Milvus vs Vespa: Vector Databases Comparison
Compare Milvus and Vespa 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 Vespa if scales to billions of data items with sub-100ms query latencies.
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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 | vespa.ai |
| Key Features |
| — |
| Use Cases |
| — |
Pros & Cons: Milvus vs Vespa
- +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
- +Scales to billions of data items with sub-100ms query latencies
- +True hybrid search combining vector, text, and structured filters in single query
- +Native tensor support for advanced machine learning ranking
- +Real-time updates and inference at massive scale
- +Open-source foundation with enterprise support
- −Steeper learning curve compared to simpler vector databases
- −Requires more infrastructure expertise to deploy and optimize
- −Pricing not transparently published
- −May be over-engineered for simple use cases
Pricing: Milvus vs Vespa
- · 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 Vespa
- 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 Vespa
Vespa is an AI-powered search platform for developing and operating large-scale applications that combine big data, vector search, machine-learned ranking, and real-time inference. Originally developed at Yahoo and spun out as an independent company in 2017, Vespa enables real-time AI applications like RAG, recommendation, and intelligent search at enterprise scale. The platform features native tensor support for complex ranking and decisioning, with capabilities including vector and tensor search with any number of vector fields, true positional text indexes with detailed text match features, and hybrid search combining structured filters, full-text retrieval, and vector similarity in a single query. Vespa can scale to billions of constantly changing data items, handling thousands of queries per second with latencies below 100 milliseconds. Based in Trondheim, Norway, Vespa raised $31M in Series A funding in November 2023.
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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