2026 年最佳 AI 数据库方案对比：从 pgvector 到 Pinecone、Milvus

To get AI into production, you need more than just a vector store. . Your RAG pipelines require complex enterprise knowledge, while your AI agents need to access and maintain real-time operational data in order to function.. The challenge here is clear: you have to bridge the gap between accurate AI search and traditional transactional databases.

The best AI database depends on how you bring these capabilities together. You may extend an existing Postgres database with specialized systems or consolidate these workloads within a unified data platform. However, each approach involves tradeoffs in data freshness and performance as applications move into production.

This guide compares 10 AI database solutions across search and retrieval capabilities, serverless scaling, Postgres compatibility, high concurrency, and low-latency performance. We’ll examine how each option performs across these criteria and where each one fits based on your existing stack and production AI workload.

TL;DR

• The best AI database depends on your existing data stack and workload. Key considerations include search capabilities, Postgres compatibility, serverless scaling, concurrency, and latency.
• Existing PostgreSQL teams can use pgvector to add vector search without introducing a separate database. For workloads centered on large-scale vector retrieval, dedicated databases such as Pinecone, Qdrant, Milvus, and Weaviate provide purpose-built search capabilities.
• MongoDB Atlas Vector Search and Redis Vector are strong options when vector retrieval needs to remain close to application data already stored within those ecosystems.
• If you need transactional Postgres and AI search alongside analytics, data engineering, and machine learning, Lakebase brings these workloads together within the Databricks platform.

What is an AI database solution?

An AI database is a database designed to store, retrieve, and serve the data used by AI applications. This can include vector embeddings for semantic search, unstructured content retrieved by Retrieval-Augmented Generation (RAG) systems, and operational data that AI agents read and update as they execute tasks.

AI databases extend traditional database capabilities with features designed for AI retrieval patterns, such as vector search, full-text search, and hybrid search. Depending on the architecture, they may specialize in high-performance vector retrieval or combine AI search with relational data and transactional operations. This allows applications to retrieve relevant context while keeping that data close to the systems where it is stored and updated.

Categories of AI database solutions

The solutions in this guide generally fall into two categories based on how they store and retrieve data:

• Vector databases: These databases are purpose-built to store, index, and search vector embeddings at scale. Solutions such as Pinecone, Milvus, Weaviate, and Qdrant are designed primarily for similarity and semantic search, making them well-suited to retrieval-heavy workloads such as RAG and recommendation systems.
• Relational databases with AI search capabilities: These solutions add vector and other AI search capabilities to relational database systems. PostgreSQL with pgvector and Lakebase Postgres, for example, allow you to combine AI retrieval with structured data and transactional operations rather than maintaining a separate vector database.

This guide compares both approaches to help you determine when a specialized vector database makes sense and when keeping AI and operational data within a relational database is the better fit.

How we evaluated these AI database solutions

Strong vector search performance alone does not determine whether a database will work well in production. You also need to consider how it scales, integrates with your existing data stack, handles concurrent workloads, and fits into your broader data architecture. We evaluated each solution across the following criteria:

• Search capabilities: We considered the retrieval methods each database supports, including vector, full-text, and hybrid search. This matters when your application needs to combine semantic similarity with keyword or structured retrieval.
• Serverless: A serverless database removes the need to provision and manage database infrastructure. This can reduce operational overhead and make it easier to create and run databases for AI applications without managing the underlying servers.
• Elasticity: We evaluated how well each solution can scale compute with changing demand, including whether it can scale down when activity drops. Elasticity is particularly useful for AI workloads with unpredictable traffic or variable agent activity.
• Postgres compatibility: We considered whether each solution supports PostgreSQL and its surrounding ecosystem of SQL tooling, drivers, ORMs, and operational workflows. For teams already using Postgres, compatibility can reduce migration effort and avoid introducing an entirely new data layer.
• High concurrency: Production AI applications may have many users, agents, and services accessing data simultaneously. We considered how each solution handles concurrent reads and writes as workloads scale.
• Low latency: We evaluated how quickly each solution can return results for latency-sensitive workloads. This is particularly important when multiple database or retrieval calls occur within a single agent execution loop.
• Unified governance: We considered how each solution manages access and governance as AI applications use data across systems. Centralized governance can reduce the need to maintain separate permissions and policies for every specialized database added to the architecture.
• Analytics integration: We evaluated how easily operational and AI application data can connect with analytics systems for enrichment and downstream analysis. Closer integration can reduce data movement and make current application data more readily available to analytics and AI workflows.

Together, these criteria provide a broader view of each database's production fit beyond vector search performance alone.

AI database solutions compared

In the table below, we explore a side-by-side comparison of each solution across the key production criteria we identified above:

Databricks Lakebase

Databricks Lakebase is a serverless Postgres database built for operational and AI applications on the Databricks Data Intelligence Platform. It brings transactional data closer to analytics and AI workloads, reducing the need to maintain separate systems and synchronization pipelines between operational and analytical data.

Best use case: Lakebase is best suited to teams that need transactional Postgres alongside AI search, analytics, and machine learning. It is particularly useful when agents need to retrieve context and continuously read or update operational data within the same broader data environment.

Key features

• Serverless and elastic architecture: Lakebase removes the need to provision database infrastructure and separates compute from storage. Compute can scale with workload demand, making it suitable for applications with variable or unpredictable traffic.
• Postgres compatibility: Lakebase supports familiar PostgreSQL syntax, drivers, ORMs, and tooling, allowing you to build AI applications without adopting a new database interface.
• AI search: Lakebase supports vector, full-text, and hybrid search alongside relational queries. Its vector search capabilities are compatible with pgvector, making it possible to combine semantic retrieval with structured and transactional application data.
• Databricks integration and governance: Lakebase connects operational data with the broader Databricks environment, allowing application data to participate in analytics and AI workflows while using Databricks governance capabilities.
• Write performance: Lakebase delivers up to 5x higher write throughput, 2x lower p99 read tail latency, and 94% less Write-Ahead Log (WAL) traffic compared with the previous Lakebase architecture. Its stateless compute layer streams WAL records to distributed storage, supporting applications where agents and users frequently update operational state.
• High concurrency and low latency: Lakebase separates compute from storage so compute resources can respond to workload demand. This architecture is designed for concurrent transactional workloads and low-latency access required by production applications.

Drawbacks

• Lakebase's strongest integration benefits apply to teams already using the Databricks ecosystem.
• Dedicated vector databases may be a simpler choice for workloads focused exclusively on large-scale vector retrieval.

Pinecone

Pinecone is a fully managed vector database built for storing, indexing, and retrieving vector embeddings at scale. It is designed for AI retrieval workloads such as semantic search and RAG, with serverless infrastructure that removes the need to provision or manage database clusters.

Best use case: Pinecone is best suited to teams that need a managed, purpose-built vector database for large-scale semantic search and RAG.

Key features

• Vector and hybrid search: Pinecone supports dense vector search as well as hybrid retrieval that combines semantic and lexical search. Metadata filtering can further narrow results using attributes associated with stored records.
• Serverless and elastic architecture: Pinecone manages the underlying infrastructure and separates storage from query execution. Resources can scale with demand, making it suitable for applications with variable retrieval workloads.
• High concurrency and low latency: Pinecone is designed for high-throughput vector retrieval and provides options for scaling read capacity for workloads that require more predictable performance.

Drawbacks

• Relational and transactional application data typically requires a separate database.
• Does not provide PostgreSQL compatibility or allow teams to reuse existing SQL workflows.
• Introducing a separate vector database can require additional data synchronization, governance, and integration with analytics systems.

Weaviate

Weaviate is an open-source vector database designed for semantic and hybrid search. It stores objects alongside their vector embeddings and supports metadata filtering, allowing applications to combine semantic retrieval with structured conditions. Teams can use Weaviate as a managed cloud service or deploy it within their own infrastructure.

Best use case: Weaviate is best suited to teams building RAG, semantic search, and knowledge retrieval applications that need flexible search capabilities and a choice between managed and self-hosted deployment.

