krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-10-03 01:48:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

Update case-study-qovery.md

Browse Source
This commit is contained in:
daniel-azoulai
2025-05-27 11:19:41 -07:00
parent 80eb94b81f
commit ff0f3f6a2c
1 changed files with 11 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/blog/case-study-qovery.md
+11 -5
View File
@@ -5,7 +5,7 @@ short_description: "Qovery scaled its AI-driven DevOps Copilot, significantly ac
description: "Discover how Qovery empowered developers and drastically reduced infrastructure management latency using Qdrant."
preview_image: /blog/case-study-qovery/social_preview_partnership-qovery.jpg
social_preview_image: /blog/case-study-qovery/social_preview_partnership-qovery.jpg
date: 2025-05-22T00:00:00Z
date: 2025-05-27T00:00:00Z
author: "Daniel Azoulai"
featured: true
@@ -31,25 +31,31 @@ Qovery’s ambitious vision for the DevOps Copilot ([read more here](https://www
![Qovery API](/blog/case-study-qovery/api-qovery.png)
*Qovery API*
### Seamless Integration of Scalable and Efficient Vector Search
Qovery chose [Qdrant Cloud](https://qdrant.tech/cloud/) after carefully evaluating several options. Romaric Philogène, CEO and co-founder of Qovery, highlighted the importance of open-source credibility, performance, ease of use, and scalability. Qdrant’s native support for [real-time indexing](https://qdrant.tech/documentation/concepts/indexing/) and low-latency queries made it ideal for handling Qovery’s significant data volume and frequency of updates.
The integration process was straightforward, with minimal operational overhead, enabling the Qovery team to focus their resources on enhancing the Copilot's capabilities rather than maintaining complex database infrastructure. With its Rust-based architecture, Qdrant delivered the speed, accuracy, and low resource utilization Qovery required.
"Qdrant is incredibly performant and stable, with virtually no maintenance overhead. It simply works, letting our engineers focus on the Copilot’s features instead of database management."
*"Qdrant is incredibly performant and stable, with virtually no maintenance overhead. It simply works, letting our engineers focus on the Copilot’s features instead of database management."*
— Romaric Philogène, CEO, Qovery
![Qovery Application](/blog/case-study-qovery/background-ingestion-qovery.png)
![Qovery Background Ingestion](/blog/case-study-qovery/background-ingestion-qovery.png)
*Qovery Background Ingestion*
### Real-time Infrastructure Management at Scale
Qovery’s implementation of Qdrant provided immediate benefits, notably in the speed and accuracy critical to DevOps operations. The DevOps Copilot drastically reduced the time developers spent waiting for infrastructure-related tasks. Actions that previously required hours or days are now executed within seconds, enabling Qovery's customers to iterate faster and deploy more reliably.
"The integration of Qdrant was so seamless and straightforward—it allowed us to rapidly scale our capabilities and deliver real-time, precise infrastructure management."
*"The integration of Qdrant was so seamless and straightforward—it allowed us to rapidly scale our capabilities and deliver real-time, precise infrastructure management."*
— Romaric Philogène, CEO, Qovery
![Qovery Background Ingestion](/blog/case-study-qovery/devops-qovery.png)
![Qovery Application](/blog/case-study-qovery/devops-qovery.png)
*Qovery Application*
Qovery currently manages over 100,000 vectors, with a trajectory to exceed 500,000 within two months. Even at this scale, Qdrant maintained rapid response times and accuracy, allowing Qovery to confidently scale their AI-driven DevOps services to more companies without compromising quality.