Using PostgreSQL as a Vector Database: a Guide

In the era of generative AI and multi-modal applications, vector databases have emerged as key tools for storing and searching high-dimensional data like vector embeddings. 

Many organizations already rely on PostgreSQL for traditional relational data—so a common question arises: Can I use PostgreSQL as a vector database? 

The short answer is yes. Thanks to PostgreSQL’s extensibility (specifically the pgvector extension), a regular Postgres instance can be augmented to handle vector storage and similarity search, essentially becoming a PostgreSQL vector database. 

In this PostgreSQL vector database tutorial, we’ll explore how pgvector works, how to set it up, and what trade-offs to consider. 

We’ll also compare dedicated vector databases to. regular databases, discuss factors in choosing the best vector database for your needs, and touch on which vector solution OpenAI uses. 

Additionally, we’ll examine how a distributed database architecture (like YugabyteDB’s distributed PostgreSQL) addresses scalability and performance for production-scale vector workloads.

Can I use PostgreSQL as a Vector Database?

Yes – PostgreSQL can be used as a vector database by installing the pgvector extension. PostgreSQL has a robust extension ecosystem that allows it to support new data types and functions. The pgvector extension (a Postgres vector database extension) introduces a VECTOR data type along with index types and operators for similarity search. In essence, pgvector transforms PostgreSQL into a vector database by enabling you to store and query vectors directly in SQL. This eliminates the need for a separate specialized vector database in many scenarios.

You can use YugabyteDB to build robust AI applications that scale from thousands to billions of vectors using familiar PostgreSQL and powerful vector search capabilities, without architectural changes. You can read about the latest YugabyteDB features here. 

How pgvector Works

Once enabled, pgvector lets you create table columns of type VECTOR(n) where n is the dimensionality of your embeddings (e.g. 1536 for OpenAI’s text embeddings). You can insert high-dimensional vectors into these columns and then query them using dedicated operators for distance/similarity. The extension supports common distance metrics – Euclidean (L2) distance, cosine distance, and inner product – to measure similarity between vectors. For example, <-> is used for Euclidean distance, <=> for cosine distance, and <#> for inner product (noting that <#> returns the negative inner product to align with PostgreSQL’s sorting expectations).

Enabling pgvector: Using pgvector is straightforward. After installing the extension (it’s available for PostgreSQL 14+ and included in many cloud Postgres services), you activate it in your database and create tables with vector columns. Here’s a quick example:

— Enable the pgvector extension (actual extension name is “vector”)

CREATE EXTENSION IF NOT EXISTS vector;

— Create a table with a 3-dimensional vector column

CREATE TABLE items (

  id BIGSERIAL PRIMARY KEY,

  embedding VECTOR(3)

);

— Insert some sample vectors

INSERT INTO items (embedding) 

VALUES (‘[0.1, 0.3, 0.5]’), (‘[0.2, 0.1, 0.9]’), (‘[0.4, 0.4, 0.4]’);

— Query for the nearest neighbor to a new vector using Euclidean distance

SELECT id, embedding

FROM items

ORDER BY embedding <-> ‘[0.2, 0.2, 0.2]’  — ‘<->’ computes L2 distance

LIMIT 1;

In the above snippet, we inserted three 3-dimensional vectors and then queried for the most similar vector to [0.2, 0.2, 0.2] using the <-> operator. The query returns the item with the smallest Euclidean distance to the target vector. Similarly, you could use the cosine distance operator <=> in the ORDER BY clause to find nearest neighbors by cosine similarity. The familiarity of SQL syntax makes querying vectors very approachable – you can even combine vector similarity filters with standard SQL predicates (e.g., find similar items where some category = X).

Vector Indexing

By default, a pgvector similarity search will scan through vectors linearly, which can be slow for large datasets. To accelerate queries, pgvector supports approximate nearest neighbor indexes like HNSW and IVF (IVFADC). You can create an index on a vector column using these methods. For example, to index the embedding column with an HNSW index for Euclidean distance:

— Create an approximate HNSW index for faster vector search (L2 distance in this case)

CREATE INDEX ON items USING hnsw (embedding vector_l2_ops);

This index will significantly speed up similarity searches by traversing a small-world graph of vectors instead of scanning all rows. The trade-off is that HNSW (and other ANN indexes) achieve approximate results for much faster performance. In practice, you can tune index parameters to balance speed vs. recall. The ability to index vector data in Postgres is a game-changer – you get the benefit of efficient vector searches while staying inside the familiar relational database environment.

PostgreSQL Extensibility in Action

The pgvector extension exemplifies Postgres’s extensibility. It adds new capabilities without requiring changes to the core database. Beyond pgvector, PostgreSQL has many extensions that enable JSON storage, full-text search, GIS/spatial queries, etc. This means you can combine vector searches with all the other features Postgres offers (joins, transactions, etc.). 

