Swiftype VS Weaviate
Compare Swiftype VS Weaviate and see what are their differences
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Swiftype
Algolia
ElasticSearch
Apache Solr
Site Search 360
Typesense
Amazon CloudSearch
Sajari
Weaviate
Qdrant
Milvus
pgvecto.rs
txtai
Zilliz
Vespa.ai
EVA DB
Swiftype is a customizable website search and content marketing embedded platform that creates real-time search results based on suggestions and search preference. Swiftype lets you manage results via the user login dashboard and assess search trends in order to identify profitable terms and promote products and services.
Swiftype lets you create a free account and view a demo version before payment is required and also provides online support for sales and technical enquiries. The Swiftype software is completely optimizable for mobile devices which gives your website users complete functionality.
Swiftype WeaviateBased on our record, Weaviate seems to be more popular. It has been mentiond 28 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Weaviate: An open-source, cloud-native vector database built for scalable and fast vector searches. It's particularly effective for semantic search applications, combining full-text search with vector search for AI-powered insights. - Source: dev.to / 3 months ago
Weaviate is an open-source vector search engine with out-of-the-box support for vectorization, classification, and semantic search. It is designed to make vector search accessible and scalable, supporting use cases such as semantic text search, automatic classification, and more. - Source: dev.to / 4 months ago
Congrats to them! What have your experiences with vector databases been? I've been using https://weaviate.io/ which works great, but just for little tech demos, so I'm not really sure how to compare one versus another or even what to look for really. - Source: Hacker News / 4 months ago
A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving... - Source: dev.to / 5 months ago
To find semantically similar texts we need to calculate the distance between vectors. While we have just a few short texts we can brute-force it: calculate the distance between our query and each text embedding one by one and see which one is the closest. When we deal with thousands or even millions of entries in our database, however, we need a more efficient way of comparing vectors. Just like for any other way... - Source: dev.to / 7 months ago
Algolia - Algolia's Search API makes it easy to deliver a great search experience in your apps & websites. Algolia Search provides hosted full-text, numerical, faceted and geolocalized search.
Qdrant - Qdrant is a high-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Apache Solr - Solr is an open source enterprise search server based on Lucene search library, with XML/HTTP and...
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs