Defining vector databases and relational databases

Vector databases and relational databases differ fundamentally in their data models, storage mechanisms, and primary use cases.

Relational databases

A relational database is a widely used data storage system where data is organized into tables consisting of rows and columns. Each row represents a unique record, and columns define the attributes of that record.

Key aspects include:

  • Data model: Store structured data in tables with predefined schemas, using rows and columns to represent entities and their relationships.
  • Storage and retrieval: Data is retrieved using Structured Query Language (SQL) based on exact matches, range filters, and joins between tables.
  • Search mechanisms: Used for deterministic searches.
  • Performance and scalability: Suitable for transactional workloads and vertical scaling.
  • Security: Supports more mature, fine-grained security.
  • Ecosystem: More mature, with multiple tools available.
  • Use cases: Excel at managing transactional data, ensuring data integrity (ACID properties), and handling complex queries requiring joins, filters, or aggregations. Common applications include eCommerce platforms, financial systems, and content management.

Vector databases

A vector database is a specialized data system to store, index, and query vectors, which are sequences of numerical values representing high-dimensional data, commonly used in AI data processing.

Key aspects include:

  • Data model: Optimized for storing and querying high-dimensional vectors, which are numerical representations (embeddings) of unstructured data like text, images, or audio.
  • Storage and retrieval: Data is stored as vectors, and queries involve finding vectors that are “similar” to a given query vector, often using approximate nearest neighbor (ANN) search algorithms.
  • Search mechanisms: Used for similarity search.
  • Performance and scalability: Useful for large-scale vector search, supporting billions of vectors with low latency.
  • Security: Supports encryption and authentication.
  • Ecosystem: Less mature, mostly open source or managed services.
  • Use cases: Primarily used in AI and machine learning applications where semantic search and similarity matching are crucial. Examples include recommendation systems, natural language processing (NLP), image recognition, and anomaly detection.

Vector database vs. relational database: The key differences

1. Data model and structure

In a vector database, the core data unit is a high-dimensional vector, often between 128 and 4,096 dimensions, produced by an embedding model. These vectors represent semantic or perceptual meaning, allowing “similar” items to be placed near each other in vector space. Metadata such as IDs, timestamps, and tags can be stored alongside vectors to support filtering and hybrid search. The database schema is usually flexible, with few constraints, since the focus is on similarity metrics rather than relational integrity.

In a relational database, data is stored in structured tables, with each column having a defined data type and constraints. Relationships between tables (one-to-one, one-to-many, many-to-many) are explicitly modeled using primary keys and foreign keys. Data normalization reduces redundancy, and schemas are strictly enforced, meaning any data must match the table definition. This model ensures consistency and enables complex joins.

2. Storage and retrieval

Vector databases store vectors in specialized indexes optimized for fast high-dimensional search. Common index types include:

  • HNSW (hierarchical navigable small world graphs) for fast recall with good accuracy.
  • IVF (inverted file index) for clustering vectors into partitions to limit search space.
  • PQ (product quantization) for compressing vectors and reducing memory usage.

Data can be stored in memory for low-latency retrieval or persisted to disk for durability. Retrieval often combines similarity scoring with metadata-based filtering to narrow down results.

Relational databases store data in row-oriented or column-oriented formats, depending on the use case. Indexing strategies like B-trees, hash indexes, and composite indexes optimize lookups and range queries. Retrieval uses SQL queries, which can combine multiple tables, filter results, and compute aggregates in a single execution plan.

3. Search mechanisms

Vector databases natively support similarity search, where the goal is to find items “closest” to a query vector. This is done using distance functions like cosine similarity, Euclidean distance, or dot product. Searches can be:

  • Exact: All vectors are compared directly, ensuring perfect accuracy but with higher latency.
  • Approximate (ANN): Uses prebuilt indexes to return near-identical results with much lower latency, making it feasible for real-time applications.

Relational databases support deterministic searches (exact match, range search, pattern matching) using SQL operators (=, <, >, LIKE, IN). They are not optimized for vector similarity, and any such search requires either an extension (e.g., PostgreSQL + pgvector) or preprocessing in the application layer.

