PostgreSQL vector search guide: Everything you need to know about pgvector

You're probably already using Postgres as your database for everything from user profiles to transaction histories. But what if that same database could understand the meaning behind your data, not just store it?

Imagine searching your database by meaning and similarity, not just exact matches or keywords. This is the promise of vector search technology, and pgvector brings this capability directly to your existing PostgreSQL database. In today's world of exponentially growing unstructured data, traditional search methods fall short. Vector search lets you find semantically similar content, power recommendation engines, and build AI-enhanced applications without overhauling your infrastructure.

In this article, you will learn everything you need to know about pgvector, how it compares to popular vector databases, how to deploy PostgreSQL with pgvector as an addon on Northflank in less than 5 minutes, and how to test your vector database.

What is pgvector?

pgvector is an extension for PostgreSQL that adds vector similarity search capabilities to this widely-used relational database. It allows you to store embedding vectors (numerical representations of data) alongside your traditional data and perform efficient similarity searches. These vectors can represent virtually anything—text documents, images, audio, user behavior patterns, or any other data that can be meaningfully embedded into vector space.

The beauty of pgvector lies in its seamless integration with your existing PostgreSQL infrastructure. Rather than introducing a completely new database system, pgvector extends what you already have, allowing you to leverage PostgreSQL's robust features like transactions, backups, and security while gaining powerful vector search capabilities.

The importance of Vector Databases

Vector databases bridge the gap between traditional data storage and how humans naturally think about information. While conventional databases excel at exact matches ("find customer #12345"), they struggle with meaning-based queries ("find articles similar to this one").

Vector databases solve this by converting data into mathematical vectors where similar concepts exist close together in multi-dimensional space. This enables powerful "nearest neighbor" searches based on semantic similarity rather than exact matches.

This capability transforms multiple industries - from e-commerce product recommendations and content discovery systems to intelligent customer support and advanced anomaly detection in security. By understanding the meaning behind data, vector databases enable applications to find relevant information even when there's no exact keyword match.

How do Vector Databases work?

Think of vector space like a cosmic map where words or concepts are stars. Similar concepts (like "happy" and "joyful") appear close together, while unrelated ones ("happy" and "taxation") are far apart. When searching, we're essentially asking "what stars are closest to this one?" rather than looking for exact matches.

Vector databases operate through a straightforward four-step process:

1. Embedding generation: An AI model converts raw data (text, images, etc.) into numerical vectors - essentially transforming content into points in mathematical space where similar items cluster together.
2. Vector storage: These numerical representations are stored in specialized formats optimized for rapid similarity searches rather than exact matches.
3. Similarity calculation: When searching, your query gets converted to a vector too. The database finds matches by measuring distances between vectors using methods like cosine similarity (angle between vectors) or Euclidean distance (straight-line distance).
4. Optimized search algorithms: To handle millions of vectors efficiently, pgvector uses approximate nearest neighbor (ANN) algorithms like HNSW and IVF, letting you balance between search speed and precision for your specific needs.

Are managed Vector Databases overhyped?

The vector database space has seen an explosion of funding, with companies like Pinecone, Weaviate, and Chroma raising hundreds of millions of dollars to build dedicated vector search engines. But do you really need a separate database just for vector search?

If you’re already using PostgreSQL, pgvector brings powerful vector search directly into your existing database—without extra infrastructure, proprietary lock-in, or vendor pricing models. Many "AI-native" vector databases market themselves as groundbreaking, but under the hood, they’re often just specialized indexes with a sleek API.

Before you adopt a vector database, it’s worth understanding the pros and cons of the various options so you can pick the tool best suited to your particular use case. If you plan to step out of your existing PostgreSQL implementation, you want to be sure that updating your architecture warrants the benefits.

In the next section, we’ll compare pgvector with these managed alternatives and see whether the hype is justified.

Comparing pgvector to other Vector Databases

1. pgvector vs Weaviate

Weaviate functions as a complete knowledge graph with vector capabilities built from the ground up, offering a different approach than pgvector's extension model.

Weaviate advantages:

• Purpose-built vector search engine with specialized optimizations for vector operations. For example, Weaviate can search through 10 million product embeddings in milliseconds, while pgvector might take seconds for the same operation.
• GraphQL API that simplifies complex vector-related queries
• Built-in classification and data enrichment capabilities

pgvector advantages:

• Leverages your existing PostgreSQL infrastructure rather than introducing a new technology
• Familiar SQL interface for teams with PostgreSQL experience
• Benefits from PostgreSQL's mature ecosystem and decades of development

2. pgvector vs Pinecone

As a fully managed vector database service, Pinecone focuses exclusively on vector operations.

