Cost-Effective, Low Latency Vector Search with Azure Cosmos DB
Summary: Integrates DiskANN directly into Azure Cosmos DB’s partitioned index trees, maintaining vector indexes with operational data. Delivers <20ms search on 10M vectors, billion-scale autoscaling, stable recall, and 12–43× lower cost than serverless vector DBs. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Nitish Upreti (Microsoft)
- 2. Harsha Vardhan Simhadri (Microsoft)
- 3. Hari Sudan Sundar (Microsoft)
- 4. Krishnan Sundaram (Microsoft)
- 5. Samer Boshra (Microsoft)
- 6. Balachandar Perumalswamy (Microsoft)
- 7. Shivam Atri (Microsoft)
- 8. Martin Chisholm (Microsoft)
- 9. Revti Raman Singh (Microsoft)
- 10. Greg Yang (Microsoft)
- 11. Tamara Hass (Microsoft)
- 12. Nitesh Dudhey (Microsoft)
- 13. Subramanyam Pattipaka (Microsoft)
- 14. Mark Hildebrand (Microsoft)
- 15. Magdalen Manohar (Microsoft)
- 16. Jack Moffitt (Microsoft)
- 17. Haiyang Xu (Microsoft)
- 18. Naren Datha (Microsoft)
- 19. Suryansh Gupta (Microsoft)
- 20. Ravishankar Krishnaswamy (Microsoft)
- 21. Prashant Gupta (Microsoft)
- 22. Abhishek Sahu (Microsoft)
- 23. Hemeswari Varada (Microsoft)
- 24. Sudhanshu Barthwal (Microsoft)
- 25. Ritika Mor (Microsoft)
- 26. James Codella (Microsoft)
- 27. Shaun Cooper (Microsoft)
- 28. Kevin Pilch (Microsoft)
- 29. Simon Moreno (Microsoft)
- 30. Aayush Kataria (Microsoft)
- 31. Santosh Kulkarni (Microsoft)
- 32. Neil Deshpande (Microsoft)
- 33. Amar Sagare (Microsoft)
- 34. Dinesh Billa (Microsoft)
- 35. Zishan Fu (Microsoft)
- 36. Vipul Vishal (Microsoft)
BibTeX Citation
@article{upreti_vldb25,
title = {{Cost-Effective, Low Latency Vector Search with Azure Cosmos DB}},
author = {Upreti, Nitish and Simhadri, Harsha Vardhan and Sundar, Hari Sudan and Sundaram, Krishnan and Boshra, Samer and Perumalswamy, Balachandar and Atri, Shivam and Chisholm, Martin and Singh, Revti Raman and Yang, Greg and Hass, Tamara and Dudhey, Nitesh and Pattipaka, Subramanyam and Hildebrand, Mark and Manohar, Magdalen and Moffitt, Jack and Xu, Haiyang and Datha, Naren and Gupta, Suryansh and Krishnaswamy, Ravishankar and Gupta, Prashant and Sahu, Abhishek and Varada, Hemeswari and Barthwal, Sudhanshu and Mor, Ritika and Codella, James and Cooper, Shaun and Pilch, Kevin and Moreno, Simon and Kataria, Aayush and Kulkarni, Santosh and Deshpande, Neil and Sagare, Amar and Billa, Dinesh and Fu, Zishan and Vishal, Vipul},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5166--5183},
doi = {10.14778/3750601.3750635},
url = {https://doi.org/10.14778/3750601.3750635},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,193 | An In-Depth Study of Filter-Agnostic Vector Search on a PostgreSQL Database System: [Experiments & Analysis] | 2026 | SIGMOD | 5.093636e-05 |
| 10,512 | SVFusion: A CPU-GPU Co-Processing Architecture for Large-Scale Real-Time Vector Search | 2026 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 93 | Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph | 2019 | VLDB | 0.00034701237 |
| 406 | AnalyticDB-V: A Hybrid Analytical Engine Towards Query Fusion for Structured and Unstructured Data | 2020 | VLDB | 0.00019054532 |
| 2,534 | ELPIS: Graph-Based Similarity Search for Scalable Data Science | 2023 | VLDB | 8.4561875e-05 |
| 2,920 | SingleStore-V: An Integrated Vector Database System in SingleStore | 2024 | VLDB | 7.9628307e-05 |
| 3,424 | Schema-Agnostic Indexing with Azure DocumentDB | 2015 | VLDB | 7.4241655e-05 |
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