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Vector Databases vs. Object-Relational Databases

Vector Databases vs. Object-Relational Databases

Mar 15, 202518 min read

Introduction

Vector databases excel at storing and querying high-dimensional vector embeddings, enabling AI applications to find semantic and perceptual similarities through specialized index structures optimized for nearest-neighbor search. Object-relational databases bridge the gap between relational and object-oriented worlds, extending traditional relational systems with object-oriented features like custom data types, inheritance, and methods while maintaining ACID guarantees and SQL compatibility.

But here's where things get interesting: as enterprise applications increasingly need both AI-powered features and complex data modeling capabilities, the boundaries between these specialized database types are beginning to blur. Some object-relational databases are adding vector extensions, while vector databases are enhancing their ability to represent and query complex relationships alongside embeddings.

For architects and developers designing systems in 2025, understanding when to leverage each technology—and when they might complement each other—has become essential for building applications that effectively balance sophisticated AI capabilities with enterprise-grade data modeling and consistency requirements. The decision often isn't about which approach is universally better, but rather which one aligns most closely with your specific application's core requirements and technical priorities.

Today's Database Landscape: Specialization Reigns

Remember when relational databases were the default choice for virtually every application? Those days are firmly behind us. The modern data landscape has evolved into a rich ecosystem of purpose-built solutions, each optimized for specific data types, access patterns, and query requirements.

In this increasingly specialized landscape:

Pure relational databases continue to excel at structured data with well-defined schemas and relationships
Document databases handle flexible JSON-like data with nested structures and schema flexibility
Key-value stores provide blazing-fast simple data access with minimal overhead
Graph databases make relationship-heavy data efficiently queryable and traversable
Time series databases efficiently manage chronological data points with time-optimized storage and queries
Wide-column stores distribute massive structured datasets across clusters with column-oriented optimizations

Vector databases and object-relational databases represent two distinct specializations in this ecosystem, addressing fundamentally different challenges:

Vector databases have emerged as essential infrastructure for AI applications, effectively bridging the gap between models that generate embeddings and applications that need to efficiently query them. The explosive growth in generative AI, semantic search, and recommendation systems has made them increasingly central to modern applications.
Object-relational databases evolved from traditional RDBMSs to address the "impedance mismatch" between relational models and object-oriented programming. By adding support for complex data types, inheritance, and methods, they provide a more natural mapping between application code and database structures while maintaining the ACID properties and SQL compatibility that enterprises depend on.

What makes this comparison particularly relevant is the growing number of applications that need both the AI-powered capabilities of vector databases and the complex data modeling and transactional integrity of object-relational systems—from intelligent enterprise applications to content platforms with sophisticated data models.

Why You Might Be Deciding Between These Database Types

If you're reading this, you're likely facing one of these scenarios:

You're adding AI features to an enterprise application: Perhaps you have an existing application using an object-relational database and now need to incorporate semantic search or recommendations.
You're building a complex application with AI requirements: You're developing a system that needs both sophisticated data modeling and vector similarity capabilities.
You're evaluating PostgreSQL extensions vs. dedicated solutions: You're wondering whether PostgreSQL with vector extensions can meet your needs or if a specialized vector database would be better.
You're concerned about transactional integrity with AI features: You need to ensure that AI-powered components maintain consistency with your core business data.
You're future-proofing your architecture: You want to understand how these technologies might complement each other as your application evolves.

As someone who's implemented both types of systems across diverse industries, I can tell you that making the right choice requires understanding not just what each database type excels at, but how their architectural differences impact your specific application requirements and development practices.

Vector Databases: The Backbone of Modern AI Search

Architectural Foundations

At their core, vector databases like Milvus and Zilliz Cloud revolve around a powerful concept: representing data items as points in high-dimensional space where proximity equals similarity. Their architecture typically includes:

Vector storage engines optimized for dense numerical arrays that can range from dozens to thousands of dimensions
ANN (Approximate Nearest Neighbor) indexes like HNSW, IVF, or PQ that make billion-scale vector search practical
Distance computation optimizations for calculating similarity using metrics like cosine, Euclidean, or dot product
Filtering subsystems that combine vector search with metadata constraints
Sharding mechanisms designed specifically for distributing vector workloads

The key insight: vector databases sacrifice the perfect accuracy of exact nearest neighbor search for the dramatic performance gains of approximate methods, making previously infeasible similarity search applications practical at scale.

