Semantic Search
Go beyond keyword matching. Learn how semantic search understands meaning, how vector databases power it, and how to add it to your product. Comparisons, explainers, and integration guides.
How to Build a Recommendation Engine (Without a Data Science Team)
Recommendation engines used to require ML engineers and a pile of infrastructure. They don't anymore. Here's how to build one that actually works, using tools any developer can ship in a weekend.
Giorgi Kenchadze2026-04-27Vecstore vs Weaviate: Open-Source Power vs Plug-and-Play Search
Weaviate is a powerful open-source vector database with hybrid search and module support. Vecstore is a managed search API. Here's how to decide which one your project needs.
Giorgi Kenchadze2026-04-27The Hidden Costs of Self-Hosting Your Vector Database
Everyone benchmarks Qdrant vs Milvus vs pgvector on raw performance. Nobody benchmarks what happens six months in. Here's the real TCO of self-hosting a vector database.
Giorgi Kenchadze2026-04-18Vecstore vs Elasticsearch: Managed Search API vs DIY Search Infrastructure
Elasticsearch gives you control. Vecstore gives you a finished search API. Here's where each one makes sense, and where teams usually underestimate the cost of doing it themselves.
Giorgi Kenchadze2026-04-13What Is Multimodal Search?
Multimodal search lets users search across text, images, and other data types using any of them as the query. Here's how it works, why it matters, and how to build it.
Giorgi Kenchadze2026-04-08pgvector vs Pinecone vs Qdrant: 2026 Benchmarks
We benchmarked pgvector, Pinecone, Qdrant and Weaviate on latency, recall, and cost per million vectors. See which one actually wins on each metric.
Giorgi Kenchadze2026-04-06How to Add Semantic Search to a Next.js App
Add semantic search to your Next.js app using server actions. No separate backend needed. Users search by meaning, not keywords. Full code included.
Giorgi Kenchadze2026-03-21Search Is the Most Neglected Feature in Every SaaS App
68% of users go straight to the search bar. 80% leave because the results are bad. Search is where your highest-intent users go, and most products completely fumble it.
Giorgi Kenchadze2026-03-11You Don't Need a Vector Database
Most teams shopping for a vector database don't actually need one. They need search that works. There's a difference, and it matters more than you think.
Mariam Rokhvadze2026-03-06Vecstore vs Pinecone: When You Don't Need a Raw Vector Database
Pinecone gives you a vector index. Vecstore gives you a working search product. Here's how to decide which one your project actually needs.
Giorgi Kenchadze2026-03-01Vecstore vs Algolia: Semantic Search vs Keyword Search
Algolia is great at keyword search. Vecstore is built for semantic search, image search, and understanding meaning. Here's when to use each.
Giorgi Kenchadze2026-02-14Keyword vs Semantic Search: Which One to Use
Keyword search matches exact words. Semantic search understands meaning. Here's how each works, when to use which, and why picking wrong frustrates your users.
Giorgi Kenchadze2026-01-29Explore More Topics
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Image Search
Everything about building image search — reverse image lookup, text-to-image search, face search, visual search for e-commerce, and OCR. Tutorials, architecture breakdowns, and production cost analysis.
Tutorials
Step-by-step coding guides for adding search, image recognition, and content moderation to your app. Python, JavaScript, React, Next.js — full code included.
Industries
How different industries use image search, semantic search, and content moderation. Real estate, fashion, marketplaces, dating apps, and more.
Comparisons
Side-by-side breakdowns of search and vector database tools. Vecstore vs Pinecone, Vecstore vs Algolia, and more — so you can pick the right tool for your stack.
Content Moderation
Automate NSFW detection and content moderation without building your own ML pipeline. Practical guides for keeping user-generated content safe at scale.