Qdrant
Milvus
Weaviate
pgvecto.rs
Vespa.ai
txtai
Zilliz
ElasticSearch
InfluxData
TimescaleDB
Prometheus
MongoDB
MapR Converged Data Platform
Google Cloud Dataproc
Amazon EMR
VictoriaMetrics
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 seems to be a lot more popular than InfluxData. While we know about 40 links to Qdrant, we've tracked only 2 mentions of InfluxData. 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 / 16 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 / 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
I would highly recommend using a proper Time Series Database like QuestDB or InfluxDB to do this instead. You can always export data from wither of those two into Excel if your boss wants it in excel, but it's much easier to do data transformations, create graphs and reports, etc. If you have all the data in a proper database. Source: over 2 years ago
I would suggest using something better suited to IoT data than ... a spreadsheet. I'd recommend looking at one of the Time Series Databases for this. 1) QuestDB or 2) InfluxDB as these are much better suited to streaming data. Source: over 2 years ago
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
TimescaleDB - TimescaleDB is a time-series SQL database providing fast analytics, scalability, with automated data management on a proven storage engine.
Weaviate - Welcome to Weaviate
Prometheus - An open-source systems monitoring and alerting toolkit.
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
