AI agents generate and consume far more data than traditional software applications.
They store application state, retrieve knowledge, maintain conversation history, search embeddings, process documents, and analyze large datasets as they complete increasingly complex tasks.
Traditional databases can support many of those workloads, but no single database is designed to handle all of them equally well. Different AI applications prioritize different kinds of data, retrieval methods, and performance requirements, leading developers to adopt a range of database architectures.
Some platforms build on familiar relational databases with AI capabilities such as vector search. Others are purpose-built vector databases designed for semantic retrieval, while broader data platforms combine analytics, machine learning, and AI workloads within a unified environment.
This guide focuses on nine of the leading database platforms developers are using to build AI agents. While the list is not exhaustive, it does cover many of the database technologies teams evaluate when building agentic systems.
By the end of this roundup, you'll understand where each platform excels, the tradeoffs behind its design, and the types of AI applications it's best suited to support.
What Is a Database for AI Agents?
A database for AI agents stores and retrieves the information agents need to complete tasks.
That information can take many forms. AI agents may store application state, user data, conversation history, documents, embeddings, search indexes, or the results of previous actions. As applications become more capable, they often rely on multiple kinds of data that need to be accessed quickly and reliably.
Different databases are designed for different workloads. Relational databases organize structured application data, vector databases store embeddings for semantic search, document databases handle flexible data structures, and data platforms support large-scale analytics and machine learning.
In addition, many production AI applications use more than one database. An agent might store user accounts and application state in PostgreSQL, retrieve knowledge from a vector database, cache frequently accessed data in Redis, and analyze historical data in a platform like Databricks.
Choosing the right database depends on what your agent needs to store, how it retrieves information, and how the application is expected to scale.
How to Evaluate a Database for AI Agents
While every application has different requirements, we've found that these five considerations provide a useful starting point when evaluating databases for AI agents.
Data Model
Start by considering the type of data your agent needs to store.
Relational databases organize structured application data, document databases support flexible schemas, vector databases store embeddings for semantic search, and data platforms manage large analytical datasets. Choosing a database that matches your primary workload can simplify development and improve long-term performance.
Query and Retrieval
Some applications primarily rely on SQL queries, while others depend on semantic search, metadata filtering, hybrid retrieval, or analytical queries across large datasets. Understanding how your agent accesses information will help narrow the field considerably.
Scalability and Performance
The database that works well during development may not be the right choice once your application reaches production.
As AI applications grow, consider how each platform handles larger datasets, concurrent workloads, indexing, replication, latency, and operational scaling. The right architecture should continue performing reliably as both your users and data increase.
AI-Native Capabilities
Some databases have added AI capabilities over time, while others were built specifically for AI workloads.
Features such as vector search, embedding storage, hybrid retrieval, and integrations with AI frameworks can reduce the amount of infrastructure developers need to build themselves. Which capabilities matter most depends on the type of application you're building.
Developer Experience and Ecosystem
Consider how easily each platform integrates with your existing programming languages, frameworks, deployment environment, and developer tools. Well-designed SDKs, documentation, and managed services can significantly reduce operational overhead as your application evolves.
The 9 Best Databases for AI Agents
1. Databricks Lakebase
Best for: Enterprise AI applications that need a transactional database tightly integrated with analytics, machine learning, and AI workloads.
Databricks Lakebase is a fully managed PostgreSQL database integrated into the Databricks Data Intelligence Platform. It combines low-latency transactional storage with native access to the broader Databricks ecosystem, allowing teams to build operational applications alongside analytics and AI workloads. Key capabilities include autoscaling, instant branching, and Unity Catalog integration.
Lakebase is designed to support AI applications and agents. It can serve as a state store for AI agents, power application backends, and synchronize operational data with the lakehouse. It also supports semantic, keyword, and hybrid retrieval through Lakebase Search, allowing developers to combine transactional data with AI-powered search within the same PostgreSQL environment.
For organizations already building on Databricks, Lakebase provides a transactional database that fits naturally into the broader data and AI platform. Teams can build operational applications while keeping application data closely connected to analytics, machine learning, and governed enterprise data.
Why You Might Choose Databricks Lakebase
- Provides a fully managed PostgreSQL database for AI applications.
- Integrates transactional workloads with the Databricks Data Intelligence Platform.
- Supports semantic, keyword, and hybrid retrieval through Lakebase Search.
- Keeps operational data closely connected to analytics and machine learning workloads.
- Scales automatically without requiring database infrastructure management.
- Includes built-in governance through Unity Catalog.