4. Performance and scalability

Vector databases handle massive-scale similarity search, often managing hundreds of millions or billions of vectors while keeping query latency under 100 ms. They scale horizontally through sharding, distributing vectors across nodes and searching in parallel. Indexes are optimized for read-heavy workloads, and some support incremental updates without full reindexing.

Relational databases handle transactional workloads well, ensuring ACID guarantees even under high concurrency. Vertical scaling (adding more CPU/RAM) is common, but horizontal scaling often requires complex strategies like partitioning, replication, or distributed SQL engines. While they can handle large datasets, their performance degrades for high-dimensional similarity queries.

5. Security

Vector databases typically support TLS encryption for data in transit and AES-based encryption at rest. Authentication may use API keys, tokens, or integration with identity providers. Role-based access control exists but is often coarse-grained, and audit logging may be minimal unless integrated with external tools. Compliance features for regulated industries are still emerging.

Relational databases have mature, fine-grained security models. Permissions can be granted at the table, column, or even row level. They provide robust auditing, query logging, and compliance certifications for finance, healthcare, and government use cases. Advanced features include transparent data encryption, data masking, and integration with enterprise IAM systems.

6. Ecosystem

Vector databases are a relatively young category. Many are open source (e.g., NetApp Instaclustr, Milvus, Weaviate, Qdrant) or managed services (e.g., NetApp Instaclustr, Pinecone, Vespa, Azure Cognitive Search). They integrate tightly with AI/ML pipelines, embedding generation services, and vector-capable search frameworks. Tooling is still evolving, with fewer standardized administration and monitoring tools compared to mature databases.

Relational databases benefit from decades of development. Tools exist for backup, replication, monitoring, schema migration, and query optimization. Virtually all programming languages, BI tools, and analytics platforms support them natively. The surrounding ecosystem includes ORM libraries, database migration frameworks, and query profilers.

7. Use cases

Vector databases are essential in AI-first applications:

  • Semantic search in large text corpora.
  • Image and video similarity search in media libraries.
  • Real-time recommendation engines for eCommerce or streaming platforms.
  • Fraud detection based on behavioral pattern similarity.

Relational databases dominate in structured data systems:

  • Financial transaction processing with strict integrity requirements.
  • Inventory, supply chain, and ERP systems.
  • CRM platforms storing structured customer data.
  • Government records and compliance-driven data management.

Tips from the expert

Anil Inamdar

Anil Inamdar

Director, Professional Services

Anil has 20+ years of experience in data and analytics roles. Joining Instaclustr in 2019, he works with organizations to drive successful data-centric digital transformations via the right cultural, operational, architectural, and technological roadmaps. Before Instaclustr, he held data & analytics leadership roles at Dell EMC, Accenture, and Visa.

In my experience, here are tips that can help you better leverage vector and relational databases in practice:

  1. Use dual-write strategies for hybrid systems: In systems combining relational and vector databases, implement a dual-write mechanism where metadata and vector embeddings are stored simultaneously. Ensure eventual consistency through background jobs or change data capture (CDC) pipelines.
  2. Optimize ANN search with quantization-aware model training: Train embedding models with quantization in mind to improve ANN index performance. Quantization-aware training can yield vectors that are more robust to approximate search errors, improving recall and reducing latency.
  3. Leverage vector compression for better cost-efficiency: Use advanced compression techniques (e.g., scalar quantization, vector quantization, or PQ with residuals) to reduce storage costs and memory footprint, especially when dealing with billions of vectors.
  4. Enrich similarity search with hybrid scoring: Combine vector similarity scores with metadata-based ranking (e.g., recency, popularity, or user profile matching) to improve relevance in search or recommendation engines.
  5. Deploy vector indexes using GPU acceleration for real-time workloads: For high-throughput, low-latency environments, use GPU-accelerated vector search libraries like FAISS with GPU backends or libraries like cuML/RAFT to reduce response times dramatically.

Pros and cons of relational databases

Relational databases have been the backbone of enterprise systems for decades, offering reliable and secure data management. Their rigid structure ensures data integrity, but it can also limit flexibility when working with unstructured or high-dimensional data.