Pinecone advantages:

• Optimized specifically for massive-scale vector workloads
• Managed service reduces operational complexity and maintenance
• Specialized performance for high query-per-second (QPS) requirements

pgvector advantages:

• Keeps vector data alongside traditional data, eliminating synchronization challenges
• More cost-effective for many use cases compared to specialized service pricing
• Provides full relational database capabilities in addition to vector operations

3. pgvector vs Chroma

This open-source embedding database targets AI application development with a streamlined approach.

Chroma advantages:

• Simplified API designed specifically for AI/ML workflows
• Strong focus on document retrieval use cases
• Lightweight implementation for certain applications

pgvector advantages:

• Battle-tested PostgreSQL foundation provides enterprise-grade reliability
• Richer query capabilities through full SQL integration
• Larger community and support ecosystem

When to choose pgvector

Still wondering if pgvector is right for your needs? Here's when it makes the most sense as your vector database solution:

1. When your data is already in PostgreSQL: If your application already relies on PostgreSQL, introducing pgvector is a natural extension rather than adopting an entirely new database technology.
2. For hybrid search needs: When you need both traditional queries and vector similarity search in the same application, pgvector allows you to combine these naturally in SQL.
3. When operational simplicity matters: Managing a single database system rather than multiple specialized systems reduces operational complexity and costs.
4. For moderate-scale vector operations: For applications with thousands to millions of vectors, pgvector offers excellent performance without the need for specialized infrastructure.
5. When SQL integration is valuable: If your application benefits from combining vector searches with complex SQL queries, joins, and PostgreSQL's rich feature set.

When not to choose pgvector

While pgvector offers many advantages, it isn't the ideal solution for every use case. Consider alternatives when:

1. You're starting from scratch: Without existing PostgreSQL infrastructure or expertise, the advantages of integrating with your current database diminish, potentially making specialized vector databases more attractive.
2. You need a highly scalable vector database: For applications requiring billions of vectors or thousands of queries per second, dedicated vector databases like Pinecone or Weaviate may deliver superior performance at scale.
3. Vector search is your primary workload: When vector similarity is your application's core functionality rather than an additional feature, purpose-built vector databases offer specialized optimizations that may deliver better results.
4. Real-time performance is critical: For applications where consistent millisecond-level response times are essential, dedicated vector databases with hardware-optimized indexing might be necessary.

How to create a PostgreSQL database on Northflank

To create a PostgreSQL database on Northflank, go to your dashboard, create a new project with any name of your choice, select a region of your choice, and click Create project.

After successfully creating your project, go to the Addons tab and click Create Addon. Select PostgreSQL, then enter the required information, such as the name and version, based on your needs. Finally, click the Create Addon button.

Note: Instead of making your database publicly accessible, we recommend using the Northflank CLI's forward command to securely access your database locally. Publicly exposing your database increases security risks. If you must enable internet access to the database (not recommended), ensure that TLS is enabled.

How to connect to your Postgres database locally

You can forward your Postgres database for local access using the Northflank CLI.

• To forward a specific database:sudo northflank forward addon --projectId [project-name] --addonId [addon-name]
• To forward all ports in a project:sudo northflank forward all --projectId [project-name]

How to install pgvector in Postgres

Once you've created your PostgreSQL database on Northflank and you have made it accessible locally, you're ready to enable pgvector and start working with vector embeddings. First, connect to your database using your database connection string:

psql "$DATABASE_URL"

Next, you'll need to enable the vector extension. This only needs to be done once per database:

CREATE EXTENSION vector;

How to use pgvector

Now that pgvector is enabled, let's create a simple table with a vector column. This example uses 3-dimensional vectors, but in real applications, you might use vectors with hundreds or thousands of dimensions to represent complex data:

CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));

Let's insert some sample vector data. Notice how vectors are represented as simple arrays:

INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');

Get the nearest neighbors by L2 distance

Finally, let's perform a basic vector similarity search using the L2 distance operator (<->). This query finds the vectors closest to [3,1,2]:

SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;

This simple example demonstrates the fundamental operation of vector similarity search - finding items most similar to your query vector. In real applications, these vectors would represent embeddings of text, images, or other data generated by machine learning models, enabling semantic search across your content.

The beauty of pgvector is that you can easily combine these vector searches with traditional SQL queries, joining vector similarity results with other tables in your database to create rich, context-aware search experiences.

Conclusion

You've now seen how pgvector turns your existing PostgreSQL database into a powerful vector search engine. This extension lets you keep your traditional data and vector embeddings in one place, combining familiar SQL capabilities with modern semantic search.

Deploying PostgreSQL with pgvector on Northflank takes just minutes, giving you a production-ready vector database without the complexity of managing separate systems. Whether building recommendation engines, semantic search, or AI-powered applications, pgvector offers a practical solution that leverages your existing PostgreSQL expertise.

As the boundary between structured and unstructured data continues to blur, solutions like pgvector represent the future of database technology – where meaning and context become first-class citizens alongside traditional data types.

Why not give it a try? Your current database might be just an extension away from powering your next AI innovation.