How I Built a RAG Pipeline with DeepSeek + Weaviate

Dev.to / 6/16/2026

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Key Points

The author explains how they redesigned a retrieval-augmented generation (RAG) pipeline to control costs after finding that generic RAG tutorials can become expensive at scale.
They argue that model selection is the biggest lever for reducing spend, since the API pricing for available models ranges widely (from $0.01 to $3.50 per million tokens).
The article highlights five models the author repeatedly uses—especially DeepSeek V4 Flash for most queries and DeepSeek V4 Pro for longer context needs.
They describe additional options like Qwen3-32B and GLM-4 Plus for specific retrieval workloads, and they compare these choices against GPT-4o to show the cost gap.

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