Qdrant
Milvus
Weaviate
pgvecto.rs
Vespa.ai
txtai
Zilliz
ElasticSearch
Redis
MongoDB
ArangoDB
Apache Cassandra
PostgreSQL
neo4j
memcached
CouchBase
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.
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes with radius queries and streams. Redis has built-in replication, Lua scripting, LRU eviction, transactions and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster.
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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, Redis should be more popular than Qdrant. It has been mentiond 188 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.
Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 10 days ago
AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / about 1 month 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 / about 2 months 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 / about 2 months 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 / 3 months ago
Valkey is an open source alternative to Redis. It's a community-driven, Linux Foundation project created to keep the project available for use and distribution under the open source Berkeley Software Distribution (BSD) 3-clause license after the Redis license changes. - Source: dev.to / 5 days ago
Many popular open source projects are beloved and closely tied to particular vendors. For example, web frameworks like React and Angular are associated with Meta and Google, respectively. Database software like MongoDB, Elasticsearch, and Redis are also tied to specific commercial entities but are widely used and praised for their functionality. When there is a clear driver of a project, it can offer some benefits:. - Source: dev.to / 5 days ago
One of the most effective ways to improve the application’s performance is caching regularly accessed data. There are two leading key-value stores: Memcached and Redis. I prefer using Memcached Cloud add-on for caching because it was originally intended for it and is easier to set up, and using Redis only for background jobs. - Source: dev.to / 16 days ago
Hi there! I want to show off a little feature I made using hanami, htmx and a little bit of redis + sidekiq. - Source: dev.to / about 1 month ago
Data Handling: Utilizes Windmill for data pipelines, with a primary database powered by PostgreSQL. Auxiliary data storage is handled by MongoDB, with Redis for caching to optimize performance. - Source: dev.to / about 2 months ago
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Weaviate - Welcome to Weaviate
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
