Milvus vs TigerGraph: Vector Databases Comparison
Compare Milvus and TigerGraph side by side. Both are tools in the Vector Databases category.
Updated February 28, 2026
The short answer
Choose Milvus if extreme scalability handling billions of vectors in distributed environments.
Choose TigerGraph if industry-first distributed native graph database with vector capabilities.
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Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | Open Source | — |
| Best For | Organizations that need vector search at billion-scale with high throughput | — |
| Website | milvus.io | tigergraph.com |
| Key Features |
| — |
| Use Cases |
| — |
Pros & Cons: Milvus vs TigerGraph
- +Extreme scalability handling billions of vectors in distributed environments
- +Fully open-source under Apache 2.0 with no vendor lock-in
- +Hybrid search combining dense and sparse vectors in single queries
- +Rich index flexibility with IVF, HNSW, DiskANN and GPU acceleration
- +Managed cloud option reduces DevOps burden with auto-scaling
- −Complex distributed architecture requiring Kubernetes for production
- −Steep learning curve for index tuning and deployment configuration
- −High memory and storage requirements for large datasets
- −No in-place vector updates requiring delete and re-insert workarounds
- +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
Pricing: Milvus vs TigerGraph
- · Apache 2.0 license
- · Self-hosted on Kubernetes
- · Full feature access
- · Community support
- · 5GB storage
- · 2.5M vCUs/month
- · Up to 5 collections
- · Up to 32 query CUs
- · Manual scaling
- · Email/ticket support
- · 99.95% uptime SLA
- · SSO and RBAC
- · 24/7 enterprise support
- · VPC peering
When to Choose Milvus vs TigerGraph
- Billion-vector similarity search
- Large-scale recommendation systems
- Image and video retrieval
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About Milvus
Milvus is an open-source vector database built for scalable similarity search, capable of handling billions of vectors in distributed environments. Created by Zilliz, a company founded in 2017 by Charles Xie (former founding engineer of Oracle 12c cloud database), Milvus has become one of the most widely deployed vector databases with over 30,000 GitHub stars.
The database supports multiple index types including IVF, HNSW, and DiskANN, with GPU-accelerated search and hybrid search combining dense and sparse vectors in a single query. Milvus runs on Kubernetes for production deployments and is governed under the LF AI & Data Foundation. Zilliz Cloud offers a fully managed version with automatic scaling, starting with a free tier and usage-based pricing from $4 per million vector compute units.
Zilliz has raised approximately $113-132 million in funding, with a $60 million Series B extension in August 2022 led by Prosperity7 Ventures (Aramco). The company is headquartered in San Francisco with roughly 140 employees. Zilliz was named "Highest Performer" and "Easiest to Use" in G2's Summer 2025 Vector Database Grid Report.
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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