Best Vector Database Software - Page 2

SingleStore

SingleStore enables organizations to scale from one to one million customers, handling SQL, JSON, full text and vector workloads — all in one unified platform.

Average Rating: 4.5/5.0

Total Reviews: 114

Tembo

Tembo is a multi-workload Postgres managed service that enables organizations to harness the full power of Postgres for transactional, analytical, and AI workloads. With robust SaaS and self hosted deployment options, Tembo enables everyone – from the smallest startups to the Fortune 500 – to go “all in” on Postgres, achieving unprecedented stability and efficiency across a variety of applications and use cases. With Tembo, customers get all the stability, reliability, and extensibility of Postgres open source with enhanced observability, compliance, and developer experience.

Average Rating: 4.7/5.0

Total Reviews: 26

Meilisearch

Meilisearch empowers developers and business teams to create the most intuitive search experience that increases search-based conversions

Average Rating: 4.8/5.0

Total Reviews: 4

MyScale

MyScale is a powerful SQL vector database that offers minimal learning curve, maximum value, and a cost-effective solution for organizations seeking optimal performance and efficiency in their data management strategies. It enables every developer to build production-grade GenAI applications with powerful and familiar SQL.

Average Rating: 4.3/5.0

Total Reviews: 2

SvectorDB

Vector database built from the ground up for serverless. The only vector database with built-in native CloudFormation / CDK support

Average Rating: 4.8/5.0

Total Reviews: 2

Typesense

Typesense is a modern, privacy-friendly, open source search engine (with a hosted SaaS option) meticulously engineered for performance & ease-of-use. It uses cutting-edge search algorithms that take advantage of the latest advances in Hardware Capabilities & AI / Machine Learning. We serve 1.6+ Billion Searches per month, across 1K+ customers around the world, just on Typesense Cloud, and several Billions more in self-hosted clusters every month. Typesense reduces the time-to-market for developers to build a blazing-fast search experience that provides relevant results out-of-the-box, all without breaking the bank and without operational overhead.

Average Rating: 4.7/5.0

Total Reviews: 5

Vald

Vald is designed and implemented based on the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT to search neighbors. Vald has automatic vector indexing and index backup, and horizontal scaling which made for searching from billions of feature vector data. Vald is easy to use, feature-rich and highly customizable as you needed.

Average Rating: 5.0/5.0

Total Reviews: 2

ApertureDB

ApertureDB is a vector + graph database purpose-built to streamline the development and scaling of multimodal AI and analytics applications. Designed for modern AI and analytics workflows, it combines multimodal data management, vector search capabilities and knowledge graph into a single integrated solution. With ApertureDB developers and organizations get 2-10X faster vector search performance than the competition, save 6 to 9 months on average in infrastructure setup time and improve machine learning teams productivity by 10X. It powers use cases like semantic search, RAG chatbots, Generative AI applications and AI-driven agents. ApertureDB seamlessly integrates across your AI stack including popular large scale Language Models (LLMS), AI and machine learning frameworks and workflows. Its robust multi-tenant architecture, designed to handle complex multimodal data text, images, videos, embeddings, metadata and easily scales for large-scale deployments while maintaining enterprise-grade performance and reliability. ApertureDB offers flexible deployment options and optimized pricing performance. Available in the cloud, on-premises or hybrid, ApertureDB meets the needs of diverse organizations, from startups to large enterprises. Our optimized pricing empowers teams to choose a deployment model that aligns with their budget and can scale effortlessly without breaking the bank.

Average Rating: 5.0/5.0

Total Reviews: 1

CockroachDB

Overview Cockroach Labs is the creator of CockroachDB, the cloud-native, resilient, distributed SQL database enterprises worldwide trust to run mission-critical AI and other applications that scale fast, avert and survive disaster, and thrive everywhere. It runs on the Big 3 clouds, on prem, and in hybrid configurations powering Fortune 500, Forbes Global 2000, and Inc. 5000 brands, and game-changing innovators, including OpenAI, CoreWeave, Adobe, NETFLIX, Booking.com, DoorDash, FANDUEL, Cisco Systems, P&G, UiPath, FORTINET, Roblox, EA, BestBuy, SpaceX, NVIDIA, The VA, Squarespace, The Home Depot, and Hewlett Packard Enterprise. Cockroach Labs has customers in 40+ countries across all world regions, 25+ verticals, and 50+ Use Cases. Cockroach Labs operates its own ISV Partner Ecosystem powering Payments, Identity Management (IDM/IAM), Banking & Wallet, Trading, and other high-demand use cases. Cockroach Labs is an AWS Partner of the Year finalist and has achieved AWS Competency Partner certifications in Data & Analytics and Financial Services (FSI). CockroachDB pricing is available at https://www.cockroachlabs.com/pricing/ Vector, RAG, and GenAI Workloads CockroachDB includes native support for the VECTOR data type and pgvector API compatibility, enabling storage and retrieval of high-dimensional embeddings. These vector capabilities are critical for Retrieval-Augmented Generation (RAG) pipelines and GenAI workloads that rely on similarity search and contextual embeddings. By supporting distributed vector indexing within the database itself, CockroachDB removes the need for external vector stores and allows AI applications to operate against a single, consistent data layer. C-SPANN Distributed Indexing At the core of CockroachDB’s vector search capabilities is the C-SPANN indexing engine. C-SPANN provides scalable approximate nearest neighbor (ANN) search across billions of vectors while supporting incremental updates, real-time writes, and partitioned indexing. This ensures low-latency retrieval in the tens of milliseconds, even under high query throughput. The algorithm eliminates central coordinators, avoids large in-memory structures, and leverages CockroachDB’s sharding and replication to deliver scale, resilience, and global consistency. Machine Learning and Apache Spark Integration CockroachDB integrates with modern ML workflows by supporting embeddings generated through frameworks such as AWS Bedrock and Google Vertex AI. Its compatibility with the PostgreSQL JDBC driver allows seamless integration with Apache Spark, enabling distributed processing and advanced analytics on CockroachDB data. PostgreSQL Compatibility and JSON Support CockroachDB speaks the PostgreSQL wire protocol, so applications, drivers, and tools designed to work with Postgres can connect to CockroachDB without modification, enabling seamless use of familiar SQL features and integration with the wider Postgres ecosystem. This includes support for advanced data types such as JSON and JSONB, which allow developers to store and query semi-structured data natively. Geospatial and Graph Capabilities CockroachDB also provides first-class geospatial data support, allowing developers to store, query, and analyze spatial data directly in SQL. For graph workloads, CockroachDB employs JSON flexibility to represent relationships and delivers query capabilities for graph-like traversals. This combination enables hybrid applications that merge relational, geospatial, document, and graph data within a single platform. Analytics, BI, and Integration To support high-performance analytics and BI, CockroachDB supports core analytical use cases and functions including Enterprise Data Warehouse, Lakehouse, and Event Analytics, and offers materialized views for precomputing complex joins and aggregations. Its PostgreSQL wire compatibility ensures direct connectivity with all relevant BI and analytics apps and tools including Amazon Redshift, Snowflake, Kafka, Google BigQuery, Salesforce Tableau, Databricks, Cognos, Looker, Grafana, Power BI, Qlik Sense, SAP, SAS, Sisense, and TIBCO Spotfire. Data scientists can interact with CockroachDB through Jupyter Notebooks, querying structured and semi-structured data and loading results for analysis. Change data capture (CDC) streams provide real-time updates to analytics pipelines and feature stores, keeping downstream systems fresh and reliable. Columnar vectorized execution accelerates query processing, optimizes transactional throughput, and minimizes latency for demanding distributed workloads. MOLT AI-Powered Migration Organizations often know their data infrastructure is not supporting the business, but find it too painful to change. CockroachDB’s MOLT (Migrate Off Legacy Technology) is designed to enable safe, minimal-downtime database migrations from legacy systems to CockroachDB. MOLT Fetch supports data migration from PostgreSQL, MySQL, SQL Server, and Oracle, with SQL Server and DB2 coming soon. CockroachDB also has a portfolio of data replication platform integrations including Precisely, Striim, Qlik, Confluent, IBM, etc. Together, these capabilities ensure that CockroachDB supports both operational and analytical workloads, bridging traditional SQL applications with emerging Gen AI and ML use cases.

Average Rating: 4.3/5.0

Total Reviews: 27

Endee

Endee is an open-source, cloud-native vector database built for production AI workloads, delivering high-performance vector search with 10x less memory than traditional vector databases like Pinecone, Weaviate, or Qdrant. Designed for developers and enterprises alike, Endee powers the AI use cases that matter most Retrieval-Augmented Generation (RAG), semantic search, agentic AI pipelines, and AI-powered recommendation systems all at scale, without the infrastructure headache. Unlike legacy vector search engines, Endee is built from the ground up to be fast, scalable, and secure without compromising on developer experience. Its architecture handles billions of vector embeddings with low latency and high throughput, making it the right foundation for real-world AI applications, LLM integrations, and similarity search in production. Whether you're a startup shipping your first AI feature or an enterprise running mission-critical machine learning workloads, Endee gives you the performance edge and flexibility to grow open-source at its core, enterprise-ready when you need it.

Average Rating: 5.0/5.0

Total Reviews: 1

Featureform Embedding Hub

Experience a comprehensive database designed to provide embedding functionality that, until now, required multiple platforms. Elevate your machine learning quickly and painlessly through Embeddinghub.

Average Rating: 4.5/5.0

Total Reviews: 1

Tiger Data

Tiger Data, from the creators of TimescaleDB, is the #1 Postgres time-series database for developers, devices and agents. Keep sensor, on-chain, and customer data fresh while retaining years of history, all queryable in standard SQL. For IoT, Web3, and AI. Why teams choose Tiger Data: - Trusted by thousands of developers. 3M+ active databases, 2k+ customers - Up to 95% Compression. Keep years of history online at a fraction of the cost. - Production-ready without the operational pain. Multi-AZ HA, PITR, cross-region backups, SOC 2/HIPAA/GDPR, deep observability. - Scale effortlessly. Disaggregated compute & storage. Never pay for idle capacity. - Unified data architecture. Connect any data source and automatically sync it between your operational database, and data lake. - Hyperscaler procurement. Available on AWS Marketplace and Azure Marketplace. Key capabilities: - Automatic partitioning Ingest millions of data points per second without manual table management or sharding. - Incremental materialized views Precompute and cache rollups for instant dashboards and APIs. - Hybrid row/column storage Fast writes, compressed reads, optimized for real-time and historical queries. - Compression (up to 95%) Columnar encodings apply filters & aggregates directly on compressed data for faster queries and big savings. - Tiered Storage Automatically move older or less-frequently accessed data to low-cost object storage while keeping it fully queryable through the same SQL interface. - Fully managed Postgres Cloud Scale compute and storage independently, tier S3 storage to manage costs, deploy globally, and skip database ops. Industry verticals: Developers and platform teams in Industrial IoT, manufacturing, Crypto, SaaS/ML and DevOps tooling rely on Tiger to combine operational and historical data for real-time dashboards and mission-critical insights, queryable in standard-SQL. How to get started: Try Tiger Cloud for 1-month free with no credit card needed, or use us indefinitely as part of our free plan. Get started now - https://console.cloud.timescale.com/signup?utm_source=g2&utm_medium=referral&utm_campaign=free-trial-g2

Average Rating: 4.6/5.0

Total Reviews: 32

Essofore Semantic Search

Essofore is a document store powered by a semantic search engine that understands the meaning of your query rather than searching for keywords in your query. You can use it to develop enterprise search or RAG applications.

Graphium Labs

Graphium Labs offers HyperGraph, a robust data and compute platform engineered to scale seamlessly from startup environments to mega-hyper-scale operations. Available as both an on-premise solution and a Platform-as-a-Service (PaaS), HyperGraph provides flexibility in deployment to meet diverse organizational needs.

Hazelcast Platform

Hazelcast Platform is the Live Data Platform that delivers data at the speed of relevance, providing the in‑memory foundation for applications that act on data the instant it's created—ensuring businesses never miss a moment of opportunity. By converging distributed caching, compute, stream processing, and real‑time AI into one low‑latency runtime, Hazelcast delivers sub‑millisecond performance, linear scalability, and enterprise resilience. Global 2000 firms trust Hazelcast to simplify architectures, reduce costs, and power mission‑critical, time‑sensitive applications.

Average Rating: 4.3/5.0

Total Reviews: 12