Key features

• Vector and hybrid search: Weaviate combines vector search with BM25 keyword search and supports metadata filtering, allowing applications to balance semantic similarity with exact-term retrieval.
• Managed and self-hosted deployment: Weaviate Cloud provides a managed deployment option, while open-source deployments give teams more control over their infrastructure and configuration.
• High concurrency and low latency: Weaviate supports distributed vector workloads through horizontal scaling and sharding. Retrieval performance depends on factors such as dataset size, index configuration, infrastructure, and query complexity.
• Integrated vectorization: Weaviate can connect with embedding providers to generate vector representations during data ingestion, reducing the need to maintain a separate vectorization pipeline.

Drawbacks

• Does not provide PostgreSQL compatibility, so existing PostgreSQL workflows and tooling do not transfer directly.
• Self-hosted deployments can introduce additional infrastructure and operational responsibilities.
• Applications that require complex relational queries or transactional workloads may still need a separate operational database.
• Using Weaviate alongside operational and analytical databases can introduce additional data synchronization, governance, and integration requirements.

PostgreSQL + pgvector

pgvector is an open-source PostgreSQL extension that adds vector similarity search to Postgres. It allows you to store and query vector embeddings alongside existing relational and transactional data using the same database and SQL workflows.

Best use case: PostgreSQL + pgvector is best suited to teams already using Postgres that want to add semantic search or RAG without introducing a separate vector database.

Key features

• Vector search: pgvector supports exact and approximate nearest-neighbor search, including HNSW and IVFFlat indexes. Vector queries can also be combined with PostgreSQL filtering and other SQL operations.
• Postgres-native: Because pgvector runs as a PostgreSQL extension, you can continue using familiar SQL, transactions, drivers, ORMs, and other PostgreSQL tooling while adding vector retrieval.
• Deployment flexibility: PostgreSQL + pgvector can run wherever your PostgreSQL environment supports the extension. Serverless and elastic scaling capabilities depend on the database provider and deployment model you choose.
• Relational and vector data: Embeddings can live alongside application data in PostgreSQL, allowing semantic retrieval to be combined with structured queries and transactional operations.

Cons

• Vector search and transactional workloads share database resources, which can create contention as either workload grows.
• Scaling large vector workloads may require additional indexing, partitioning, and infrastructure tuning.
• Serverless scaling, elasticity, governance, and analytics integration depend largely on the PostgreSQL platform and surrounding infrastructure rather than pgvector itself.

Milvus

Milvus is an open-source vector database designed for large-scale similarity search. Its distributed architecture separates storage, query, indexing, and data-processing components, allowing teams to scale different parts of a vector retrieval workload independently.

Best use case: Milvus is best suited to teams running large-scale vector search or RAG workloads that need distributed scaling and greater control over their deployment.

Key features

• Vector search at scale: Milvus is purpose-built for similarity search across large collections of vector embeddings and supports multiple approximate nearest-neighbor indexing strategies for different performance and resource requirements.
• Distributed architecture: Query, indexing, storage, and data services can scale independently, allowing teams to allocate resources based on the demands of their retrieval workload.
• Managed and self-hosted deployment: Milvus can be deployed within your own infrastructure, while Zilliz Cloud provides managed and serverless options for teams that do not want to operate the underlying clusters themselves.
• High concurrency and low latency: Its distributed architecture is designed to support high-throughput vector search across concurrent workloads. Actual latency depends on factors such as index type, dataset size, hardware, filtering, and deployment configuration.
• GPU acceleration: Milvus supports GPU-accelerated indexing and search, giving teams another option for improving performance on computationally demanding vector workloads.

Drawbacks

• Self-hosted distributed deployments can introduce significant infrastructure and operational complexity.
• Milvus uses its own architecture and APIs rather than PostgreSQL, so existing Postgres workflows and tooling do not transfer directly.
• Applications that also require relational transactions may need a separate operational database.
• Running Milvus alongside operational and analytical systems can introduce additional data synchronization, governance, and integration requirements.

Qdrant

Qdrant is an open-source vector database written in Rust and designed for vector similarity search with structured metadata filtering. It supports semantic and hybrid retrieval for AI applications and can be deployed through Qdrant Cloud or within your own infrastructure.