What Sets Vector DBs Apart

In my experience implementing these systems, these capabilities really make vector databases shine:

Tunable accuracy-performance tradeoffs: The ability to adjust index parameters to balance search speed against result precision
Multi-vector record support: Storing multiple embedding vectors per item to represent different aspects or modalities
Hybrid search capabilities: Combining vector similarity with traditional filtering for precise results
Distance metric flexibility: Supporting different similarity measures for different embedding types
Metadata filtering: Narrowing results based on traditional attributes alongside vector similarity

Recent innovations have further expanded their capabilities:

Sparse-dense hybrid search: Combining traditional keyword matching strengths with semantic understanding
Cross-encoder reranking: Refining initial vector search results with more computationally intensive models
Serverless scaling: Automatically adjusting resources based on query and indexing loads
Multi-stage retrieval pipelines: Orchestrating complex retrieval flows with filtering and reranking stages

Zilliz Cloud and Milvus: Leading the Vector Database Ecosystem

Among the growing ecosystem of vector database solutions, Zilliz Cloud and the open-source Milvus project have emerged as significant players:

Milvus is a widely-adopted open-source vector database that has gained popularity among developers building AI applications. Created to handle vector similarity search at scale, it provides the foundation for many production systems in areas ranging from recommendation engines to image search. The project has a strong community behind it and is designed with performance and scalability in mind.

Zilliz Cloud is the managed service version of Milvus, offering the same core functionality without the operational complexity. For development teams looking to implement vector search capabilities without dedicating resources to database management, Zilliz Cloud provides a streamlined path to production. This cloud-native approach aligns with modern development practices where teams increasingly prefer to consume databases as services rather than managing the underlying infrastructure themselves.

Popular Use Cases: Vector Databases

Vector databases are transforming various industries with their ability to power similarity-based applications:

Retrieval-Augmented Generation (RAG): Vector databases connect language models with relevant information sources. Users can ask complex questions like "What were our Q2 sales results in Europe?" and receive accurate answers drawn directly from internal documents—ensuring responses are factual and up-to-date.
Semantic Search: Vector databases enable natural language search that understands user intent rather than just matching keywords. Users can search with conversational queries like "affordable vacation spots for families" and receive semantically relevant results, even when these exact words don't appear in the content.
Recommendation Systems: E-commerce platforms, streaming services, and content platforms use vector databases to deliver personalized recommendations based on semantic similarity rather than just collaborative filtering. This approach reduces the "cold start" problem for new items and can better explain why recommendations are being made.
Image and Visual Search: Retailers and visual platforms use vector databases to enable search-by-image functionality. Users can upload a photo to find visually similar products, artwork, or designs—particularly valuable in fashion, interior design, and creative fields.
Anomaly Detection: Security and monitoring systems leverage vector databases to identify unusual patterns that don't match expected behaviors. This is particularly valuable for fraud detection, network security, and manufacturing quality control.

Object-Relational Databases: Bridging the Object-Relational Divide

Architectural Foundations

Object-relational databases like PostgreSQL, Oracle Database, and SQL Server with object extensions evolved to address the fundamental mismatch between relational data models and object-oriented programming. Their architecture typically includes:

Extended type systems supporting user-defined complex data types, arrays, and nested structures
Inheritance mechanisms allowing for type hierarchies and polymorphic queries
Method support enabling encapsulation of business logic within the database
Rules and triggers providing powerful mechanisms for maintaining data integrity
Extensions frameworks enabling domain-specific functionality without compromising core RDBMS capabilities

The core insight: by extending the relational model with object-oriented concepts while maintaining the strong consistency guarantees and declarative query language of relational systems, object-relational databases provide a more natural mapping between application code and database structures for complex domains.

