Every AI application, from chatbots to copilots to recommendation systems, has one thing in common: it constantly needs to store, retrieve, and make sense of data. A lot of it.
That data isn’t just text or numbers anymore. It’s embeddings, context windows, user interactions, fine-tuning sets, and metadata that connect everything together. The way an AI system performs often depends less on the model itself and more on how well it can access and organize that data in real time.
That’s why databases are back in the spotlight. We’re no longer just asking “where should I host my data?”, we’re asking “what database can handle my AI workloads?”
In this post, we’ll cover how SQL and NoSQL databases fit into modern AI stacks, what trade-offs actually matter, and how to pick the right one for your use case.
Understanding SQL and NoSQL databases
Before comparing what works best for AI applications, it helps to understand what these database types actually are and how they differ.
SQL Databases
SQL Databases are where most developers start. They store data in structured tables with rows, columns, and defined relationships. Think of systems like PostgreSQL, MySQL, or SQLite.
They’re designed around consistency. Every piece of data follows a schema, and every query follows rules. That makes them great for applications where relationships and transactions matter, like financial systems, CRMs, or analytics dashboards.
NoSQL Databases
NoSQL Databases were built to handle the kind of data SQL struggled with, data that’s large, fast-changing, or doesn’t fit neatly into tables. They can store information as documents, key-value pairs, graphs, or wide columns, depending on the system.
Popular examples include MongoDB, Cassandra, and Redis. They trade rigid structure for flexibility and horizontal scalability, which is why they’re often used for high-traffic web apps, real-time data, or anything that needs to evolve quickly without schema constraints.
In short:
- SQL is about structure and relationships.
- NoSQL is about flexibility and scale.
How Databases are evolving for AI workloads
AI hasn’t changed what data is. It’s changed how we store, query, and retrieve it.
Modern databases now need to handle structured, unstructured, and semantic data side by side.
Here are some factors that are playing an important role:
Handling different data types
Modern applications mix structured tables, documents, and embeddings.
- PostgreSQL supports JSONB for semi-structured data and pgvector for embeddings, making it a good choice when you want to keep relational data and vector representations in one place.
- MongoDB offers schema-flexible documents and native vector search, which works well for workloads that combine metadata with semantic retrieval.
Similarity search as a core capability
Instead of exact matches, AI systems often need to find related or semantically similar items.
- Postgres + pgvector supports approximate nearest-neighbor (ANN) search using HNSW indexes for fast vector lookups.
- MongoDB’s vector search provides cosine or Euclidean distance queries within the same document structure, so you can filter and retrieve by meaning in one query.
Performance and scaling for continuous data
AI pipelines generate embeddings and metadata constantly, which means the database must handle high write throughput and frequent reindexing.
- Postgres performs well for moderate-scale workloads where structure and consistency matter.
- MongoDB handles dynamic, distributed data more easily, making it fit for real-time or frequently updated AI systems.
Which Database should I choose for my AI application
While there’s no one-size-fits-all answer, generally document-based (NoSQL) databases like MongoDB tend to align better with the way AI systems actually work.
Here’s how:
Flexible data models
AI data changes constantly. New fields, formats, and embeddings appear as models evolve. Document databases handle this without schema migrations.
Easier handling of unstructured data
AI systems deal with text, logs, and contextual metadata. Document stores naturally map to this kind of data and let you bundle it all together.
Built-in scalability
NoSQL databases like MongoDB scale horizontally across clusters, making it easier to handle growing datasets or continuous data ingestion.
Native vector search support
MongoDB now includes Atlas Vector Search that now supports vector queries directly, allowing you to combine semantic search with filters on metadata in a single query.
Fast iteration for experiments
When you’re iterating on prompts, context, or data formats, NoSQL’s flexibility means you can experiment faster without reworking your schema every time.
That said, relational databases like PostgreSQL still have their place, especially when you need strong consistency, transactional integrity, or complex joins. With extensions like pgvector, they can even serve smaller or hybrid AI workloads efficiently.
Frequently asked questions
1. Which is the best database for AI applications?
There isn’t a single “best” database for AI. It depends on your use case.
If you’re dealing with unstructured or rapidly changing data, MongoDB is a strong choice thanks to its flexible schema, native vector search, and scalability.
For structured or transactional workloads, PostgreSQL works great, especially with the pgvector extension for embeddings.
For large-scale similarity search or LLM retrieval, purpose-built vector databases like Pinecone, Weaviate, or Milvus can also play a key role.
2. Which databases are most commonly used for AI development?
The most widely used databases for AI right now include:
- MongoDB for document-based and hybrid workloads
- PostgreSQL for structured data with vector support via
pgvector - Pinecone, Weaviate, and Milvus for dedicated vector similarity search
- Redis for fast caching, embeddings, and session data
Together, they cover most AI needs, from managing user context and metadata to embedding storage and retrieval-augmented generation (RAG) pipelines.
3. Is SQL or NoSQL better for AI applications?
In general, NoSQL databases align better with AI workloads. They’re flexible, schema-free, and built to handle unstructured data like embeddings and contextual metadata.
That said, SQL databases like PostgreSQL are evolving fast. With extensions like pgvector, you can now store and query embeddings natively, making them a great fit for smaller-scale or hybrid systems where relationships and consistency still matter.
4. Can MongoDB be used for AI?
Yes, MongoDB is one of the most popular choices for AI and GenAI development. It supports flexible document structures, integrates with major AI frameworks, and offers Atlas Vector Search for semantic queries.
Wrapping up
Choosing the right database is one of the most important decisions you’ll make when building an AI application. It determines how fast you can experiment, how easily you can scale, and how efficiently your models can access the right context in real time.
SQL databases still make sense for workloads where structure, consistency, and transactional guarantees come first. But as AI systems move toward handling unstructured, evolving, and semantic data, document-based databases like MongoDB align more naturally with that reality.
In the end, the best database for AI isn’t the most powerful. It’s the one that grows with your data, adapts to your workflow, and helps your models make sense of the world faster.
