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‎content/topics/open-source-databases/relational-vs-nosql-vs-vector-databases/images/cover.png‎

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‎content/topics/open-source-databases/relational-vs-nosql-vs-vector-databases/index.md‎

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---
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draft: false
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title: 'How to Choose Between Relational, NoSQL, and Vector Databases'
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date: '2025-09-26'
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summary: 'This guide explains the key differences between relational, NoSQL, and vector databases, highlighting their strengths, best use cases, and examples. Relational databases are best for structured data and transactions, NoSQL excels at scalability and flexibility, while vector databases power AI and semantic search. Learn how to choose the right database—or combine them in a polyglot approach—for your project.'
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description: 'Learn how to choose between relational, NoSQL, and vector databases. Compare use cases, strengths, and examples to find the best fit for your project.'
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tags: ["relational databases", "NoSQL", "vector databases", "open-source databases", "PostgreSQL", "MongoDB", "Qdrant", "Milvus", "Weaviate", "AI workloads"]
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categories: ['Databases', 'Open-Source Hosting', 'Cloud & Infrastructure']
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author: 'OctaByte'
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cover:
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image: images/cover.png
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caption: 'Choosing between Relational, NoSQL, and Vector databases — a visual guide for modern data infrastructure.'
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alt: 'Flat-style infographic showing icons for relational, NoSQL, and vector databases with the title “How to Choose Between Relational, NoSQL, and Vector Databases.”'
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relative: true
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ShowToc: true
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TocOpen: true
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---
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Choosing between **relational, NoSQL, and vector databases** depends on your workload:
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- Use **relational databases** (like PostgreSQL, MySQL, MariaDB) for structured, transactional data.
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- Use **NoSQL databases** (like MongoDB, Redis, Cassandra) for unstructured or scalable workloads.
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- Use **vector databases** (like Qdrant, Weaviate, Milvus) for AI, embeddings, and semantic search.
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---
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## Introduction
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If you’re building a new application or scaling your infrastructure, you’ll quickly face this question: **Should I use a relational, NoSQL, or vector database?**
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The short answer: it depends on your **data model, query needs, and scalability goals**. This guide breaks down the strengths, weaknesses, and **best use cases** of each type — so you can confidently choose the right database for your project.
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For a broader overview of all database types, check out our [Ultimate Guide to Open-Source Databases (2025)](/topics/open-source-databases/ultimate-guide-2025/).
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---
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## What Is a Relational Database?
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Relational databases organize data in **tables with rows and columns**, using SQL for queries. They shine when you need **structured data, relationships, and ACID transactions**.
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**Examples:**
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- [PostgreSQL](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/postgresql)
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- [MySQL](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/mysql)
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- [MariaDB](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/mariadb)
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**Best for:**
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- Banking, e-commerce, ERP systems
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- Applications with strong **consistency requirements**
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- Reporting and analytics with structured datasets
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👉 Related read: [PostgreSQL vs MySQL vs MariaDB: Which Open-Source Database Should You Choose?](/topics/open-source-databases/postgresql-vs-mysql-vs-mariadb/)
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---
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## What Is a NoSQL Database?
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NoSQL databases handle **unstructured, semi-structured, or rapidly scaling workloads**. Instead of fixed schemas, they offer flexibility and horizontal scalability.
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**Types of NoSQL:**
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- Document (e.g., MongoDB, [FerretDB](https://octabyte.io/fully-managed-open-source-services/databases/nosql/ferretdb))
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- Key-value (e.g., [Redis](https://octabyte.io/fully-managed-open-source-services/databases/nosql/redis), [Valkey](https://octabyte.io/fully-managed-open-source-services/databases/nosql/valkey))
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- Columnar (e.g., [Cassandra](https://octabyte.io/fully-managed-open-source-services/databases/nosql/cassandra))
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- Search (e.g., [OpenSearch](https://octabyte.io/fully-managed-open-source-services/databases/nosql/opensearch))
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**Best for:**
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- Real-time apps (chat, gaming, IoT)
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- Systems with **high write throughput**
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- Flexible schemas where data changes often