What Sets Object-Relational DBs Apart

Having worked with object-relational databases across enterprise applications, I've found these capabilities particularly valuable:

Rich type system: Support for custom composite types, arrays, JSON, XML, and other complex data structures
Inheritance and polymorphism: The ability to model type hierarchies and query across subtypes
Procedural extensions: Embedding business logic through stored procedures, functions, and methods
Strong consistency: Maintaining ACID properties for complex transaction processing
Extensibility: Adding domain-specific functionality through extension frameworks

Recent innovations have further enhanced object-relational capabilities:

Advanced JSON/XML support: Better integration between structured and semi-structured data
Column-store capabilities: Adding analytical performance while maintaining transactional integrity
Machine learning extensions: Bringing predictive models directly into the database
Vector support: Adding specialized types and indexes for embedding vectors
Cloud-native architectures: Evolving deployment models for cloud scalability

Popular Use Cases: Object-Relational Databases

Object-relational databases excel in scenarios where complex domain models meet enterprise requirements for consistency and integrity:

Enterprise Resource Planning (ERP): Modern ERP systems leverage object-relational databases to model complex business entities with inheritance hierarchies and rich relationships. The combination of advanced data modeling capabilities and strong transactional guarantees ensures that critical business processes maintain consistency across complex operations like order-to-cash and procure-to-pay.
Healthcare Information Systems: Medical applications rely on object-relational databases to handle the extraordinary complexity of healthcare data models, from patient records with inheritance hierarchies to treatment protocols with nested structures. The ability to enforce complex integrity constraints while supporting specialized medical data types makes object-relational databases ideal for systems that must maintain strict compliance with healthcare regulations.
Financial Services Platforms: Banking and investment systems use object-relational databases to model sophisticated financial products, account hierarchies, and transaction rules. The combination of ACID transactions for regulatory compliance with rich domain modeling capabilities enables these platforms to handle complex financial operations while maintaining audit trails and data integrity.
Geographic Information Systems (GIS): Spatial applications leverage object-relational databases with geographic extensions to store and analyze location data alongside traditional attributes. The extensibility of the object-relational model made it possible to add specialized spatial types, operators, and indexes without sacrificing the relational foundation, creating a unified platform for location-aware applications.
Content Management Systems: Enterprise CMS platforms use object-relational databases to manage complex content types with inheritance relationships, versioning, and workflow states. The ability to model content hierarchies naturally while maintaining referential integrity across related assets makes object-relational databases well-suited for organizations with sophisticated content structures and approval processes.
Telecommunications Management: Telecom operators implement object-relational databases to model network infrastructure, service offerings, and customer relationships. The combination of complex data modeling for network elements with high-performance transaction processing for provisioning and billing operations provides a unified platform for telecom operations support systems.

Head-to-Head Comparison: Vector DB vs Object-Relational DB


Feature	Vector Databases (Milvus, Zilliz Cloud)	Object-Relational Databases (PostgreSQL, Oracle)	Why It Matters
Primary Optimization	Similarity search in high-dimensional space	Complex data modeling with relational integrity	Determines the core strengths and limitations for your primary use case
Data Model	Vector embeddings with simple metadata	Rich types with inheritance, methods, and relationships	Influences how naturally you can represent your domain concepts
Query Paradigm	Vector similarity with filtering	SQL with object-oriented extensions	Affects how you express questions and the complexity of operations
Type System	Limited to vectors and basic types	Extensible with custom complex types and hierarchies	Determines how well you can model complex domain entities
Transactional Model	Limited or eventual consistency	ACID with strong consistency guarantees	Impacts data reliability for critical business operations
Performance Focus	Optimized for ANN search operations	Balanced for both transactions and queries	Aligns with your application's primary workload type
Scaling Approach	Horizontal scaling for vector operations	Vertical scaling with some horizontal capabilities	Determines how your database grows with increasing data and users
Development Paradigm	Specialized for vector operations	SQL with object-oriented principles	Affects the learning curve and productivity for your team
AI Integration	Native support for embeddings and similarity	Extensions or procedural code for AI capabilities	Determines ease of implementing AI-powered features
Ecosystem Maturity	Newer, rapidly evolving technology	Established enterprise technology with proven reliability	Influences operational confidence and available support resources

Vector Databases In Action: Real-World Success Stories

Vector databases shine in these use cases:

Retrieval-Augmented Generation (RAG) for Enterprise Knowledge

A global consulting firm implemented a RAG system using Zilliz Cloud to power their internal knowledge platform. They converted millions of documents, presentations, and project reports into embeddings stored in a vector database. When consultants ask questions, the system retrieves the most relevant context from their knowledge base and passes it to a large language model to generate accurate, contextually relevant answers.

This approach dramatically improved knowledge discovery, reduced research time by 65%, and ensured responses were grounded in the firm's actual experience and methodologies rather than generic LLM outputs. The vector database was critical in enabling real-time retrieval across massive document collections while maintaining sub-second query response times.