Potential Tradeoffs
Lakebase is designed for teams building on the Databricks Data Intelligence Platform. Organizations looking for a standalone PostgreSQL service or a dedicated vector database may also want to evaluate more specialized alternatives.
2. Neon
Best for: AI applications that need a serverless PostgreSQL database for transactional data, agent state, and modern development workflows.
Neon is a serverless PostgreSQL platform that separates compute from storage, allowing each to scale independently. Developers get the full PostgreSQL experience while gaining capabilities such as instant branching, scale-to-zero compute, rapid provisioning, and fully managed infrastructure.
For AI applications, Neon provides a foundation for storing structured application data, user information, workflow state, and agent state. It also supports vector storage and similarity search through the pgvector extension, allowing developers to combine transactional data and semantic retrieval within PostgreSQL for many AI workloads. In addition, Neon offers APIs, an MCP server, and the @neondatabase/toolkit to help AI agents create databases, run queries, and manage database operations programmatically.
One of Neon's defining capabilities is database branching. Developers can create isolated copies of a database in seconds, making it easier to test new features, evaluate agent behavior, or experiment with different workflows without affecting production data. Combined with serverless scaling and PostgreSQL compatibility, this makes Neon a strong choice for teams building and iterating on AI-powered applications.
Why You Might Choose Neon
- Provides a fully managed serverless PostgreSQL database.
- Separates compute and storage for independent autoscaling.
- Supports vector search through the pgvector extension.
- Creates instant database branches for development and testing.
- Includes APIs, an MCP server, and AI tooling for programmatic database management.
- Maintains full PostgreSQL compatibility with existing applications and tools.
Potential Tradeoffs
Neon is designed around PostgreSQL and operational application data. Teams building retrieval-heavy applications with very large embedding collections or highly specialized vector search requirements may also want to evaluate purpose-built vector databases alongside PostgreSQL.
3. MongoDB
Best for: AI applications that need a flexible document database with integrated vector search and support for evolving data models.
MongoDB is a document database that stores data in flexible BSON documents rather than fixed tables and rows. This document model makes it well suited for AI applications that work with user-generated content, conversation history, documents, and other data that may change structure over time. MongoDB Atlas, the company's fully managed cloud platform, includes Atlas Vector Search, allowing developers to combine operational data and vector search within the same database.
Instead of maintaining separate systems for application data and semantic retrieval, developers can use MongoDB Atlas to support both workloads. Atlas Vector Search combines vector similarity search with metadata filtering, aggregation, and the broader MongoDB query language, making it easier to build retrieval-augmented generation (RAG) applications and AI agents on a single platform.
For teams already building on MongoDB, Atlas Vector Search extends an existing database with AI retrieval capabilities rather than requiring a separate vector database. This allows developers to introduce semantic search while continuing to use the same document model, APIs, and operational workflows.
Why You Might Choose MongoDB
- Stores structured, semi-structured, and unstructured data in a flexible document model.
- Supports semantic retrieval through Atlas Vector Search.
- Combines vector search with metadata filtering and aggregation.
- Extends existing MongoDB applications with AI search capabilities.
- Integrates with popular AI frameworks and developer tools.
- Maintains operational and vector data within the same database.
Potential Tradeoffs
MongoDB is built around a document data model rather than PostgreSQL's relational model. Teams that rely heavily on SQL workflows or require advanced relational features may also want to evaluate PostgreSQL-based databases.
4. Redis
Best for: AI applications that need low-latency data access, caching, and real-time application state.
Redis is an in-memory data platform that stores data primarily in memory, enabling extremely low-latency reads and writes. Originally adopted for caching and session management, Redis has expanded to support vector search, semantic retrieval, and other AI workloads through Redis Query Engine. This makes it well suited for AI applications that need to retrieve and update data in milliseconds.
For AI applications, Redis is commonly used to store session state, conversation history, cached retrieval results, and other latency-sensitive application data. Redis Query Engine also supports vector similarity search, allowing developers to combine semantic retrieval with operational data on the same platform for AI workloads where response time is critical.
Redis is frequently used alongside other databases rather than replacing them. A production AI application might use PostgreSQL for transactional data, a vector database for long-term semantic retrieval, and Redis to accelerate frequently accessed data and reduce response times. This complementary architecture allows each database to handle the workload it is designed for.
Why You Might Choose Redis
- Stores frequently accessed data in memory for low-latency access.
- Supports vector similarity search through Redis Query Engine.
- Accelerates AI applications with built-in caching capabilities.
