Milvus vs Turbopuffer: Vector Databases Comparison
Compare Milvus and Turbopuffer 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 Turbopuffer if up to 100x cost reduction compared to traditional vector databases.
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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 | turbopuffer.com |
| Key Features |
| — |
| Use Cases |
| — |
Pros & Cons: Milvus vs Turbopuffer
- +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
- +Up to 100x cost reduction compared to traditional vector databases
- +Sub-10ms p50 latency with serverless auto-scaling
- +No namespace limits unlike competitors
- +Enterprise compliance (HIPAA, SOC 2, CMEK) on standard plan
- +Trusted by major companies: Notion, Cursor, Linear, PlayerZero
- −$64/month minimum spend makes it less suitable for small experiments
- −Relatively new platform with smaller community
- −Limited documentation compared to more established solutions
- −Object storage architecture may have latency trade-offs for some use cases
Pricing: Milvus vs Turbopuffer
- · 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 Turbopuffer
- 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 Turbopuffer
Turbopuffer is a serverless vector and full-text search database trusted by leading companies including Notion, Cursor, Linear, and PlayerZero. Founded in 2023 by ex-Shopify engineers Simon Eskildsen and team, Turbopuffer reached $1 million in ARR within a year of launch and now operates profitably with only 22 employees while powering billions of vectors. The platform features serverless architecture with automatic scaling, sub-10ms p50 latency, support for billions of vectors, full-text search, hybrid search, and metadata filtering. TurboPuffer achieves up to 100x cost reduction compared to traditional vector databases by storing data on object storage like S3 at $0.02 per GB instead of in-memory at $2+ per GB. Turbopuffer has no enforced namespace limits and includes enterprise-grade compliance features like HIPAA BAA, SOC 2, and CMEK even on the non-enterprise plan. Query prices have been reduced by up to 94%, making it 10x-100x cheaper than alternatives with usage-based pricing.
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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