Pros

  • Strong ACID compliance ensures data consistency and reliability in transactional workloads
  • Mature security features, including fine-grained permissions and auditing
  • Well-established ecosystem with abundant tools, libraries, and community support
  • Highly optimized for structured queries and complex joins
  • Long history of use in mission-critical enterprise applications

Cons

  • Rigid schemas make handling unstructured or rapidly changing data difficult
  • Scaling horizontally can be complex and expensive compared to NoSQL or vector databases
  • Poor performance for similarity or high-dimensional vector searches without extensions
  • Indexing and storage models not designed for AI/ML embedding workloads
  • Schema changes in large systems can be slow and risky

Pros and cons of vector databases

Vector databases are purpose-built for similarity search and AI-driven applications, enabling fast retrieval of semantically related content. While they handle embeddings and large-scale vector operations well, they lack the maturity and broad ecosystem of traditional databases.

Pros

  • Optimized for high-dimensional similarity search at scale
  • Supports approximate nearest neighbor (ANN) indexing for real-time retrieval
  • Integrates directly with AI/ML pipelines for embedding-based search
  • Flexible schema allows easy addition of metadata fields
  • Scales horizontally to handle billions of vectors efficiently

Cons

  • Less mature security and compliance features compared to relational databases
  • Limited tooling for administration, migration, and query optimization
  • Generally weaker transactional guarantees (often eventual consistency)
  • Higher storage and memory requirements for large vector indices
  • Still evolving ecosystem with fewer standardized integration patterns

Related content: Read our guide to vector database use cases

Relational database vs vector database: How to choose?

Choosing between a relational and a vector database depends on the nature of your data, query patterns, and system requirements. While both can coexist in hybrid architectures, selecting the right primary store is critical for performance, scalability, and maintainability.

Key considerations

  • Data type and structure: Use a relational database for structured, tabular data with well-defined relationships; use a vector database for unstructured or semi-structured data represented as embeddings.
  • Query patterns: Choose relational databases for exact matches, complex joins, and aggregations; choose vector databases for similarity searches, semantic queries, and recommendation tasks.
  • Scalability requirements: Relational databases scale well vertically but need complex setups for horizontal scaling; vector databases are built for horizontal scale across large vector sets.
  • Latency expectations: For sub-100 ms similarity search at scale, vector databases with ANN indexing are optimal; for predictable transactional latency, relational systems are better.
  • Ecosystem and tooling: Mature relational databases offer robust tooling and integrations; vector databases may require more custom development and monitoring solutions.
  • Security and compliance: If fine-grained access control, audit logs, and strict compliance are priorities, relational databases are generally stronger; vector databases are improving but less mature in this area.
  • Hybrid possibilities: Many AI-driven systems pair relational databases for metadata and transactions with vector databases for semantic search, enabling the best of both worlds.

Unleashing the power of vector databases with Instaclustr

Harnessing the power of artificial intelligence and machine learning requires a new approach to data management. Vector databases are at the forefront of this shift, providing the essential infrastructure for similarity searches that fuel applications like recommendation engines, image recognition, and natural language processing. Instaclustr delivers a robust, enterprise-ready platform for deploying, managing, and scaling these critical technologies, empowering smarter, more responsive applications.

Instaclustr simplifies the complexity of running high-performance vector databases. It provides production-ready deployments of leading open source technologies like Casssandra, PostgreSQL with the pgvector extension and OpenSearch, fully optimized for vector search workloads. This enables the power of advanced similarity search without the operational overhead. Instaclustr handles the provisioning, monitoring, and maintenance, so DevOps teams can focus on innovation instead of infrastructure management. The Instaclustr Managed Platform is built for scalability, allowing seamless growth of clusters as data volume and query traffic increase, ensuring consistent performance at any scale.

Instaclustr is designed to fit perfectly within existing data ecosystems. By combining vector database capabilities with other technologies, such as Apache Kafka® for real-time data streaming and Apache Cassandra for massive-scale data storage, organizations can build a unified, powerful data layer. This synergy allows for the creation of sophisticated, end-to-end data pipelines that support even the most demanding data-driven applications. Backed by world-class, 24x7x365 expert support, Instaclustr unlocks the full potential of data.

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