Best use case: Qdrant is best suited to teams building RAG and semantic search applications that need flexible vector retrieval, advanced metadata filtering, and a choice between managed and self-hosted deployment.

Key features

• Vector search and filtering: Qdrant combines vector similarity search with filtering over structured payload data, allowing applications to narrow semantic results using metadata and other conditions.
• Hybrid and multi-stage retrieval: Qdrant supports retrieval strategies that combine dense and sparse vectors, multiple queries, and reranking approaches. This gives teams flexibility when building retrieval pipelines that need more than basic vector similarity.
• Managed and self-hosted deployment: Qdrant Cloud provides a managed option, while open-source deployments allow teams to run the database within their own infrastructure.
• High concurrency and low latency: Qdrant is designed for high-throughput vector retrieval and supports distributed scaling through sharding and replication. Actual performance depends on dataset size, index configuration, filtering, hardware, and deployment architecture.
• API-based integration: Qdrant provides REST and gRPC interfaces along with client libraries for integrating vector retrieval into AI applications.

Drawbacks

• Does not provide PostgreSQL compatibility, so existing SQL workflows and Postgres tooling do not transfer directly.
• Applications that require relational transactions will generally need a separate operational database.
• Self-hosted distributed deployments require additional infrastructure management and tuning.

Redis Vector

Redis Vector adds vector search to Redis, allowing applications to store and retrieve embeddings alongside frequently accessed operational data. It is designed for latency-sensitive AI workloads where vector retrieval needs to happen close to application state, cached data, or other data already stored in Redis.

Best use case: Redis Vector is best suited to teams already using Redis that want to add semantic search, RAG, or recommendation capabilities without introducing a separate vector store.

Key features

• Vector search and filtering: Redis supports vector similarity search alongside filtering over other indexed fields, allowing applications to combine semantic retrieval with structured conditions.
• Multiple vector indexes: Redis supports HNSW for approximate nearest-neighbor search and FLAT for exact vector search, giving teams different trade-offs between retrieval speed, resource usage, and recall.
• Managed and self-managed deployment: Redis Cloud provides managed infrastructure, while Redis can also be deployed and operated within your own environment.
• High concurrency and low latency: Redis is designed for high-throughput, low-latency data access, making vector search suitable for applications where retrieval occurs within latency-sensitive request paths.
• Operational data integration: Vector embeddings can live alongside other Redis data structures, allowing AI retrieval to remain close to frequently changing application data.

Drawbacks

• Does not provide PostgreSQL compatibility, so teams cannot directly reuse PostgreSQL's SQL interface and tooling.
• Applications that require complex relational queries or transactional relationships may still need a separate operational database.
• Teams not already using Redis introduce another data model and operational system into their architecture.
• Using Redis alongside separate transactional and analytical systems can add synchronization, governance, and integration requirements.

Actian VectorAI DB

Actian VectorAI DB is a database designed to combine relational data with vector search and AI-oriented analytics. Unlike vector databases focused primarily on cloud retrieval, it supports deployment across cloud, on-premises, local, and edge environments, allowing applications to perform vector search closer to where their data is generated.

Best use case: Actian VectorAI DB is best suited to applications that need vector retrieval alongside structured business data, particularly when deployment flexibility is important. Its support for local and edge environments also makes it relevant for industrial, manufacturing, and other applications that need to perform AI retrieval closer to devices or data sources.

Key features

• Relational and vector search: Actian VectorAI DB supports relational queries and vector similarity search within the same database, allowing applications to work with structured data and embeddings without maintaining separate stores for each.
• Flexible deployment: The database can run across cloud, on-premises, local, and edge environments, giving teams more control over where AI data and retrieval workloads operate.
• Edge support: Its deployment model extends vector retrieval to resource-constrained and embedded environments, making it suitable for applications that cannot rely exclusively on centralized cloud infrastructure.
• Vector indexing: Actian VectorAI DB supports approximate nearest-neighbor search and indexing techniques such as HNSW for similarity retrieval.
• SQL interface: The database provides SQL-based access to relational and vector data, although it does not provide PostgreSQL compatibility.

