How to Choose a Vector Database

Recent advancements in large language models (LLMs) and generative AI have revolutionized how we store, manage, and work with unstructured data, making vector databases an essential component of modern AI and machine learning applications.

Vector embeddings, a critical feature for AI models, mathematically represent the semantic meaning of different types of data (such as images, text, or audio), enabling more efficient and meaningful data retrieval. The remarkable aspect of this representation is that data perceived as similar by humans will have similar vector embeddings, even if the data's structure differs. In text search, for example, vector embeddings can identify phrases with similar meanings even when they use completely different words—something that traditional keyword-based searches often fail to do.

The power of vector embeddings for tasks like search and classification has driven the development of specialized vector databases designed to store data organized by embedding representation, optimizing them for semantic search and AI applications.

Vector databases efficiently and quickly search through unstructured data to find similar items based on semantics or meaning. This search capability has numerous applications, including AI-driven functionalities such as image search, anomaly detection, semantic text similarity, recommendation systems, and retrieval-augmented generation (RAG) for chatbots.

However, choosing the right vector database can be challenging, given today’s wide range of options. This guide aims to help you navigate your choices based on your specific application needs, query patterns, and system requirements.

Understanding Your Vector Database Application Needs

The type of vector database you choose should be heavily influenced by the application you are building. The right database can vary significantly based on the use case:

Retrieval-augmented generation (RAG) for chatbots: RAG helps enhance chatbot responses by retrieving relevant information from vector databases to generate more context-aware and informative answers.
Semantic search for product catalogs: vector databases enable semantic searches, allowing users to find products based on meaning rather than exact keyword matches, enhancing the search experience.
Recommendation systems: vector embeddings help identify similarities between user preferences and products, improving the relevance of recommendations.
Data augmentation or classification: vector databases store embeddings that assist in categorizing data and augmenting training datasets for machine learning models.
Image recognition and analysis: vector databases store image embeddings, which allow for efficient image retrieval, similarity searches, and object recognition.
Fraud detection and anomaly detection: by storing and comparing vector embeddings, vector databases can help detect unusual patterns in data, aiding in fraud prevention and anomaly detection.
Personalized content recommendations: vector databases facilitate the generation of personalized content by matching user preferences with relevant content based on vector similarity.

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Natural language understanding (NLU) for voice assistants: vector embeddings improve the ability of voice assistants to understand and process spoken language, enabling more accurate and context-aware responses.
Intelligent document retrieval: vector databases enable the retrieval of documents that are semantically similar, making it easier to find relevant information in large document repositories.
Real-time event detection: vector databases can be used for analyzing and identifying events in real time, which is especially useful for monitoring and reacting to critical situations.
Medical data analysis: vector embeddings can be used to analyze medical images and health records, providing insights that assist in diagnostics and personalized treatment.
Customer support chatbots: vector databases enhance customer support by allowing chatbots to retrieve the most relevant information based on semantic understanding of customer queries.
Sentiment analysis: using vector embeddings, sentiment analysis can be more accurate, enabling companies to understand customer feedback effectively.
Voice command recognition: Vector databases play a role in efficiently processing and matching voice commands, enhancing the capabilities of virtual assistants and smart devices.

Different applications use vector databases in distinct ways, and selecting the right one involves understanding factors like query rates, partitioning ability, filtering needs, and data synchronization. Let’s look at each of these in detail.

Key Evaluation Criteria for Choosing a Vector Database

So, what exactly should you look for in a vector database when building a specific AI application? Here are some evaluation criteria for making a sound vector database choice:

1. Query rate

How frequently your data will be queried is a crucial aspect of evaluating vector databases. For internal use cases, such as a semantic search for a small company's documents, query rates are typically low. However, consumer-facing applications, like powering a search bar on an e-commerce site, often experience high query rates. You have two main options on this front, which we’ll examine in more detail later:

Serverless vector databases: These are optimized for low-query rate scenarios. They are suitable for projects with occasional queries, as they automatically scale resources as needed, keeping costs down for infrequent use.
Dedicated vector databases: These databases are better suited for applications with high and consistent query rates, offering predictable and stable performance. However, they can be more expensive if the database remains idle for long periods, as you still pay for the dedicated resources even if they're not in use.

2. Partition-ability

Consider whether your data can be partitioned. Multi-tenant applications often benefit from databases that can partition the data to scope searches to specific segments, reducing overall latency. Serverless databases like Pinecone and turbopuffer often optimize performance for use cases with a few hot partitions while most other partitions remain cold. This is reflected in their pricing structure as well. If your data isn't multi-tenant, it's often better to use a dedicated database.

3. Secondary filtering needs

If your searches involve complex filtering beyond semantic search, it's crucial to consider how well the database supports these operations. Vector databases that rely solely on simple JSON-formatted metadata for filtering may struggle with more complex scenarios. In some cases, it might be more efficient to execute filters using a metadata index followed by a brute-force vector search. In other situations, a vector-based index could be more suitable. PostgreSQL stands out as one of the few vector databases with a sophisticated query planner, which is essential for efficiently making these trade-offs.

4. System of record considerations

Operational safety and reliability become paramount if your vector database is your primary data store. Look for features such as the following:

Backups and high availability: these ensure continuous system availability, even during failures, through automated backups and failover solutions.
Durability guarantees, transaction support, and data integrity: verify that data is correctly inserted and consistently stored, with built-in mechanisms for transaction management and constraint checking to maintain data accuracy.

If the vector database is not your system of record, consider how you’ll synchronize it with your main database. You may need to implement custom code to keep the embeddings updated as data changes, which is often complex and error-prone.

5. Data changes and synchronization

Your choice should also account for how frequently data changes and how those changes are synchronized. Some vector databases require custom code for updating embeddings when the underlying data is modified, while others may offer more seamless integration for such updates.