See more RAG case studies:

Agentic RAG for Complex Workflows

Agentic RAG is an advanced RAG framework that enhances the traditional RAG framework by incorporating intelligent agent capabilities. A healthcare technology provider built an agentic RAG system that uses vector search to power a clinical decision support tool. The system stores medical knowledge, treatment guidelines, and patient case histories as embeddings in a vector database. When physicians input complex patient scenarios, the agentic system:

Decomposes the complex query into sub-questions
Performs targeted vector searches for each sub-question
Evaluates and synthesizes the retrieved information
Determines if additional searches are needed
Delivers a comprehensive, evidence-based response

This advanced implementation reduced clinical decision time by 43% and improved treatment recommendation accuracy by 28% in validation studies. The vector database's ability to perform multiple rapid similarity searches with different contexts was essential for the agent's multi-step reasoning process.

The DeepSearcher, built by Zilliz Engineers, is a prime example of agentic RAG and is also a local, open-source alternative to OpenAI’s Deep Research. What sets DeepSearcher apart is its unique combination of advanced reasoning models, sophisticated search features, and an integrated research assistant. By leveraging Milvus (a high-performance vector database built by Zilliz) for local data integration, it delivers faster and more relevant search results while allowing easy model swapping for customized experiences.

Semantic Search Beyond Keywords

A legal research platform replaced their traditional Boolean search with a vector database-powered approach, allowing attorneys to search using natural language queries that capture the intended meaning rather than specific legal terminology. Their vector database indexed embeddings of millions of case documents, statutes, and legal commentaries.

This implementation improved search relevance scores by 47%, reduced search abandonment by 38%, and significantly decreased the time attorneys spent finding relevant precedents. Particularly notable was the improvement for new associates, who previously struggled with crafting effective Boolean queries but could now find relevant cases using natural language descriptions of legal scenarios.

See more semantic search case studies:

AI-Powered Image Search

A digital asset management platform implemented visual search using a vector database to store embeddings of millions of images across their enterprise customers' media libraries. Content creators could now upload reference images to find visually similar assets—a capability impossible with their previous metadata-based search.

This feature transformed how creative teams discovered assets, increasing asset reuse by 62% and reducing time spent searching for suitable imagery by 47%. The vector database efficiently handled libraries with millions of images while maintaining search latency under 200ms, even for the largest enterprise collections.

See more image search case studies:

Object-Relational Databases in Action: Real-World Success Stories

Object-relational databases excel in these scenarios:

Healthcare Platform Modernization

A major healthcare software provider rebuilt their clinical information system on PostgreSQL's object-relational foundation to handle the complexity of modern healthcare data. Their previous relational solution struggled with representing complex medical concepts, inheritance relationships among clinical entities, and integrating diverse data types.

The object-relational implementation leveraged inheritance hierarchies for clinical observations, composite types for complex measurements, and specialized extensions for medical terminology. This approach reduced schema complexity by 62%, improved query performance by 45% for complex clinical queries, and dramatically accelerated development of new clinical modules by providing a more natural mapping between domain models and database structures.

Telecommunications Inventory Management

A telecom operator implemented an object-relational database to manage their complex network inventory across physical and virtual network elements. Their previous system couldn't effectively model the intricate relationships between equipment types, inheritance hierarchies of network elements, and the polymorphic nature of connectivity.

The object-relational solution used type hierarchies to model diverse network elements, composite types for complex configurations, and constraint triggers to maintain network topology integrity. This implementation reduced provisioning errors by 78%, accelerated new service deployment by 53%, and provided a single consistent view of the network with real-time integrity guarantees—critical capabilities during network transformation initiatives.

Financial Services Product Platform

An investment firm built their product management platform on an object-relational database to handle the extraordinary complexity of modern financial instruments. Their previous system struggled with representing the diverse attributes of different product classes while maintaining consistency across related entities.

The object-relational implementation used inheritance to model the product hierarchy, complex types for structured attributes, and procedural functions for validation and pricing logic. This approach enabled them to reduce time-to-market for new products by 67%, ensure regulatory compliance through enforced validation rules, and maintain a unified view of client positions across diverse product types with strict transactional guarantees.

Benchmarking Your Vector Search Solutions on Your Own

VectorDBBench is an open-source benchmarking tool designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems using their own datasets and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.

VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.

Download VectorDBBench.