Drawbacks

• Does not provide PostgreSQL compatibility, so existing Postgres tooling, ORMs, extensions, and workflows may require changes.
• Has a smaller ecosystem and track record in vector database workloads than more established options in this comparison.
• Teams primarily looking for a managed cloud vector service may find specialized platforms simpler for that use case.

MongoDB Atlas Vector Search

MongoDB Atlas Vector Search adds vector retrieval to MongoDB Atlas, allowing applications to store embeddings alongside existing document data. This lets teams add semantic and hybrid search to applications already built on MongoDB without introducing a separate vector database.

Best use case: MongoDB Atlas Vector Search is best suited to teams already using MongoDB Atlas that want to add semantic search, RAG, or recommendation capabilities while keeping embeddings and application data within the same database.

Key features

• Vector and hybrid search: Atlas Vector Search supports semantic retrieval and can combine vector search with other search and filtering capabilities, allowing applications to retrieve results using both meaning and structured document data.
• Managed infrastructure: Because Vector Search is integrated with MongoDB Atlas, teams can use managed database infrastructure rather than deploying and operating a separate vector search system.
• Document-native storage: Embeddings can be stored alongside the application data and metadata in MongoDB documents, keeping the information used for retrieval close to its source data.
• High concurrency and low latency: Atlas is designed for production application workloads and can scale across distributed deployments. Vector search performance depends on factors such as dataset size, index configuration, filtering, workload, and deployment configuration.
• MongoDB integration: Teams already using MongoDB can add vector retrieval without adopting a separate database architecture or maintaining an additional vector store.

Drawbacks

• Its strongest architectural benefits apply to teams already using MongoDB Atlas.
• Does not provide PostgreSQL compatibility, so PostgreSQL tooling and SQL workflows do not transfer directly.
• Teams that only need specialized vector retrieval may prefer a purpose-built vector database with greater retrieval-specific flexibility.
• Organizations using separate analytics or governance platforms may still need additional integration between MongoDB and those systems.

Chroma

Chroma is an open-source database designed for storing and retrieving data used by AI applications. It supports vector, full-text, and metadata search and is designed around a developer-friendly interface for building semantic search and RAG applications. Teams can run Chroma locally or use Chroma Cloud for managed infrastructure.

Best use case: Chroma is best suited to developers and teams that want a straightforward way to add vector retrieval to AI applications without managing a more complex distributed vector database. It is particularly useful for semantic search and RAG applications where ease of development and integration are priorities.

Key features

• Vector, full-text, and metadata search: Chroma supports multiple retrieval methods within the same system, allowing applications to combine semantic similarity with keyword search and metadata filtering.
• Managed and local deployment: Chroma Cloud provides managed infrastructure, while Chroma can also run locally for development or be deployed within your own environment.
• Developer-focused API: Chroma provides an accessible interface for storing embeddings, attaching metadata, and querying AI application data without requiring developers to manage lower-level vector infrastructure.
• AI framework integrations: Chroma integrates with popular AI development frameworks such as LangChain and LlamaIndex, making it easier to incorporate retrieval into existing RAG workflows.
• Production retrieval: Chroma supports production AI applications, but teams with demanding concurrency, scale, or latency requirements should benchmark it against their specific workloads and deployment configuration.

Drawbacks

• Does not provide PostgreSQL compatibility, so existing PostgreSQL workflows and tooling do not transfer directly.
• Applications requiring relational transactions will generally need a separate operational database.
• Teams with highly specialized distributed scaling or performance requirements may prefer vector databases that expose greater infrastructure and indexing control.
• Using Chroma alongside operational and analytical databases can introduce additional synchronization, governance, and integration requirements.

How to choose the right AI database solution

The right AI database depends on the role it needs to play within your application and existing data architecture. Use the following questions to narrow down your options.

1. What kind of search does your application need?

Start with the retrieval patterns your application actually uses. If the workload is primarily semantic similarity search over embeddings, a dedicated vector database such as Pinecone, Qdrant, Milvus, or Weaviate may be a natural fit.

If your application also needs keyword search, metadata filtering, relational queries, or transactional operations, consider how many separate systems would be required to support those patterns. Solutions such as PostgreSQL + pgvector and Lakebase allow vector retrieval to operate alongside relational data, while other databases offer different combinations of vector, full-text, and hybrid search.