6. Handling structured data

If your application involves structured and unstructured data, choosing a system that can manage and connect all of this data at the database layer is advantageous. This enables more powerful data operations such as joins and analysis that blend structured and unstructured information.

Serverless vs. Dedicated Vector Databases

As mentioned, your query rate can influence your database choice. While a serverless database may be enough for a low query rate scenario, vector databases can handle the challenges of a consistently high query rate. However, to choose between the two, other factors are at play based on your application’s requirements, such as scalability and cost efficiency.

Serverless vector databases are designed for flexibility, scalability, and cost efficiency, especially in use cases with low or unpredictable query rates. They automatically scale resources based on demand, which can be highly convenient, but the cost per query is high.

Serverless databases are best suited for applications with sporadic or low query volumes, where maintaining provisioned infrastructure would be cost-prohibitive. Additionally, serverless databases are often well-suited for multi-tenant scenarios where most tenants have low or no usage.

Dedicated vector databases offer dedicated resources, which ensures consistent performance, particularly for higher-query rate scenarios. Dedicated databases are ideal for applications with steady or high demand, as they can handle large volumes of requests without the latency spikes sometimes associated with serverless architectures.

Even at a query rate of one (1) query per second (QPS), dedicated databases are often a better choice due to their consistent performance and usually lower cost than serverless options at similar usage levels. However, these databases can be more expensive if underutilized, as you pay for the allocated capacity regardless of the actual usage.

Choosing Between General-Purpose and Specialized Vector Databases

If, after careful evaluation, you realize your application requires a vector database, you now have yet another choice to make. Should you go for a general-purpose or specialized vector database?

Specialized vector databases: Examples include Pinecone, Qdrant, and Milvus, which are explicitly designed for vector search. They excel in vector search, but often present challenges such as requiring custom client libraries for data operations, lack of SQL support, and limited operational maturity features like observability, high availability, backups, and data integrity. Additionally, these databases only support vector data, meaning that storing other data types will require additional databases, adding complexity to the overall architecture.

General-purpose databases: Databases like PostgreSQL weren’t originally designed for vector search, but the pgvector, pgvectorscale, and pgai extensions have added such functionality. There is active development of new data types and index types, and the performance is quickly becoming on par with the best-of-breed dedicated databases. These offer greater versatility, allowing you to perform traditional analytics alongside vector operations, making them easier to use for developers who are familiar with SQL. For example, it is easy to query for documentation that has lots of likes on it:

WITH related_content (
   SELECT 
     doc_id,
     title,
     content 
   FROM 
    docs
  ORDER BY embedding <> {query_vector}
  LIMIT 100
)
SELECT
  related_content.*
  reactions.like_count
FROM
  related_content
  LEFT JOIN reactions on (related_content.doc_id = reactions.doc_id)
ORDER BY reactions.like_count DESC;

That query would be impossible in a specialized vector database, instead, you’d have to do this in application logic, which is more complex, error-prone, and less efficient.

Open-Source vs. Closed-Source Vector Databases

Choosing between an open-source or closed-sourced database is not just a philosophical debate—it has real-world technical implications. Open-source software allows for more effective debugging, as you can trace the exact code path taken by an action. It also allows fixing issues directly without relying on a third-party vendor. Moreover, having access to the code helps in understanding performance and the broader implications of a feature or approach, leading to more informed decision-making and optimization.

Additional Considerations for Vector Database Selection

Performance

Vector databases prioritize approximate searches, meaning 100 percent accuracy may not be guaranteed, but performance metrics such as latency, queries per second (QPS), and recall rate are critical. Ensure that your chosen vector database handles efficient index building, as this can significantly impact retrieval times and the overall performance of AI-driven applications.

Security and reliability

Make sure the database meets your requirements for security and reliability. Managed services need to encrypt data both in transit and at rest and ideally hold certifications like SOC Type II, GDPR, and HIPAA compliance if necessary. Dedicated vector databases often lack the enterprise-grade security of more mature, general-purpose databases like PostgreSQL.

Developer experience

General-purpose databases typically offer a more straightforward experience for developers, with rich ecosystems, existing integrations, and support for SQL. Specialized vector databases, while powerful, may come with a steep learning curve and limited support for complex queries involving metadata.

Observability

Effective observability is crucial for maintaining the performance and reliability of vector databases. It involves monitoring key metrics such as query latency, resource utilization, and the use of EXPLAIN plans to analyze query execution paths.

Good observability tools help detect issues early, optimize performance, and ensure the system meets the required service-level agreements (SLAs). Features like detailed logging, tracing, and real-time dashboards provide the visibility needed to understand database behavior and troubleshoot problems efficiently.

Specialized vector databases may lack advanced observability features, making it important to assess whether the chosen solution can support the level of monitoring your application requires.

Conclusion

Choosing the right vector database is all about understanding your application's needs, query patterns, and system's requirements. While specialized vector databases provide excellent performance for some use cases, general-purpose databases like PostgreSQL offer versatility and ease of use, especially when integrating structured and unstructured data. Carefully evaluate cost, reliability, scalability, and developer experience to choose the best vector database for your specific needs.

If you’re looking for a general-purpose vector database that can handle your application’s metadata, vector embeddings, and time-series data, try Timescale Cloud. Its simple stack for AI applications—with pgvector, pgai, and pgvectorscale—will help eliminate the operational complexity of data duplication, synchronization, and tracking updates across multiple systems. Start building your AI application with a free PostgreSQL database on Timescale Cloud.

If you want to build locally, both pgai and pgvectorscale are open source under the PostgreSQL License and are available for you to use in your AI projects today. Installation instructions are on the pgai and pgvectorscale GitHub repositories (⭐s welcome!).