Blog
Vector Databases vs. Graph Databases

Vector Databases vs. Graph Databases

Feb 27, 202518 min read

Introduction

Vector databases excel at storing and querying high-dimensional vector embeddings, powering AI applications with the ability to find semantic and perceptual similarities that traditional query methods cannot detect. Graph databases, on the other hand, specialize in modeling, storing, and querying highly interconnected data, making relationship patterns first-class citizens in both data structure and query language.

But here's where things get interesting: as applications increasingly need both semantic understanding and relationship intelligence, the boundaries between these specialized database types are beginning to blur. Graph databases are starting to incorporate vector capabilities for semantic similarity, while vector databases are enhancing their ability to represent connections between entities.

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 can effectively handle both AI-powered similarity search and complex relationship analysis.

Today's Database Landscape: Specialization Reigns

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

In this increasingly specialized landscape:

Relational databases continue to excel at transactional workloads with structured relationships
Document databases handle flexible JSON-like data with nested structures
Key-value stores provide blazing-fast simple data access
Time series databases efficiently manage chronological data points
Wide-column stores distribute massive structured datasets across clusters

Vector databases and graph databases represent two of the most specialized and fastest-growing categories, each addressing fundamental challenges in modern applications:

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, similarity search, and recommendation systems has made them increasingly central to modern applications.
Graph databases have revolutionized how we work with highly connected data, enabling applications to efficiently traverse complex relationship networks in ways that would be prohibitively expensive with traditional databases. They've become indispensable for social networks, fraud detection, recommendation systems, and knowledge graphs.

What makes this comparison particularly relevant is the growing number of applications that span both domains—from knowledge graphs with semantic search to recommendation systems that combine relationship analysis with content similarity.

Why You Might Be Deciding Between These Database Types

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

You're building a recommendation system: Perhaps you're developing a platform that needs both relationship-based recommendations ("users who bought this also bought") and similarity-based suggestions ("visually similar products").
You're creating an advanced knowledge graph: Maybe you need to represent complex domain knowledge while enabling semantic search across the content.
You're optimizing infrastructure costs: With limited resources, you're trying to determine which specialized database will deliver the most value for your specific use cases.
You're evaluating hybrid approaches: You're considering whether a graph database with vector capabilities or a vector database with relationship features could meet your needs.
You're future-proofing your architecture: You want to understand how these technologies might converge or complement each other as your applications evolve.

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 does well, but how their architectural differences impact real-world applications.

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.

Graph Databases: Making Relationships First-Class Citizens

Architectural Foundations

Graph databases like Neo4j, TigerGraph, and Amazon Neptune are built around a fundamentally different paradigm: explicitly modeling and storing relationships between entities as first-class citizens. Their architecture typically includes:

Node and edge data structures that directly represent entities and their relationships
Index-free adjacency where connected entities directly reference each other, eliminating the need for expensive join operations
Graph traversal engines optimized for relationship-based queries and pattern matching
Path-finding algorithms built into the query engine for efficient network analysis
Graph partitioning strategies for distributed storage and processing

The core insight: by physically structuring data around relationships rather than tables or documents, graph databases achieve orders-of-magnitude better performance for traversal-heavy workloads that would require expensive join operations in traditional databases.

What Sets Graph DBs Apart

Having deployed graph databases across multiple domains, I've found these capabilities particularly valuable:

Relationship-first modeling: The ability to represent complex, variable relationship patterns without schema limitations
Path-finding and traversal: Efficiently answering questions about connectivity and network structure
Pattern matching: Identifying complex relationship patterns that would require multiple joins in relational databases
Graph algorithms: Built-in support for centrality, community detection, and other network analysis tools
Recursive query support: Handling queries of arbitrary depth like "find all friends of friends" without performance cliffs

Recent innovations have further enhanced graph databases:

Distributed graph processing: Scaling graph operations across clusters while maintaining ACID properties
Graph machine learning integration: Supporting node embedding and graph neural networks
Temporal graph support: Tracking how relationships evolve over time
Multi-modal graphs: Representing different types of entities and relationships in a unified model
Graph visualization tools: Helping users understand complex relationship structures

Popular Use Cases: Graph Databases

Graph databases excel in domains where relationship patterns are the primary source of value:

Social Network Analysis: Platforms use graph databases to store user connections and enable complex queries like "friends of friends who live nearby and share similar interests." The graph model naturally represents the social network structure, making relationship-based recommendations and connection discovery highly efficient.
Fraud Detection: Financial institutions leverage graph databases to identify suspicious patterns of transactions and relationships. By modeling accounts, transactions, and entities as a connected network, analysts can detect complex fraud rings and money laundering schemes that would be nearly impossible to find with traditional query methods.
Knowledge Graphs: Organizations use graph databases to build comprehensive knowledge representations of their domains. These knowledge graphs connect entities, concepts, and information in ways that enable complex reasoning, inferencing, and discovery. They power everything from enterprise search to AI assistants that need to understand how different pieces of information relate.
Supply Chain Management: Companies deploy graph databases to model their complex supply networks, from raw materials to finished products. This approach enables them to analyze dependencies, identify vulnerabilities, and optimize logistics in ways that traditional tabular data models simply cannot support.
Life Sciences Research: Pharmaceutical companies and research institutions use graph databases to model biological networks, chemical interactions, and research literature connections. The graph structure is ideal for representing protein interactions, disease pathways, and the complex relationships between genes, diseases, and potential treatments.
Recommendation Engines: Media and e-commerce platforms use graph databases to build context-aware recommendations that consider not just item similarity but also complex user-item interaction patterns. This approach produces more diverse and contextually relevant recommendations than traditional collaborative filtering alone.

Head-to-Head Comparison: Vector DB vs Graph DB


Feature	Vector Databases (Milvus, Zilliz Cloud)	Graph Databases (Neo4j, TigerGraph)	Why It Matters
Data Model	High-dimensional vectors with metadata	Nodes, edges, and properties representing entities and relationships	Determines how you model your domain concepts and what operations are efficient
Query Patterns	Similarity search, k-NN, range queries	Traversal, pattern matching, path finding	Defines the types of questions you can efficiently ask of your data
Primary Strength	Finding similar items based on semantic or perceptual similarity	Analyzing connected data and complex relationship patterns	Aligns database capabilities with your core application needs
Scalability	Horizontal scaling optimized for search workloads	Graph partitioning with relationship awareness	Impacts how your database grows with increasing data and users
Performance Focus	Fast approximate nearest neighbor search	Efficient relationship traversal without joins	Affects query response times for key application patterns
Query Complexity	Relatively simple similarity functions with filters	Complex pattern matching with variable-length paths	Influences what types of insights can be easily extracted
Use Case Alignment	AI-powered applications needing semantic understanding	Applications centered on relationship analysis	Determines fit with your application's core value proposition
Query Language	Vector-specific APIs, similarity functions	Graph query languages (Cypher, GSQL, Gremlin)	Affects developer learning curve and query expressiveness
Typical Data Size	Can efficiently handle billions of vectors	Scales to billions of nodes and relationships	Determines fit with your data volume requirements
Ecosystem Integration	Strong integration with ML/AI frameworks	Rich ecosystem of graph algorithms and analysis tools	Impacts how easily the database fits into your tech stack

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 company replaced their traditional search with a vector database-powered approach, allowing legal professionals to search case law with natural language queries like "workplace discrimination cases involving remote employees" instead of precise keyword combinations. Their vector database indexed embeddings of millions of legal documents, capturing the semantic meaning beyond specific terminology.

The results transformed their product: search relevance improved by 52%, user satisfaction scores increased by 38%, and subscribers reported saving an average of 5-7 hours per week on research tasks. The vector database enabled them to deliver these improvements while handling over 10 million documents with sub-second query response times.

See more semantic search case studies:

AI-Powered Image Search

A stock photography platform implemented visual search using a vector database to store embeddings of their image catalog. Users could now upload reference images or sketches to find visually similar photos—a capability impossible with their previous metadata-based search.

This feature increased user engagement by 43%, with paid downloads rising 26% as users discovered relevant content they couldn't find before. The vector database handled over 50 million images while maintaining search latency under 200ms, even as they continuously added new content to the platform.

See more image search case studies:

Graph Databases in Action: Real-World Success Stories

Graph databases excel in these scenarios:

Financial Fraud Detection Network

A major payment processor implemented a graph database to detect sophisticated fraud patterns. They modeled their entire transaction network as a graph, with accounts as nodes and transfers as relationships. This approach allowed them to identify complex fraud patterns like money mule networks and sleeper fraud rings that remained dormant for months before activation.

The graph database enabled them to run complex pattern-matching queries that would have required dozens of expensive joins in their previous relational database. This implementation reduced false positives by 37% while increasing fraud detection rates by 42%, resulting in an estimated $18M annual savings from prevented fraud. Most importantly, fraud investigators could now visualize suspicious networks directly, making their investigations significantly more efficient.

Pharmaceutical Research Knowledge Graph

A pharmaceutical company built a comprehensive biomedical knowledge graph to accelerate drug discovery. They integrated data from scientific literature, clinical trials, genetic databases, and their proprietary research into a unified graph database with over 100 million nodes and 2 billion relationships.

The graph database allowed researchers to identify non-obvious connections between diseases, genes, proteins, and potential treatment compounds. One notable success involved discovering a potential repurposing opportunity for an existing drug, identified through complex path analysis that revealed unexpected biochemical pathway connections. The knowledge graph reduced candidate identification time for new drug targets by 65% and enabled cross-disciplinary insights that weren't possible with their previous siloed data approach.