2. Do you need Postgres compatibility?

If your application already runs on PostgreSQL, pgvector provides a straightforward way to add vector search while preserving your existing SQL queries, drivers, ORMs, and operational workflows.

Lakebase is a stronger option when you need serverless Postgres for operational AI workloads alongside the broader Databricks environment.

3. How much does your workload fluctuate?

Consider both your normal traffic and how dramatically demand changes over time. Applications with unpredictable traffic, intermittent agent activity, or sudden usage spikes can benefit from infrastructure that automatically scales rather than requiring capacity to be provisioned for peak demand.

4. What are your concurrency and latency requirements?

Consider how many users, agents, services, and background processes may access the database simultaneously, and how much latency each database operation can add to the application.

These requirements matter more for agentic applications because a single execution may involve multiple retrievals, reads, and writes. Rather than relying on a database's headline latency number, evaluate performance under a workload that resembles your own, including dataset size, index configuration, filtering, concurrent operations, and read/write patterns.

5. Where does your operational data need to go next?

AI application data rarely remains isolated inside the serving database. Agent interactions, application state, retrieval results, and other operational data may eventually be used for analytics, model development, monitoring, or data enrichment.

Consider how easily each database connects to those downstream systems. Adding a specialized vector database can be worthwhile when retrieval is the primary requirement, but it may also introduce another data pipeline if the same information needs to move between operational, retrieval, and analytical systems.

6. How will you govern data across the architecture?

Every additional database can introduce another set of permissions, access policies, credentials, and governance workflows. Consider whether you can manage governance alongside the rest of your data environment, or whether adopting a specialized database creates an additional security and governance boundary.

Wrapping up

Choosing an AI database depends on your architecture. Dedicated vector databases such as Pinecone, Weaviate, Milvus, and Qdrant are well suited to large-scale vector retrieval, while pgvector is a straightforward option for adding vector search to an existing PostgreSQL database.

For applications that also need transactional Postgres, elastic infrastructure, AI search, and analytics integration, maintaining separate systems can add synchronization and governance overhead. Databricks Lakebase brings these workloads together, making it a strong choice for teams building production AI applications on Databricks.

Frequently asked questions

Which AI database is best?

The best AI database depends on your workload and existing data stack. Dedicated vector databases such as Pinecone, Qdrant, Milvus, and Weaviate are strong options when specialized vector retrieval is the primary requirement. PostgreSQL with pgvector is a natural fit for teams that want to add vector search to an existing Postgres database. For teams using Databricks that need transactional Postgres alongside AI search, analytics, and governed enterprise data, Lakebase provides a more integrated option.

What are the most popular AI databases?

Popular AI database options include dedicated vector databases such as Pinecone, Qdrant, Milvus, Weaviate, and Chroma, as well as established databases that have added vector retrieval capabilities, including PostgreSQL with pgvector, MongoDB Atlas Vector Search, and Redis Vector. Databricks Lakebase provides another approach by combining serverless Postgres with search capabilities and integration with the broader Databricks platform.

The right choice depends less on popularity than on whether you need specialized vector retrieval, relational transactions, compatibility with an existing database ecosystem, or closer integration with analytics and AI workloads.

Can AI manage a database?

AI can assist with database administration tasks such as query optimization, performance analysis, anomaly detection, capacity planning, and troubleshooting. Some managed database platforms also automate infrastructure tasks such as scaling, backups, maintenance, and resource management.

However, these capabilities vary by platform and should not be confused with a database autonomously managing every aspect of production operations. Teams still need appropriate monitoring, access controls, reliability practices, and oversight for production systems.

What should you look for in an AI database?

Evaluate how well the database supports the search patterns and operational requirements of your application. Important considerations include vector, full-text, and hybrid search capabilities; serverless infrastructure and elasticity; compatibility with your existing database stack; concurrency and latency requirements; governance; and integration with analytics and AI systems.

For applications that also maintain operational state, consider whether keeping transactional data and AI retrieval together could reduce the synchronization and infrastructure required by a separate vector database.