Choosing between relational, NoSQL, and vector databases depends on your workload:
- Use relational databases (like PostgreSQL, MySQL, MariaDB) for structured, transactional data.
- Use NoSQL databases (like MongoDB, Redis, Cassandra) for unstructured or scalable workloads.
- Use vector databases (like Qdrant, Weaviate, Milvus) for AI, embeddings, and semantic search.
Introduction
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?
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.
For a broader overview of all database types, check out our Ultimate Guide to Open-Source Databases (2025).
What Is a Relational Database?
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.
Examples:
Best for:
- Banking, e-commerce, ERP systems
- Applications with strong consistency requirements
- Reporting and analytics with structured datasets
👉 Related read: PostgreSQL vs MySQL vs MariaDB: Which Open-Source Database Should You Choose?
What Is a NoSQL Database?
NoSQL databases handle unstructured, semi-structured, or rapidly scaling workloads. Instead of fixed schemas, they offer flexibility and horizontal scalability.
Types of NoSQL:
- Document (e.g., MongoDB, FerretDB)
- Key-value (e.g., Redis, Valkey)
- Columnar (e.g., Cassandra)
- Search (e.g., OpenSearch)
Best for:
- Real-time apps (chat, gaming, IoT)
- Systems with high write throughput
- Flexible schemas where data changes often
Related read: Redis vs Valkey vs KeyDB: Choosing the Best In-Memory Database
What Is a Vector Database?
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.
Examples:
Best for:
- AI-powered search (semantic search, RAG pipelines)
- Personalized recommendations
- Large-scale ML datasets
👉 Related read: Top Open-Source Vector Databases (Qdrant, Weaviate, Milvus, ChromaDB) Compared
Relational vs NoSQL vs Vector: Quick Comparison
| Feature | Relational | NoSQL | Vector |
|---|---|---|---|
| Data Structure | Tables, rows, columns | Documents, key-value, wide-column | High-dimensional vectors |
| Query Language | SQL | JSON, APIs, custom query languages | Vector similarity search |
| Best For | Structured, transactional apps | Scalable, unstructured data | AI, ML, semantic search |
| Consistency | Strong (ACID) | Eventual (varies by type) | Consistency not primary focus |
| Scalability | Vertical + horizontal (limited) | Easy horizontal scaling | Built for large-scale AI |
How to Choose the Right Database
Ask these questions before deciding:
- Is my data structured and relational? → Go with relational databases.
- Do I need flexibility and scale? → Choose NoSQL databases.
- Am I building AI or semantic search features? → Opt for a vector database.
In practice, many modern systems use a polyglot approach — combining relational, NoSQL, and vector databases to cover different workloads. For example:
- PostgreSQL for transactions
- Redis for caching
- Qdrant for AI-powered search
Final Thoughts
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.
👉 Want more open-source hosting insights? Don’t miss our guide: Best Open-Source Databases for AI & ML Workloads
FAQ
What is the main difference between relational and NoSQL databases?
Relational databases use structured tables and SQL, while NoSQL databases allow flexible schemas and are optimized for scalability and high write throughput.
When should I use a vector database?
Use a vector database when you need semantic search, embeddings, or AI-powered applications like chatbots, recommendation engines, or image similarity search.
Can I use relational, NoSQL, and vector databases together?
Yes, many companies use a polyglot strategy — combining relational for transactions, NoSQL for scalability, and vector for AI — to optimize across workloads.
Which open-source vector database is best?
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.