Supply Chain Resilience Transformation

A global manufacturing company deployed a graph database to model their entire supply chain network, including suppliers, manufacturing facilities, distribution centers, and transportation routes. This graph representation allowed them to identify hidden dependencies and single points of failure that weren't apparent in their previous supply chain management systems.

When semiconductor shortages hit in 2023, they leveraged the graph database to quickly identify all products affected by specific component shortages and simulate the impact of alternative sourcing strategies. The graph-based impact analysis enabled them to prioritize production effectively, securing alternative suppliers 58% faster than competitors and maintaining 92% fulfillment rates while industry averages dropped below 70%. The platform now forms the core of their supply chain resilience strategy.

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 needs to find items based on semantic or perceptual similarity
Content-based matching is more important than relationship analysis - You need to match items based on their inherent characteristics rather than their connections
You're working with embeddings from machine learning models - Your data consists of high-dimensional vector representations from language, image, or other AI models
Speed of similarity search at scale is critical - Performance of nearest-neighbor search directly impacts your user experience
Your queries are primarily about "what's similar to this item?" - The fundamental questions your application answers revolve around similarity

Choose a Graph Database When:

Relationship patterns are your primary data value - Your application's core purpose revolves around understanding connections and network structures
You need to answer questions about paths and connectivity - Questions like "how are these entities connected?" or "what's the shortest path between these nodes?" are common
Network analysis is central to your application - You need to identify influential nodes, communities, or patterns in a connected system
Your domain is naturally graph-structured - Areas like social networks, supply chains, or knowledge representations that are inherently about connections
Query flexibility for relationship patterns is essential - You need to run complex traversals with unpredictable patterns and depths

Consider a Hybrid Approach When:

You need both similarity matches and relationship analysis - Your application requires both finding similar items and understanding how they're connected
Your domain combines content and relationships - You work with rich content that has meaningful connections between items
Different parts of your application have different query patterns - Some features need similarity search while others need relationship traversal
Performance requirements differ across workloads - Vector operations and graph traversals have different scaling characteristics that might benefit from specialized databases

Consider Graph DB with Vector Capabilities When:

Your primary need is relationship analysis with occasional similarity search - Your core use case is graph-based but you sometimes need to find similar nodes
You need to combine relationship context with similarity in the same query - Questions like "find similar products purchased by people in this user's network"
Operational simplicity trumps specialized performance - Managing a single database system is a higher priority than maximizing query performance
Your vector search needs are modest - Both in terms of vector dimensions and collection size

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 can be surprisingly memory-intensive, often requiring 2-4x more RAM than you might initially estimate based on raw data size
Graph databases' performance is highly dependent on having sufficient memory to keep heavily-traversed portions of the graph accessible
Scaling considerations differ fundamentally: vector databases often scale with collection size and dimensions, while graph databases scale with both node count and relationship complexity

Development Experience

Query paradigms are completely different, requiring your team to learn new mental models regardless of which option you choose
Graph traversal complexity can be initially challenging for developers accustomed to SQL or document-based queries
Testing strategies vary significantly between these database types, with graph databases requiring special attention to relationship-based test cases

Operational Realities

Backup and recovery strategies differ substantially between these database types, with graph databases often requiring special consideration for consistency during restores
Monitoring needs vary significantly, with vector databases requiring attention to index performance and graph databases needing focus on traversal patterns
Maintenance operations impact availability differently, with index rebuilds in vector databases and graph repartitioning both requiring careful planning

Conclusion: Choose the Right Tool, But Stay Flexible

The choice between vector databases and graph databases isn't about picking a winner—it's about matching your database architecture to your specific data characteristics and query patterns.

If your core use case involves finding similar items or semantic relationships, a vector database likely makes sense as your foundation. If your fundamental need is understanding how entities are connected and analyzing network structures, a graph 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 and graph capabilities is just beginning, and the most successful architectures will be those that can adapt to incorporate the best of both worlds.

Updated on Mar 31, 2025

Chloe Williams
Chloe Williams is a technical writer at Zilliz.

Content

Start Free, Scale Easily

Try the fully-managed vector database built for your GenAI applications.

Try Zilliz Cloud for Free

Share this article

Keep Reading

How to Build RAG with Milvus, QwQ-32B and Ollama

Hands-on tutorial on how to create a streamlined, powerful RAG pipeline that balances efficiency, accuracy, and scalability using the QwQ-32B model and Milvus.

Stop Waiting, Start Building: Voice Assistant With Milvus and Llama 3.2

We'll learn to build a Voice Assistant, a specialized Agentic RAG system designed for voice interactions, with Milvus, Llama 3.2, and other GenAI tools.

How to Select the Most Appropriate CU Type and Size for Your Business?

Explore Zilliz Cloud’s three CU options and learn how to choose the most suitable one for your business