Choosing the right database for an AI agent depends on the type of data you need to store and retrieve. For a beginner, here’s a simple breakdown:
1. Types of AI Agent Data
- Structured Data (e.g., user profiles, logs) → Relational Databases (SQL)
- Unstructured Data (e.g., text, images, vectors) → NoSQL or Vector Databases
- Knowledge Storage (e.g., embeddings, RAG) → Vector Databases
- Real-time Data (e.g., chat history) → NoSQL or In-Memory Databases
2. Database Options for AI Agents
| Database Type | Use Case | Beginner-Friendly Options |
|---|---|---|
| SQL Databases (Structured) | Storing user info, logs | PostgreSQL, MySQL, SQLite |
| NoSQL Databases (Unstructured) | Chat history, JSON data | MongoDB, Firebase |
| Vector Databases (AI Knowledge, Embeddings) | Storing AI model embeddings | ChromaDB, Weaviate, Pinecone, Qdrant |
| In-Memory Databases (Fast Retrieval) | Caching AI responses | Redis |
3. Beginner Recommendations
- For simple AI projects → MongoDB (NoSQL, flexible, beginner-friendly)
- For AI chatbots (with memory) → MongoDB + Redis (for caching)
- For RAG-based AI (knowledge retrieval) → MongoDB vector, ChromaDB, or Weaviate
4. Things to Consider
✅ Ease of use – Choose a database with good documentation and easy setup.
✅ Scalability – If you expect growth, NoSQL and vector DBs scale better.
✅ Integration – Ensure the database supports AI tools (e.g., LangChain, LLMs).
Here are some of the best choices based on use cases for you:
1. Vector Databases (For AI Agents & Retrieval-Augmented Generation)
These are optimized for storing and searching high-dimensional embeddings, making them ideal for LLM-powered applications.
🔹 MongoDB Atlas (Vector Search)
- ✅ Best for: AI apps needing a mix of structured data and vector search.
- ✅ Supports hybrid search (text + vector) and integrates well with LangChain, OpenAI, DeepSeek, etc.
- ✅ No need for a separate database; combines AI, vector, and traditional data storage.
🔹 Pinecone
- ✅ Best for: Fast vector retrieval in RAG (Retrieval-Augmented Generation) AI.
- ✅ Serverless and handles billions of embeddings with low-latency search.
- 🚫 Need another DB for structured data (e.g., PostgreSQL).
🔹 Weaviate
- ✅ Best for: Multi-modal AI applications (text, images, audio embeddings).
- ✅ Open-source and supports hybrid queries (structured + unstructured search).
- ✅ Integrates with OpenAI, DeepSeek, Hugging Face.
🔹 Qdrant
- ✅ Best for: On-premise self-hosted vector search (GDPR/enterprise compliance).
- ✅ Rust-based, optimized for speed.
🔹 FAISS (Facebook AI Similarity Search)
- ✅ Best for: On-device offline AI vector search.
- 🚫 Lacks cloud scalability.
2. Relational Databases (For AI Metadata, Logs, and Transactions)
These are needed alongside vector DBs for structured data.
🔹 PostgreSQL + pgvector
- ✅ Best for: AI applications needing relational + vector search.
- ✅ Open-source with good AI extensions (pgvector for embeddings).
- ✅ Strong ACID compliance for transactions.
🔹 MySQL + HeatWave
- ✅ Best for: AI-powered analytics with MySQL familiarity.
- ✅ Offers vector search + OLAP capabilities.
🔹 ClickHouse
- ✅ Best for: High-speed analytics and AI-driven real-time event processing.
3. NoSQL Databases (For AI Agents and Chatbots)
These handle semi-structured/unstructured data well.
🔹 MongoDB (Atlas)
- ✅ Best for: AI-powered apps needing JSON-based flexible storage.
- ✅ Integrated Vector Search (alternative to Pinecone/Weaviate).
🔹 Redis + Redis Vector
- ✅ Best for: AI caching and real-time AI agents.
- ✅ Ultra-fast in-memory vector search.
4. Time-Series & Graph Databases (For AI Insights)
If your AI app needs real-time data processing or relationship mapping:
🔹 InfluxDB
- ✅ Best for: AI-based IoT, logs, and real-time time-series data.
🔹 Neo4j
- ✅ Best for: AI knowledge graphs, reasoning, and context-aware AI.
Choosing the Right Stack
| Use Case | Best Database |
|---|---|
| LLM + RAG | MongoDB Atlas, Pinecone, Weaviate |
| Hybrid Search (Text + Vectors) | MongoDB, PostgreSQL (pgvector) |
| AI Chatbots (Real-time Memory) | Redis + Vector Search |
| Transactional AI Apps | PostgreSQL, MySQL |
| On-Premise AI | Qdrant, FAISS |
| Knowledge Graph AI | Neo4j |
| AI Event Processing | ClickHouse, InfluxDB |
Tech Stack for an AI Agent (2025)
- LLM Engine: OpenAI, DeepSeek, Mistral, Gemini, Llama 3
- Database: MongoDB (Vector Search) + PostgreSQL (Metadata)
- Vector Search: Pinecone, Weaviate, Qdrant
- Orchestration: LangChain, LlamaIndex
- Cache & Memory: Redis + Redis Vector
- Cloud Deployment: AWS Bedrock, Azure AI, GCP Vertex AI
Top comments (4)
I’m building a chat app for myself. What would be a good database to use that can store my data, including chunked text from PDF documents, as well as questions and responses?
Go with Qdrant, it's fast and easy to deploy with Docker. I use it in Production and process millions of documents without much trouble. Learn more about Qdrant here
PostgreSQL is also a good option for small to medium sized projects, it's not as fast as Qdrant at scale but for a few thousand documents it should be just fine: Learn more here
You can use a VectorDB like MongoDB Vector or Weaviate to store and retrieve chunked text from PDFs as embeddings. For an easy, beginner-friendly personal app, you can use a NoSQL database like MongoDB to store chat history.
Thanks! That’s a useful topic for me to research.