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Related read: [Redis vs Valkey vs KeyDB: Choosing the Best In-Memory Database](/topics/open-source-databases/redis-vs-valkey-vs-keydb/)
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---
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## What Is a Vector Database?
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Vector databases are designed for **AI and machine learning workloads**. They store data as **high-dimensional vectors** (numerical representations of text, images, or audio). This makes them essential for **semantic search, recommendations, and embeddings**.
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**Examples:**
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- [Qdrant](https://octabyte.io/fully-managed-open-source-services/databases/specialized-databases/qdrant)
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- [Weaviate](https://octabyte.io/fully-managed-open-source-services/databases/specialized-databases/weaviate)
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- [Milvus](https://octabyte.io/fully-managed-open-source-services/databases/specialized-databases/milvus)
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- [ChromaDB](https://octabyte.io/fully-managed-open-source-services/databases/specialized-databases/chromadb)
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**Best for:**
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- AI-powered search (semantic search, RAG pipelines)
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- Personalized recommendations
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- Large-scale ML datasets
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👉 Related read: [Top Open-Source Vector Databases (Qdrant, Weaviate, Milvus, ChromaDB) Compared](/topics/open-source-databases/vector-databases-comparison/)
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---
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## Relational vs NoSQL vs Vector: Quick Comparison
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| Feature | Relational | NoSQL | Vector |
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|----------------|----------------------------------|-----------------------------------------|---------------------------------|
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| **Data Structure** | Tables, rows, columns | Documents, key-value, wide-column | High-dimensional vectors |
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| **Query Language** | SQL | JSON, APIs, custom query languages | Vector similarity search |
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| **Best For** | Structured, transactional apps | Scalable, unstructured data | AI, ML, semantic search |
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| **Consistency** | Strong (ACID) | Eventual (varies by type) | Consistency not primary focus |
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| **Scalability** | Vertical + horizontal (limited) | Easy horizontal scaling | Built for large-scale AI |
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---
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## How to Choose the Right Database
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Ask these questions before deciding:
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1. **Is my data structured and relational?** → Go with **relational databases**.
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2. **Do I need flexibility and scale?** → Choose **NoSQL databases**.
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3. **Am I building AI or semantic search features?** → Opt for a **vector database**.
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In practice, many modern systems use a **polyglot approach** — combining relational, NoSQL, and vector databases to cover different workloads. For example:
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- PostgreSQL for transactions
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- Redis for caching
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- Qdrant for AI-powered search
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---
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## Final Thoughts
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Choosing between **relational, NoSQL, and vector databases** is less about “which is best” and more about **which is right for your use case**. For most modern apps, you’ll likely use a mix — relational for structured data, NoSQL for scalability, and vector for AI workloads.
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👉 Want more open-source hosting insights? Don’t miss our guide: [Best Open-Source Databases for AI & ML Workloads](/topics/open-source-databases/best-databases-ai-ml/)
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---
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## FAQ
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### What is the main difference between relational and NoSQL databases?
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Relational databases use structured tables and SQL, while NoSQL databases allow flexible schemas and are optimized for scalability and high write throughput.
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### When should I use a vector database?
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Use a vector database when you need semantic search, embeddings, or AI-powered applications like chatbots, recommendation engines, or image similarity search.
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### Can I use relational, NoSQL, and vector databases together?
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Yes, many companies use a polyglot strategy — combining relational for transactions, NoSQL for scalability, and vector for AI — to optimize across workloads.
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### Which open-source vector database is best?
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It depends on your use case. **Qdrant** and **Weaviate** are great for general semantic search, **Milvus** is optimized for GenAI at scale, and **ChromaDB** is lightweight and developer-friendly.

‎content/topics/open-source-databases/vector-databases-comparison/index.md‎

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If you’re experimenting with **LLMs, semantic search, or AI-driven recommendations**, choosing the right database can accelerate development and cut costs.
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Want more open-source hosting insights? Don’t miss our guide on *How to Choose Between Relational, NoSQL, and Vector Databases*.
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Want more open-source hosting insights? Don’t miss our guide on [How to Choose Between Relational, NoSQL, and Vector Databases](../relational-vs-nosql-vs-vector-databases/).

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