Milvus vs Rows
Side-by-side comparison to help you choose the best tool.
Milvus
freemiumMilvus is a cloud-native, open-source vector database built to handle billions of vectors at enterprise scale. Originally developed at Zilliz and donated to the LF AI & Data Foundation, it powers semantic search, recommendation systems, and AI applications at companies like Walmart and Shopee. Milvus supports multiple index types, GPU acceleration, and a distributed architecture - making it the most scalable open-source vector database available.
Rows
freemiumAI spreadsheet that connects to live data sources, runs Python code, and uses AI to analyse and summarise spreadsheet data in plain English. Rows reimagines the traditional spreadsheet by combining familiar grid functionality with live API integrations, Python execution, and a built-in AI analyst that answers questions about your data. Teams can build automated reports that pull live data and have AI generate written summaries and data.
| Feature | Milvus | Rows |
|---|---|---|
| Pricing | freemium | freemium |
| Category | Data & Analytics | Data & Analytics |
| Rating | 4.4 | 4.3 |
| Best For | Enterprise engineering teams building billion-scale vector search systems for recommendation engines, semantic search, and AI applications | Business analysts wanting AI-improved spreadsheets with live data |
| Views | 124 | 108 |
| Attribute | Milvus | Rows |
|---|---|---|
| Pricing Type | freemium | freemium |
| Free Trial | No | No |
| API Available | No | No |
| Mobile App | No | No |
| Rating | 4.4 ✓ | 4.3 |
| Founded | - | - |
| Team Size | - | - |
| Deployment | - | - |
| Popularity (Views) | 124 ✓ | 108 |
Pros
- Handles the largest vector datasets of any open-source option
- GPU acceleration for ultra-fast indexing
- Strong enterprise adoption and LF AI foundation backing
Cons
- Complex to operate at full distributed scale
- Heavier infrastructure requirements than lighter alternatives
Pros
- Familiar spreadsheet interface with powerful extensions
- AI analyst is genuinely useful for summaries
- Live data connections eliminate manual updates
Cons
- Less mature than Google Sheets for complex formulas
- Integrations limited compared to dedicated ETL tools
- Billion-scale vector search
- Multiple index types (HNSW, IVF, DiskANN)
- GPU acceleration support
- Distributed cloud-native architecture
- Python, Java & Go SDKs
- AI analyst for natural language data Q&A
- Live API and database integrations
- Python code execution in cells
- Automated report scheduling
- Beautiful shareable report publishing
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