- Manages session state and conversation history for AI applications.
- Integrates with popular AI frameworks and developer tools.
- Complements transactional and analytical databases in production architectures.
Potential Tradeoffs
Redis is designed for low-latency operational workloads rather than serving as a system of record for long-term application data. Teams building transactional applications or managing large analytical datasets will typically pair Redis with databases designed specifically for those workloads.
5. Pinecone
Best for: AI applications that need a serverless vector database for production semantic search and retrieval.
Pinecone is a serverless vector database built specifically for AI applications. It stores, indexes, and queries vector embeddings, allowing developers to retrieve information based on semantic similarity rather than exact keyword matches. This makes Pinecone well suited for retrieval-augmented generation (RAG), semantic search, recommendation systems, and AI agents that need to retrieve relevant context from large knowledge bases.
Pinecone manages the infrastructure required to operate a production vector database, including indexing, scaling, replication, and filtering. It also supports metadata filtering, hybrid search, integrated inference models, and multitenant workloads, allowing developers to build production retrieval systems without managing the underlying infrastructure.
Because Pinecone focuses exclusively on semantic retrieval, it is typically deployed alongside another database rather than replacing one. A common architecture pairs Pinecone with a relational or document database, using Pinecone to retrieve relevant context while the primary database continues to manage operational application data.
Why You Might Choose Pinecone
- Provides a serverless vector database for AI applications.
- Optimizes semantic retrieval across large embedding collections.
- Supports metadata filtering alongside vector similarity search.
- Includes integrated inference models for retrieval workflows.
- Scales automatically without requiring infrastructure management.
- Integrates with popular AI frameworks and orchestration tools.
Potential Tradeoffs
Pinecone is designed specifically for semantic retrieval rather than transactional application data. Teams building AI applications typically pair it with a relational or document database that manages operational workloads.
6. Weaviate
Best for: AI applications that need an open-source AI-native database with flexible retrieval and deployment options.
Weaviate is an open-source AI-native vector database designed for semantic retrieval. It stores, indexes, and queries vector embeddings while also supporting keyword search, metadata filtering, and hybrid search. This allows developers to combine semantic and lexical retrieval within a single system, making Weaviate well suited for retrieval-augmented generation (RAG), recommendation systems, and AI agents that search large collections of structured and unstructured data.
One of Weaviate's defining characteristics is its modular architecture. Developers can choose from built-in vectorizers, integrate external embedding providers, and connect to leading LLMs for retrieval and generative AI workflows. Weaviate also supports reranking, multimodal search, and hybrid retrieval, giving teams flexibility to build AI applications around different models and retrieval strategies.
Weaviate is available as both an open-source database and a fully managed cloud service. Organizations can self-host Weaviate, deploy it in their own cloud environment, or use Weaviate Cloud while maintaining the same core database architecture across environments.
Why You Might Choose Weaviate
- Provides an open-source AI-native vector database.
- Supports semantic, keyword, and hybrid search.
- Integrates with leading embedding models and LLM providers.
- Enables multimodal retrieval across multiple data types.
- Offers both self-managed and fully managed deployment options.
- Supports metadata filtering alongside vector similarity search.
Potential Tradeoffs
Weaviate is designed specifically for AI retrieval rather than transactional application data. Teams building production AI applications typically pair it with a relational or document database that manages operational workloads.
7. Qdrant
Best for: AI applications that need an open-source vector database with advanced filtering and production-scale semantic search.
Qdrant is an open-source vector database built for semantic search and AI applications. It stores, indexes, and queries vector embeddings while combining vector similarity search with structured payload filtering. This allows developers to retrieve results based on both semantic meaning and structured metadata, making Qdrant well suited for retrieval-augmented generation (RAG), recommendation systems, and AI agents that need precise, context-aware retrieval.
One of Qdrant's key strengths is its filtering engine. Developers can combine vector similarity search with advanced payload filtering, payload indexing, and hybrid search to narrow retrieval results without sacrificing performance. Qdrant also supports multitenancy, distributed deployments, and quantization techniques for optimizing large-scale vector collections.
Qdrant is available as both an open-source database and a fully managed cloud service. Organizations can self-host Qdrant for full infrastructure control or use Qdrant Cloud while maintaining the same APIs and core database architecture across environments.
Why You Might Choose Qdrant
- Provides an open-source vector database for AI applications.
- Combines vector similarity search with advanced metadata filtering.
- Supports hybrid search across dense and sparse retrieval.
- Optimizes large vector collections with built-in quantization techniques.
