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The best AI database solutions for 2026 | Databricks Blog Skip to main content Teams running Databricks who need transactional Postgres alongside their analytics and AI stack will find Lakebase the strongest fit. Teams already on Postgres without a Databricks dependency can extend their stack with pgvector. For purely semantic retrieval workloads with no relational data, a dedicated managed vector database is the right starting point. For teams that need maximum control at scale, fully managed options like Pinecone reduce infrastructure overhead, while self-hosted platforms like Pinecone, Qdrant, or Milvus offer greater flexibility. 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: SolutionAI searchServerless & elasticPostgres compatibleHigh concurrencyLow latencyData & governance integration Databricks LakebaseVector, full-text, and hybrid searchServerless with elastic computeFullDesigned for concurrent OLTP workloadsDesigned for low-latency operational workloadsNative integration with Databricks analytics, AI, and governance PineconeVector and hybrid searchFully managed and serverlessNoneDesigned for high-QPS vector workloadsOptimized for low-latency vector retrievalRequires integration with external data and governance systems WeaviateVector, keyword, and hybrid searchManaged cloud or self-hostedNoneSupports distributed workloads through shardingOptimized for low-latency vector retrievalIntegrates with external data and AI systems PostgreSQL + pgvectorVector search alongside SQLProvider-dependentFullWorkload and configuration-dependentWorkload and index-dependentIntegrates with the existing PostgreSQL ecosystem MilvusVector and hybrid searchManaged through Zilliz Cloud or self-hostedNoneDesigned for distributed vector workloadsOptimized for large-scale vector retrievalRequires integration with external data and governance systems QdrantVector and hybrid searchManaged cloud or self-hostedNoneSupports distributed workloads through shardingOptimized for low-latency vector retrievalRequires integration with external data and governance systems Redis VectorVector search with filteringManaged cloud or self-managedNoneDesigned for high-throughput operational workloadsOptimized for low-latency retrievalIntegrates with the broader Redis ecosystem Actian VectorAI DBVector search alongside relational dataDeployment-dependentNoneDesigned for concurrent enterprise workloadsOptimized for local and edge retrievalSupports cloud, on-premises, and edge data environments MongoDB Atlas Vector SearchVector and hybrid searchManaged through AtlasNoneDesigned for distributed application workloadsWorkload-dependentNative integration with data stored in MongoDB Atlas ChromaVector, full-text, and metadata searchManaged cloud or self-hostedNoneBetter suited to moderate workloadsOptimized for vector retrievalIntegrates with common AI development frameworks 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 [truncated for AI cost control]