Amazon ElastiCache
Redis
Amazon DynamoDB
memcached
ArangoDB
Amazon RDS
MongoDB
Ninox
Qdrant
Milvus
Weaviate
pgvecto.rs
Vespa.ai
txtai
Zilliz
ElasticSearch
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.
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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, Qdrant should be more popular than Amazon ElastiCache. It has been mentiond 40 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.
Key-value databases are designed to store and retrieve data using simple key-value pairs, making them ideal for applications that require fast and simple data access. AWS offers a fully managed key-value database service called Amazon ElastiCache that supports popular key-value engines such as Redis and Memcached. - Source: dev.to / 8 months ago
Cloud-Based Caching Services: Evaluate the use of cloud-based caching services, such as Amazon ElastiCache or Redis Cloud, for managed caching solutions that offer scalability, resilience, and reduced maintenance overhead. - Source: dev.to / 9 months ago
Amazon ElastiCache (database) Amazon ElastiCache is a web service that simplifies deploying, operating and scaling an in-memory cache in the cloud. The service improves the performance of web applications by providing information retrieval from fast, managed, in-memory caches, instead of relying entirely on slower disk-based databases. Https://aws.amazon.com/elasticache/. - Source: dev.to / over 1 year ago
Amazon DynamoDB Accelerator (DAX) and ElastiCache both are fully managed caching services from AWS. DAX is designed especially for DynamoDB on the other hand ElastiCache can cache anything including DynamoDB. Source: over 1 year ago
Not to sound like a purist, but when I build serverless applications, I'd prefer for all of it to be serverless. Using Amazon Elasticache breaks that paradigm. That service has pay-per-hour pricing and doesn't quite have the flexibility I'm used to when working with serverless services. - Source: dev.to / over 1 year ago
Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 4 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 1 month 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 / 2 months ago
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Amazon DynamoDB - Amazon DynamoDB is a fully managed NoSQL database service offered by Amazon.
Weaviate - Welcome to Weaviate
memcached - High-performance, distributed memory object caching system
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