Check out the VectorDBBench Leaderboard for a quick look at the performance of mainstream vector databases.

Decision Framework: Choosing the Right Database Architecture

After helping numerous organizations make this decision, I've developed this practical framework:

Choose a Vector Database When:

AI-powered similarity search is your core value proposition - Your application primarily revolves around finding related items based on semantic or perceptual similarity
Performance for vector operations is critical - You need the most efficient implementation of ANN algorithms and vector-specific optimizations
You're working with high-dimensional embeddings - Your vectors typically have hundreds or thousands of dimensions from modern AI models
You need specialized vector operations and distance metrics - Your application requires efficient cosine similarity, Euclidean distance, or other vector-specific calculations
Your focus is on finding similar items rather than modeling complex relationships - The concept of "nearness" in your application is about similarity, not relational structure

Choose an Object-Relational Database When:

Complex domain modeling is your primary requirement - Your application needs to represent sophisticated real-world entities with inheritance and relationships
Transactional integrity is non-negotiable - You're handling financial, healthcare, or other data requiring strict ACID guarantees
You need a unified approach to structured and semi-structured data - Your domain includes both rigidly structured and more flexible data elements
Business logic benefits from database procedural capabilities - Complex validation rules, derivations, or workflows can be cleanly implemented in the database
Your team and ecosystem are SQL-oriented - Your developers, tools, and processes are built around SQL and relational concepts

Consider a Hybrid Approach When:

You have distinct workloads with clear boundaries - Some features need similarity search while others need complex transactional processing
Data naturally flows between AI and transactional components - Your workflow involves processing application data for ML and bringing insights back
Different teams maintain different application components - You have separate teams for AI features and core business functionality
Performance requirements differ between components - Some operations need vector similarity while others need relational joins

Consider Object-Relational DB with Vector Extensions When:

Your primary need is complex data modeling with occasional vector search - You need rich domain modeling but want to add some AI capabilities
Data consistency between transactions and vectors is critical - You need vector operations to see immediately consistent data after transactions
PostgreSQL with pgvector meets your performance needs - Your vector workload is modest enough that a specialized vector database might be overkill
Operational simplicity trumps specialized performance - Managing a single database system is a higher priority than maximizing vector search performance

Implementation Realities: What I Wish I Knew Earlier

After implementing both database types across multiple organizations, here are practical considerations that often get overlooked:

Resource Planning

Vector databases typically require significant memory for indexes, often 2-3x what you might initially estimate based on raw vector dimensions
Object-relational databases can have higher CPU requirements than simpler databases due to type checking, inheritance resolution, and procedural execution
Scaling patterns differ fundamentally: vector databases scale primarily with vector dimensions and collection size, while object-relational databases scale with schema complexity and transaction volume

Development Experience

Query paradigms are completely different between these database types, requiring distinct mental models from your development team
Object-relational features often have varying support across different database vendors, creating potential vendor lock-in
Vector search requires understanding of embedding models, distance metrics, and approximate indexing concepts that traditional database developers may not possess

Operational Realities

Backup and recovery strategies differ substantially, with vector databases often requiring special handling for large indexes
Monitoring needs vary dramatically, with vector databases requiring attention to ANN performance and object-relational databases focusing on transaction metrics and procedural execution
Schema evolution impacts each system differently, with object-relational inheritance creating more complex migration scenarios

Conclusion: Choose the Right Tool, But Stay Flexible

The choice between vector databases and object-relational databases isn't about picking a winner—it's about matching your database architecture to your specific requirements for AI capabilities, data modeling, and transactional integrity.

If your core use case involves finding similar items based on semantic or perceptual similarity, a vector database likely makes sense as your foundation. If your fundamental need is modeling complex domain entities with inheritance, relationships, and transactional integrity, an object-relational database is probably your starting point.

The most sophisticated data architectures I've helped build don't shy away from specialized databases—they embrace them while creating clean interfaces that hide complexity from application developers. This approach gives you the performance benefits of specialized systems while maintaining development velocity.

Whatever path you choose, the key is building with enough flexibility to evolve as both your requirements and the database landscape continue to change. The convergence between vector capabilities and object-relational features is just beginning, and the most successful architectures will be those that can adapt to incorporate the best of both worlds.

Updated on Apr 07, 2025

Chloe Williams
Chloe Williams is a technical writer at Zilliz.

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