For developers already comfortable with SQL and the Postgres ecosystem, using PostgreSQL as a vector store is very attractive: you avoid learning and managing a separate vector DB system when your vectors can live right alongside your relational data.

Use Cases

With pgvector, you can build AI features like semantic text search, image similarity lookup, recommendation systems, and more – all on top of Postgres. 

For example, you might generate text embeddings for documents using OpenAI or Hugging Face models and store those vectors in a Postgres table. A user’s query can be embedded and used to ORDER BY embedding <-> :query_vector LIMIT k to get the most semantically similar documents, just as you would in a purpose-built vector database. 

This hybrid approach (relational + vector in one) is powerful for applications that need both worlds.

What is the Difference Between a Standalone Vector Database and a Regular Database?

Vector databases and traditional relational databases have different strengths and use cases. 

• Traditional databases (like PostgreSQL, MySQL, Oracle) store data in structured tables of rows and columns, excelling at transactional workloads, exact matches, and relational queries (e.g. JOINS, aggregations) on structured data. 
• Vector databases are designed to handle high-dimensional embedding data and perform similarity searches based on mathematical closeness rather than exact matches.

Here are some key differences:

Data Model

Regular relational databases organize data into predefined schemas with columns (ints, text, etc.), and typically don’t natively understand the concept of a “vector” data type (without extensions). Vector databases (or “vector stores”) treat data as collections of vectors (arrays of numbers). Each data item might be represented as one or more vectors, often embeddings generated by ML models. This makes vector databases ideal for unstructured or semi-structured data (images, text, audio represented as embeddings) that don’t fit neatly into relational tables.

Query Type 

In a traditional database, queries usually involve exact matching or range conditions on values (e.g., find rows where age = 30 or price BETWEEN 100 AND 200). They use indexes like B-trees for fast lookups on those values. A vector database, however, is built for similarity search – “find the closest vectors to this query vector,” or “find items most similar in meaning to X.” These are nearest-neighbor searches in a high-dimensional space, not exact matches. 

The core of a vector database is the ability to efficiently compute distances (cosine, Euclidean, etc.) between a query vector and millions of stored vectors, and return the nearest ones. Traditional databases are not optimized for that kind of operation (without help like pgvector). Even though you could store vectors in a regular database as blobs or JSON, it wouldn’t know how to search by similarity without custom code or extensions.

Indexing and Performance

Because of the different query patterns, vector databases use specialized indexing structures and algorithms. Common approaches include HNSW graphs, IVF (inverted file index with product quantization), and other Approximate Nearest Neighbor algorithms designed to speed up similarity searches. These are quite different from the B-tree or hash indexes in a traditional SQL database. 

As a result, out-of-the-box relational databases struggle with performance on large-scale vector searches – they’d resort to full table scans. Vector databases, on the other hand, are built from the ground up to search billions of vectors efficiently via ANN (often trading a tiny bit of accuracy for big gains in speed). They can also scale horizontally for large datasets in ways a single-node SQL database might not.

A vector database is optimized for semantic similarity searches in high-dimensional spaces, whereas a regular database is optimized for structured data queries and transactions. Vector databases shine for AI/ML use cases like semantic search, recommendations, and anomaly detection. 

Traditional databases remain excellent for structured data, transactional workloads, and general-purpose querying. With extensions like pgvector (and similar capabilities in other RDBMSs), PostgreSQL and others are becoming hybrid systems that bridge this gap – allowing developers to get vector search functionality without abandoning the relational world.

What is the Best Vector Database?

With the explosion of AI applications, a plethora of vector database solutions have appeared – each with its own strengths. “Best” is subjective and depends on your specific needs: scalability, performance, ease of integration, cost, existing infrastructure, etc. Let’s break down the landscape and key considerations.

Standalone Vector Databases vs. PostgreSQL+pgvector vs. Distributed SQL Solutions with Vector Search

Dedicated Vector Databases

These are systems built from scratch (or heavily optimized from existing data stores) specifically for vector similarity search. Examples include Pinecone, Weaviate, Milvus, Chroma, Qdrant, and others. Standalone vector databases often offer:

• Performance and Scalability: Many are designed to handle millions or billions of vectors.For instance, Pinecone can scale and handle approximate search across large datasets. Milvus (open-source) is built for large-scale vector search with GPU acceleration support, etc. They use ANN indexes (HNSW, IVF, etc.) under the hood to ensure queries are fast even at large scale.
  
• APIs and Integrations: Vector databases typically provide simple client libraries or REST APIs. Some integrate with machine learning workflows or frameworks (Weaviate has a GraphQL API and built-in modules for things like text2vec, Qdrant offers gRPC and REST, etc.). Many have features to store metadata with vectors and filter results by metadata (useful for hybrid filtering, as described earlier).
  
• Managed Services: Pinecone, for example, has a managed service offering, others like Weaviate are available as hosted SaaS or can be self-hosted. These services can be convenient, but introduce external dependencies and cost.
  
