Qdrant
Milvus
Weaviate
pgvecto.rs
Vespa.ai
txtai
Zilliz
ElasticSearch
Prometheus
Grafana
Datadog
Zabbix
NewRelic
Nagios
Splunk
Dynatrace
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.
Qdrant PrometheusNo features have been listed yet.
No Qdrant videos yet. You could help us improve this page by suggesting one.
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, Prometheus should be more popular than Qdrant. It has been mentiond 230 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 / 19 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 2 months 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
The open source projects Fastly uses and the foundations we partner with are vital to Fastly’s mission and success. Here's an unscientific list of projects and organizations supported by the Linux Foundation that we use and love include: The Linux Kernel, Kubernetes, containerd, eBPF, Falco, OpenAPI Initiative, ESLint, Express, Fastify, Lodash, Mocha, Node.js, Prometheus, Jenkins, OpenTelemetry, Envoy, etcd, Helm,... - Source: dev.to / 11 days ago
Prometheus is the best-known time-series database engine. It has many use cases, but in the context of Kubernetes, it's a great way to store and query metrics that provide observability for your cluster and its workloads. You can receive alerts when metrics change, such as a Node CPU usage spike or a Pod failure, and integrate with tools like Grafana to visualize your values on dashboards. - Source: dev.to / 18 days ago
Implement health checks and monitoring to ensure the availability and performance of your microservices. Use tools like Prometheus, Grafana, or NestJS built-in health checks. - Source: dev.to / 24 days ago
Kubernetes Documentation: https://kubernetes.io/docs/home/ Kubernetes Tutorials: https://kubernetes.io/docs/tutorials/ Kubernetes Community: https://kubernetes.io/community/ Prometheus: https://prometheus.io/ Grafana: https://grafana.com/ Elasticsearch: https://www.elastic.co/elasticsearch/ Kibana: https://www.elastic.co/kibana Helm: https://helm.sh/ Prometheus Helm Chart:... - Source: dev.to / about 1 month ago
Monitoring tools and performance profiling methods are invaluable in identifying performance bottlenecks. These tools provide real-time insights into API behavior, enabling developers to spot inefficiencies and potential issues. There's a range of monitoring tools, including platforms like New Relic, Datadog, and Prometheus that offer extensive performance metrics like response times, error rates, and resource... - Source: dev.to / about 1 month ago
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Grafana - Data visualization & Monitoring with support for Graphite, InfluxDB, Prometheus, Elasticsearch and many more databases
Weaviate - Welcome to Weaviate
Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
Zabbix - Track, record, alert and visualize performance and availability of IT resources