- Offers both self-managed and fully managed deployment options.
- Integrates with popular AI frameworks and orchestration tools.
Potential Tradeoffs
Qdrant is designed specifically for semantic retrieval rather than transactional application data. Teams building production AI applications typically pair it with a relational or document database that manages operational workloads.
8. Milvus
Best for: AI applications that need an open-source vector database for large-scale similarity search and distributed deployments.
Milvus is an open-source vector database designed for storing, indexing, and querying high-dimensional vector embeddings. It supports multiple vector index types and similarity metrics, allowing developers to optimize retrieval for different AI workloads. This makes Milvus well suited for retrieval-augmented generation (RAG), recommendation systems, image search, and AI agents that retrieve context from large embedding collections.
Milvus uses a distributed architecture that allows organizations to scale the database across multiple nodes as datasets and query volumes grow. It also supports metadata filtering, hybrid search, multitenancy, and GPU acceleration for compatible workloads.
Milvus is available as an open-source database and as a fully managed cloud service through Zilliz Cloud. Organizations can self-host Milvus for complete infrastructure control or use Zilliz Cloud while maintaining compatibility with the open-source project.
Why You Might Choose Milvus
- Provides an open-source vector database for AI applications.
- Supports multiple indexing algorithms and similarity metrics.
- Scales distributed vector search across large datasets.
- Supports metadata filtering alongside vector similarity search.
- Offers both self-managed and fully managed deployment options.
- Integrates with popular AI frameworks and orchestration tools.
Potential Tradeoffs
Milvus is designed specifically for semantic retrieval rather than transactional application data. Teams building production AI applications typically pair it with a relational or document database that manages operational workloads.
9. PostgreSQL
Best for: AI applications that need a relational database with a mature ecosystem and support for AI extensions.
PostgreSQL is an open-source relational database that serves as the foundation for many modern applications. It stores structured data using tables, supports complex SQL queries, and offers a broad ecosystem of extensions that allow developers to add new capabilities without changing database platforms. For many AI applications, PostgreSQL acts as the system of record for users, application state, workflows, and transactional data.
The PostgreSQL ecosystem has expanded to support AI workloads through extensions such as pgvector, which adds vector storage and similarity search. This allows developers to store embeddings alongside operational data and perform semantic retrieval within the same database. Depending on an application's scale and retrieval requirements, this can simplify the architecture by reducing the need for a separate vector database.
Because PostgreSQL is widely supported across cloud providers, managed services, and development frameworks, it fits naturally into many existing software stacks. Teams can continue using familiar PostgreSQL tools and workflows while adding AI capabilities as retrieval, embeddings, and agent workflows become part of the application architecture.
Why You Might Choose PostgreSQL
- Provides a mature relational database for AI applications.
- Supports vector search through the pgvector extension.
- Stores transactional data and embeddings within the same database.
- Integrates with a broad ecosystem of tools and managed services.
- Maintains compatibility with standard SQL and PostgreSQL applications.
- Extends existing PostgreSQL deployments with AI capabilities.
Potential Tradeoffs
PostgreSQL is designed as a general-purpose relational database rather than a purpose-built vector database. Applications with very large embedding collections or highly specialized semantic retrieval requirements may also want to evaluate dedicated vector databases.
Which Database Should You Choose?
Choose Databricks Lakebase if...
You want a fully managed PostgreSQL database that keeps operational application data closely connected to analytics, machine learning, and governed enterprise data on the Databricks Data Intelligence Platform.
Choose Neon if...
You want a serverless PostgreSQL database with autoscaling, database branching, and tooling that allows AI agents to manage database operations programmatically.
Choose MongoDB if...
You want to store flexible document data and add semantic retrieval through Atlas Vector Search without introducing a separate vector database.
Choose Redis if...
You need low-latency access to cached data, session state, conversation history, and other real-time application data.
Choose Pinecone if...
You want a serverless vector database that manages infrastructure for production semantic retrieval workloads.
Choose Weaviate if...
You want an open-source AI-native vector database with hybrid search, multimodal retrieval, flexible model integrations, and multiple deployment options.
Choose Qdrant if...
You want an open-source vector database that combines vector similarity search with advanced metadata filtering and hybrid retrieval.
Choose Milvus if...
You need distributed vector search across large embedding collections and want the flexibility to self-host the database or use Zilliz Cloud.
Choose PostgreSQL if...
You want a mature relational database and plan to add vector search through extensions such as pgvector while continuing to use familiar SQL workflows.
