collection = client.create_collection("docs")

# Add documents with metadata
collection.add(
    documents=["PostgreSQL 17 improves parallel query execution"],
    metadatas=[{"source": "blog", "topic": "database"}],
    ids=["doc1"]
)

# Query with metadata filter
results = collection.query(
    query_texts=["database performance improvements"],
    n_results=3,
    where={"source": {"$eq": "blog"}}
)

10. LanceDB

LanceDB is an embedded vector database built on the Lance columnar format. It runs in-process with zero server management.

• Indexing: IVF + PQ (product quantization) and disk-based indexing. Designed for large-scale datasets that don’t fit in memory.
• Hybrid search: Can combine full-text search (via tantivy/FTS) with vector search in the same query.
• Filtering: SQL-style where clauses before vector search. LanceDB pre-filters efficiently using the columnar format.
• Scalability: Embedded — scales vertically with machine resources. For distributed use, combine with systems like Ray.
• Pricing: Open-source (Apache 2.0). No cloud offering yet.
• Use cases: Data science workflows, large-scale batch similarity, local-first applications where an external server is undesirable.

import lancedb

db = lancedb.connect("./.lancedb")
table = db.create_table("vectors", [
    {"vector": [0.12, 0.45, 0.78], "item": "postgres tuning", "category": "database"},
    {"vector": [0.34, 0.56, 0.90], "item": "vector search", "category": "search"},
])

# Search with pre-filtering
results = (
    table.search([0.15, 0.48, 0.80])
    .where("category = 'database'")
    .limit(5)
    .to_pandas()
)

Side-by-Side Comparison

Decision Guide

Use pgvector when you already use PostgreSQL and your vector workloads are moderate. You avoid operational complexity by keeping everything in one database, but you give up horizontal scaling and specialized vector optimizations.

Use Pinecone when you need a zero-ops managed vector database for production RAG pipelines. It handles scaling automatically but costs increase with usage, and you cannot self-host.

Use Weaviate when you want built-in vectorization modules and graph-like navigation of data. It is ideal for knowledge graph applications where you also need hybrid search and LLM integrations.

Use Qdrant when your application requires complex metadata filtering alongside vector search — for example, e-commerce category + price range + geo-location filters combined with similarity ranking.

Use Milvus when you need to search over billions of vectors and want GPU acceleration. It is the best choice for large-scale image recognition, recommendation, and multimodal systems.

Use Elasticsearch when your team already runs Elasticsearch for log analytics and wants to add semantic search to the same cluster. The RRF hybrid search in 8.12+ makes it competitive for enterprise search.

Use MongoDB Atlas Vector Search when your application uses MongoDB as its primary database and you want to add semantic search without introducing a separate infrastructure component.

Use Meilisearch when you need a fast, developer-friendly search engine for user-facing search bars. Its vector search adds semantic relevance to full-text search with minimal configuration.

Use Chroma when you are prototyping a RAG application and want a simple, self-contained solution. It trades scalability for developer ergonomics.

Use LanceDB when you work with large-scale embeddings in data science or ML pipelines and want zero server management. Its columnar format enables fast scans and disk-efficient storage.

Best Practices for Vector Search in Production

• Benchmark index parameters — HNSW ef_construction and M values affect recall vs. latency. IVF nlist affects accuracy vs. speed. Run a grid search on your data.
• Choose the right distance metric — cosine similarity for normalized embeddings, L2 for magnitude-sensitive vectors, dot product for recommendation systems.
• Quantize when memory is tight — scalar quantization (int8) reduces memory by 4x with 1-2% recall loss. Product quantization goes further at the cost of some accuracy.
• Prefer pre-filtering over post-filtering — filtering before the vector search is faster when filters are selective. Post-filtering may return fewer results than top_k if many results get eliminated.
• Monitor index build times — building HNSW on millions of vectors can take hours. Plan index rebuilds during off-peak hours or use incremental indexing where supported.

Resources

• pgvector GitHub Repository
• Pinecone Documentation
• Weaviate Documentation
• Qdrant Documentation
• Milvus Documentation
• Elasticsearch Vector Search Guide
• MongoDB Atlas Vector Search
• Meilisearch Vector Search Documentation
• Chroma Documentation
• LanceDB Documentation
• ANN Benchmarks