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atita arora
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qdrant-landing/content/blog/case-study-dailymotion.md
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@@ -73,10 +73,7 @@ Title , Tags , Description , Transcript (generated by [OpenAI whisper](https://o
### Why Qdrant?
> "We're utilizing the Google Cloud platform, so our initial approach involved leveraging their Vector search engine called matching engine (Vertex Matching Engine). However, we encountered several challenges with this matching engine. \
Firstly, the API wasn't user-friendly, and secondly, we faced limitations in adding filters and metadata, unlike the flexibility we now enjoy with Qdrant. \
Additionally, the solution was managed, limiting our control, and the cost was disproportionately high for our needs. Subsequently, we explored alternative solutions and discovered that Qdrant offered the easiest implementation process. Their comprehensive documentation facilitated testing, and we found no drawbacks for our specific use case. Moreover, the exceptional support from the Qdrant team during implementation, especially in tackling complex aspects, solidified our decision to choose Qdrant over other options." \
Samuel Leonardo Gracio - Sr Machine Learning Engineer , Dailymotion
![quote-from-Samuel](/case-studies/dailymotion/Dailymotion-Quote.jpg)
Looking at the complexity, scale and adaptability of the desired solution, the team decided to leverage Qdrant’s vector database to implement a content-based video recommendation that undoubtedly offered several advantages over other methods: