09/02/2026 11:16 pm
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I'm currently building a RAG pipeline using DeepSeek-V3 for a local documentation project and I'm torn on which vector database to pair it with. I've been looking at Milvus and Pinecone, but I'm worried about latency since DeepSeek is so snappy. My dataset is around 500k chunks, mostly technical manuals, so high-dimensional search performance is key. I'm also trying to keep the setup relatively lightweight if possible. Has anyone here tested DeepSeek with specific databases like Weaviate or Qdrant? I’d love to know which one handles the embeddings most efficiently without breaking the bank on infrastructure. What would you recommend for the best balance of speed and scalability?
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09/02/2026 11:40 pm
Curious about one thing: what's the dimension size of the embeddings you're using with DeepSeek-V3? Basically, if you're hitting 1024 or higher, the compute cost for indexing changes a lot. I've used Qdrant Vector Database before and honestly, the performance was solid, but I had some issues with memory overhead when scaling technical manuals. Before I dive into the technical details, are you planning to host this on-prem or go cloud-native?
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13/02/2026 10:15 am
Helpful thread 👍
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09/02/2026 11:40 pm
+1
