SingleStore vs TigerGraph: Vector Databases Comparison
Compare SingleStore and TigerGraph side by side. Both are tools in the Vector Databases category.
The short answer
Choose SingleStore if unified platform combining transactional, analytical, and vector workloads.
Choose TigerGraph if industry-first distributed native graph database with vector capabilities.
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Quick Comparison
| Category | Vector Databases | Vector Databases |
| Website | singlestore.com | tigergraph.com |
Pros & Cons: SingleStore vs TigerGraph
- +Unified platform combining transactional, analytical, and vector workloads
- +Co-located vector data with relational data for powerful SQL queries
- +Multiple vector index types (FLAT, IVF_FLAT, IVF_PQ, HNSW_FLAT, HNSW_PQ)
- +MySQL and MongoDB wire protocol compatibility for easy integration
- +Proven at scale with 100+ Fortune 500 customers
- −More complex than dedicated vector databases for pure vector search use cases
- −Pricing not transparently published
- −May have higher learning curve for teams unfamiliar with distributed SQL
- −Recent acquisition may lead to product changes
- +Industry-first distributed native graph database with vector capabilities
- +Powerful for interconnected data analytics and machine learning
- +Flexible pricing with granular measurement (1-minute for compute)
- +Comprehensive use cases: fraud detection, recommendations, cybersecurity, knowledge graphs
- +Graph + Vector DB on single server simplifies architecture
- −Steeper learning curve for teams unfamiliar with graph databases
- −May be over-engineered for simple vector search use cases
- −Smaller community compared to more popular vector databases
- −Pricing details require consultation for accurate estimates
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About SingleStore
SingleStore is a real-time, unified, distributed SQL database that combines transactional, analytical, and vector data workloads in a single platform. MySQL and MongoDB wire protocol-compatible, SingleStore enables organizations to scale from one to one million customers, handling SQL, JSON, full text, and vector workloads all in one unified platform. Unlike traditional vector databases, SingleStore stores vector data in relational tables alongside other types of data, allowing easy querying of extended metadata and other attributes with the full power of SQL. The system supports both semantic search using FLAT, IVF_FLAT, IVF_PQ, IVF_PQFS, HNSW_FLAT, and HNSW_PQ vector indexes, with dot product and Euclidean distance for similarity matching. Founded in 2011 and headquartered in San Francisco, SingleStore serves hundreds of customers including 100+ Fortune 500, Forbes Global 2000, and Inc. 5000 brands. The company was acquired by Vector Capital in September 2025.
About TigerGraph
TigerGraph provides a platform specially designed for advanced analytics and machine learning on interconnected data, powered by the industry's first distributed native graph database. Founded in 2012 and headquartered in Redwood City, California, TigerGraph combines graph database capabilities with vector search functionality in a single server, offering built-in roles, multiple query languages & APIs, and UI tools. Key applications include fraud detection, anti-money laundering, entity resolution, customer profiling, recommendation systems, knowledge graph formulation, cybersecurity, supply chain management, IoT analytics, and network analysis. TigerGraph Savanna offers a flexible and transparent pricing model designed to accommodate a wide range of usage scenarios, from small-scale projects to large enterprise deployments. Pricing is based on virtual machine instances and storage capacity consumed, with storage, compute, and add-ons charged with granular measurement. Having raised $205M in funding, TigerGraph serves enterprises requiring advanced graph analytics combined with vector search capabilities.
What is Vector Databases?
Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.
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