Meilisearch
Algolia
Typesense
ElasticSearch
Apache Solr
ParadeDB
Vespa.ai
Magifind
Qdrant
Milvus
Weaviate
pgvecto.rs
Vespa.ai
txtai
Zilliz
ElasticSearch
Meilisearch is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms.
Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.
Meilisearch QdrantNo features have been listed yet.
Qdrant's answer:
Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.
Qdrant's answer:
Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.
Qdrant's answer:
Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.
Based on our record, Qdrant seems to be a lot more popular than Meilisearch. While we know about 38 links to Qdrant, we've tracked only 3 mentions of Meilisearch. 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.
Meilisearch [https://meilisearch.com] for the search index. - Source: Hacker News / 12 months ago
Meilisearch is an open-source, lightning-fast, and hyper-relevant search engine that fits effortlessly into your apps, websites, and workflow. You can find more info on our website https://meilisearch.com. Source: over 2 years ago
Algolia.com - new plans are very affordable Meilisearch.com - open source. Source: about 3 years ago
Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / 3 days ago
I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / 10 days ago
I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / about 1 month ago
There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / about 2 months ago
Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance. - Source: dev.to / 2 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.
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Typesense - Typo tolerant, delightfully simple, open source search 🔍
Weaviate - Welcome to Weaviate
ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
