Latest updates for Vector-Database

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Recent items include:

  • The Vector Database Lie
  • Vector Databases: When pgvector Beats Pinecone (and When It Doesn’t)
  • Top 15 vector databases in 2026: A production decision guide from 100+ enterprise deployments

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dzone.com /1 week ago

The Vector Database Lie

The Setup: The Hype Machine It’s vector database season. Conferences are full of RAG pipeline talks. Pinecone raised over $100 million; Milvus, Weaviate, and Qdrant are all well-fu...

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javacodegeeks.com /2 weeks ago

Vector Databases: When pgvector Beats Pinecone (and When It Doesn’t)

A frank comparison from an engineering standpoint — architecture trade-offs, honest benchmarks, real pricing math, and Java client examples for both. The “which vector database sho...

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medium.com /2 weeks ago

Top 15 vector databases in 2026: A production decision guide from 100+ enterprise deployments

A practitioner’s comparison of pgvector, Pinecone, Weaviate, Qdrant, Milvus, and 10 other vector databases.Continue reading on Medium »

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marktechpost.com /2 weeks ago

Best Vector Databases in 2026: Pricing, Scale Limits, and Architecture Tradeoffs Across Nine Leading Systems

Vector databases are now core retrieval infrastructure for RAG and agentic AI. This guide compares nine production options on architecture, pricing, and scale. The post Best Vector...

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dev.to /3 weeks ago

Vector Databases Explained: What They Don’t Tell You

Everyone working in AI reaches a moment where they search a document and get back something that looks right but means nothing — or searches for a concept and gets back noise. That...

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dzone.com /1 week ago

S3 Vectors: How to Build a RAG Without a Vector Database

Every RAG tutorial follows the same script: embed your documents, spin up a vector database (Pinecone, Weaviate, pgvector, OpenSearch), manage its infrastructure, and pray the cost...

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dzone.com /1 month ago

The $50,000 Vector Database You Don't Need

The Meeting That Triggered This Article A few months ago, I sat in a room as a team pitched a $5,000/month vector database subscription. Their use case: storing roughly 100,000 pro...

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dev.to /3 weeks ago

Day 2 - RAG - What is Vector DB ?

To recall, Integrating our private documents with LLM is called RAG. Lets assume that, we have some pdfs containing our data. That data in the pdf will be broken down into chunk...

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medium.com /1 month ago

When Cosine and Dot Product Are Not Enough: Real Stories of Vector Search with Euclidean…

Choosing the right distance is not a mathematical detail. Sometimes, it is the whole product.Continue reading on Data Science Collective В»

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openstreetmap.org /1 month ago

Speeding up access to vector tiles

The problem I’ve been creating and serving web-based maps such as this one for some time. That’s based on raster tiles, and an osm2pgsql database is used to store the data that t...

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aws.amazon.com /1 month ago

Ring’s Billion-Scale Semantic Video Search with Amazon RDS for PostgreSQL and pgvector

In this post, we share Ring’s billion-scale semantic video search on Amazon RDS for PostgreSQL with pgvector architectural decisions vs alternatives, cost-performance-scale challen...

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dev.to /1 month ago

Adding Semantic Search to Your Postgres App with pgvector

pgvector is a Postgres extension that adds vector storage and similarity search to an existing database, so you can run semantic queries directly against your application data with...

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medium.com /1 week ago

How I Optimized PostgreSQL for Vector Embeddings and Cut Query Latency by 94%

Stop treating pgvector like a regular index. Here’s the production playbook nobody talks about.Continue reading on Medium »

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realpython.com /1 month ago

Real Python: Vector Databases and Embeddings With ChromaDB

The era of large language models (LLMs) is here, bringing with it rapidly evolving libraries like ChromaDB that help augment LLM applications. You’ve most likely heard of chatbots...

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habr.com /1 month ago

«ECS — like» вектор на с++

В программировании частая задача это работа с последовательными элементами. В этой, порой непростой задаче, нам часто помогают вектора. Вектора бывают самыми разными от queue и set...

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habr.com /2 days ago

Встраиваемая векторная БД для RAG на .NET 8: когда внешние сервисы избыточны

Если вы делаете RAG (Retrieval-Augmented Generation) на .NET, то рано или поздно упираетесь в вопрос: куда складывать эмбеддинги и как быстро искать по ним.Существующие варианты де...

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rubyflow.com /2 weeks ago

Stop Paying for Vector Databases: How to Build AI Search in Postgres

I see developers trying to build “AI Chatbots” that know about their specific company data. They want…

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marktechpost.com /2 days ago

A Coding Guide to Implement a pgvector-Powered Semantic, Hybrid, Sparse, and Quantized Vector Search System

In this tutorial, we build a complete pgvector playground inside Google Colab and explore how PostgreSQL can work as a powerful vector database for modern AI applications. We start...

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towardsdatascience.com /1 month ago

I Replaced Vector DBs with Google’s Memory Agent Pattern for my notes in Obsidian

Persistent AI memory without embeddings, Pinecone, or a PhD in similarity search. The post I Replaced Vector DBs with Google’s Memory Agent Pattern for my notes in Obsidian appeare...

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aws.amazon.com /3 weeks ago

Query billion-scale vectors with SQL: Integrating Amazon S3 Vectors and Aurora PostgreSQL

In this post, you’ll learn how to query Amazon S3 Vectors from Amazon Aurora PostgreSQL-Compatible Edition using standard SQL, and how to combine vector similarity results with rel...

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medium.com /2 weeks ago

Vector Search vs. RAG: Stop Building the Wrong Pipeline

Here is the uncomfortable truth: most teams shipping “RAG-powered” features today are over-engineering their stack.Continue reading on Medium »

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venturebeat.com /3 weeks ago

The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next

The vector database category is undergoing a shift in response to the needs of agentic AI. The retrieval-augmented generation (RAG)-to-vector database pipeline doesn't cut it anymo...

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venturebeat.com /1 week ago

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, em...

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marktechpost.com /1 week ago

Meet Turbovec: A Rust Vector Index with Python Bindings, and Built on Google’s TurboQuant Algorithm

turbovec brings Google Research's TurboQuant algorithm to vector search, offering 16x compression and zero codebook training for RAG pipelines. The post Meet Turbovec: A Rust Vecto...

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aws.amazon.com

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habr.com

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