12 posts
2026 vector database guide: Pinecone, Weaviate, Qdrant, Milvus, pgvector comparison table, decision framework, cost, KVKK, and the 'start with pgvector' advice.
We compare Qdrant, Pinecone, Weaviate, Milvus and pgvector on scale, latency, hybrid search and KVKK. A 2026 decision flow, table, and selection checklist.
Not 'which one' but 'which fits me.' Real benchmark data, a decision framework, the recall-latency tradeoff, and KVKK/data residency. Synthetic benchmarks lie.
Choosing a vector database depends on scale, latency and operations. I compare pgvector, Qdrant, Milvus and Pinecone with 2026 benchmarks and a decision framework.
Vector database comparison: we evaluate Qdrant, Milvus, Weaviate, and pgvector for enterprise RAG in terms of scale, performance, cost, data sovereignty, and benchmarking.
pgvector or Qdrant? A 2026 production comparison on latency, hybrid search, and scale, with RAG evaluation metrics and self-hosted options for KVKK.
Vector database selection 2026: pgvector, Qdrant, Milvus compared. Latency, throughput, scalability, and KVKK-compliant hosting. Start small, benchmark with your own data, grow.
Scale, latency/recall tradeoff, hybrid search and hosting. Why to measure on your own data, not synthetic benchmarks, with KVKK/BDDK context.
What is a vector database? A vector database is a specialized database that stores numerical vectors (embeddings) representing the meaning of text, images, or audio, and quickly finds the records closest in meaning to a query. This guide: a clear definition, how it works, similarity search and the HNSW index, tools like Qdrant, its relationship to RAG, the difference from classic databases, KVKK, and FAQs.
Which vector database is right for RAG and agents? I compare pgvector, Qdrant, Milvus, and Weaviate on performance, scale, hybrid search, and data sovereignty.
Naive RAG's six fatal weaknesses are fully solved in 2026 by agentic RAG. A production-grade RAG with plan/reflect/verify loops, hybrid retrieval, and claim-verification built on the LangGraph v0.4 state-graph used by Klarna, LinkedIn, and Uber — plus a KVKK-compliant Turkish bank case study and cost-latency tradeoffs.
A comprehensive reference for designing, scaling, and shipping Retrieval-Augmented Generation (RAG) systems in production with KVKK compliance. Covers Turkish-capable embedding model selection, vector DB comparison, chunking, hybrid search, re-ranking, hallucination control, eval harness, and three anonymized Turkish enterprise case studies — end-to-end production architecture.