Elasticsearch vs TigerGraph: Vector Databases Comparison
Compare Elasticsearch and TigerGraph side by side. Both are tools in the Vector Databases category.
The short answer
Choose Elasticsearch if most widely deployed open-source vector database with massive community.
Choose TigerGraph if industry-first distributed native graph database with vector capabilities.
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Quick Comparison
| Category | Vector Databases | Vector Databases |
| Website | elastic.co | tigergraph.com |
Pros & Cons: Elasticsearch vs TigerGraph
- +Most widely deployed open-source vector database with massive community
- +Mature ELK stack ecosystem with extensive integrations and tools
- +DiskBBQ algorithm enables efficient vector search without full in-memory indexes
- +Powerful hybrid search combining keyword, filters, and vector similarity
- +Consumption-based pricing—pay only for what you use
- −Can be complex to configure and optimize for vector search
- −Not purpose-built for vector search—added as a feature
- −Resource-intensive at scale without proper optimization
- −Pricing model complexity with multiple tiers and factors
- +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 Elasticsearch
Elasticsearch is the world's most widely deployed, open-source vector database, operated by Elastic N.V. (NYSE: ESTC). Vector search is integrated into the widely used Elasticsearch search and analytics engine, leveraging the mature ELK stack ecosystem and offering powerful filtering, aggregation, and combined keyword + vector (hybrid) search capabilities. Founded in 2012 in Amsterdam, Elastic provides a platform for enterprise search, observability, and security use cases. Recent innovations include DiskBBQ, a new disk-friendly vector search algorithm that delivers more efficient vector search at scale and eliminates the need to keep entire vector indexes in memory. Elasticsearch's pricing model is consumption-based, charging only for the compute, storage, and data transfer actually used across three deployment tiers: Standard, Platinum, and Enterprise. With over 470 customers using Elastic for AI (including 410+ using it as a vector database), Elasticsearch has proven capabilities at massive scale.
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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