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* initial commit; fixed anchor links on internal docs pages * add back in absolute paths for links in code comments * fix: update linkchecker include filter to match server port 1314 PR #1629 changed the Hugo server to port 1314 but forgot to update the --include filter, which still matched port 1313. This caused all links to be excluded, making the checker a no-op (0 checked, 82277 excluded). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix url rewrite regex so images are not impacted * Fix links from non-documentation pages * Fix broken links * more broken links * more broken links * broken link * Add srcset width descriptor to .lycheeignore * Ignore URLs that contain a % character * Anchor regex so it matches the entire URL --------- Co-authored-by: kanungle <neil.kanungo@gmail.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>
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| false | How Qovery Accelerated Developer Autonomy with Qdrant | Qovery scaled its AI-driven DevOps Copilot, significantly accelerating infrastructure management. | Discover how Qovery empowered developers and drastically reduced infrastructure management latency using Qdrant. | /blog/case-study-qovery/social_preview_partnership-qovery.jpg | /blog/case-study-qovery/social_preview_partnership-qovery.jpg | 2025-05-27T00:00:00Z | Daniel Azoulai | true |
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Qovery Scales Real-Time DevOps Automation with Qdrant
Empowering Developers with Autonomous Infrastructure Management
Qovery, trusted by over 200 companies including Alan, Talkspace, GetSafe, and RxVantage, empowers software engineering teams to autonomously manage their infrastructure through its robust DevOps automation platform. As their platform evolved, Qovery recognized an opportunity to enhance developer autonomy further by integrating an AI-powered DevOps Copilot. To achieve real-time accuracy and rapid responses, Qovery selected Qdrant as the backbone of their vector database infrastructure.
Reducing Dependency on Specialized DevOps Expertise
Qovery’s ambitious vision for the DevOps Copilot (read more here) was to drastically reduce the reliance on highly specialized DevOps engineers. The goal was to empower software developers—even those without deep DevOps expertise—to manage complex infrastructure tasks through natural language interactions. However, this required extremely accurate, fast, and scalable infrastructure capable of managing a large, continuously updated corpus of data. With 5 years of historical usage data stored in BigQuery and an indexed volume rapidly approaching 500,000 vectors, Qovery needed a solution that was both performant and easy to manage.
Qovery API
Seamless Integration of Scalable and Efficient Vector Search
Qovery chose Qdrant 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 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."
— Romaric Philogène, CEO, Qovery
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."
— Romaric Philogène, CEO, Qovery
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.
Conclusion: Streamlined Innovation and Enhanced Developer Experience
Qovery’s experience demonstrates how a performant, easy-to-use vector database can significantly accelerate product innovation and reduce operational complexity. With Qdrant, Qovery could concentrate its engineering resources on value-added features rather than database management, ultimately leading to faster development cycles and happier customers.