• Limitations: Because they’re specialized, standalone vector databases often lack some important traditional database features. For example, they might not support complex multi-table transactions or joins (beyond simple filtering). You often need to use them alongside a relational database for storing other data. Also, using a new product means learning new APIs and tools. Most importantly, most standalone vector databases were not architected for the high-availability requirements of modern enterprise software. This may make them unsuitable for mission-critical applications.    

PostgreSQL + pgvector:

This approach turns a regular PostgreSQL into a vector-capable database. Key points:

• Integration with SQL and Relational Data: The biggest advantage is that your vectors live in the same database as the rest of your application data. You can write joins between vector tables and other tables, maintain consistency with foreign keys, use standard SQL for reporting, etc. Your team can leverage existing PostgreSQL skills and tooling. For applications that already use Postgres, adding pgvector is frictionless compared to introducing a whole new database.
  
• Moderate Scale and Performance: For moderate volumes, Postgres with pgvector works great. You can store millions of embeddings and use indexes for similarity search. However, a single Postgres node will eventually hit limits if you have extremely large datasets or very high query throughput requirements – remember, it’s not an engine purpose-built solely for vector search. The pgvector extension does include ANN indexing (HNSW, IVF), so performance can be quite good, but standalone systems (especially written in C++ or Rust with lower-level optimizations, GPU support, etc.) might outperform it at the upper end. Also, memory can become a concern for very large indexes.
  
• Cost-Effectiveness: pgvector (and Postgres itself) is open-source. You avoid proprietary vendor lock-in and the extra costs of a managed service. If you’re already paying for a Postgres instance, you can often add vector capability at no extra license cost. That said, you take on the operational cost of scaling Postgres if needed.
  

Distributed SQL Databases (Postgres-Compatible with Vector Search)

Another category to consider is using a distributed SQL database like YugabyteDB . This is essentially Postgres, but scaled out across multiple nodes for horizontal scalability and high availability. They inherit Postgres’s features and ecosystem.

YugabyteDB is an open-source distributed SQL database that is PostgreSQL-compatible (it speaks the Postgres query layer). YugabyteDB supports the pgvector extension, meaning you can distribute your vector data across a cluster of nodes while still using standard SQL queries. The benefit here is massive scale and resilience: you get sharding, fault tolerance, and the ability to utilize the CPUs and storage of multiple machines for your vector searches. 

For example, large embedding datasets (say, tens of millions or more) that would be too heavy for a single Postgres node can be partitioned across a YugabyteDB cluster. Yugabyte’s architecture ensures strong consistency and low-latency queries even as you scale. 

As noted by Yugabyte’s engineers, embeddings can consume a lot of storage and memory (an OpenAI embedding with 1536 dimensions takes ~57 GB for just 10 million vectors) – scaling out horizontally provides the needed capacity and compute power. It also means vector workloads won’t suffer if one node goes down; the database stays available (ultra-resilient, with replication across nodes or even regions).

With distributed Postgres, you can achieve both the familiarity of SQL and the horizontal scale approaching that of specialized vector databases. Distributed solutions bridge the gap: you get the production-grade features of a relational database (transactions, security, SQL) plus the scale-out and performance for vector search.

Which Database Should you choose? 

If your dataset is small-to-moderate or you highly value simplicity, starting with PostgreSQL + pgvector on a single node is often sufficient (and very convenient). If you anticipate needing to scale to huge datasets or extreme QPS, you might evaluate a purpose-built vector database (for raw performance) or a distributed Postgres (for a balance of performance + rich features). 

It also depends on your team’s expertise and your use case requirements (e.g., do you need complex relational queries in addition to similarity search?). There’s no one-size-fits-all answer, which leads us to the next point: factors to consider for the “best” choice.

Bringing it All Together (and Why Not Both?)

As we’ve seen, PostgreSQL can indeed work as a capable vector database with the help of key extensions, allowing you to integrate AI-driven search directly into your existing relational data. 

If you’re excited to build semantic search and other AI features, you don’t necessarily have to adopt a completely new database from scratch. Postgres + pgvector offers an immediate path for those already in the SQL world, while numerous purpose-built vector databases provide options for those pushing the scale or performance envelope. 

And remember, for production-scale workloads that require both resilience and scalability, you can have the best of both worlds. Distributed SQL databases like YugabyteDB bring PostgreSQL’s features (including pgvector) onto a fault-tolerant, horizontally scalable architecture. This means you can start with a simple Postgres vector demo, and when you need to go big, deploy on a distributed PostgreSQL platform without rewriting your application.

Ready to get started? 

If you want to try a scalable, resilient Postgres-based vector database, download YugabyteDB (which is open source and PostgreSQL-compatible). You can start small and grow to a cluster that handles millions or billions of vectors, with zero downtime and global distribution. It’s a great way to future-proof your AI application’s data layer. 

Give YugabyteDB a try for your next vector search project! Build with confidence, knowing that your database can scale as your vectors (and users) grow. Happy coding, and happy vector searching!