Query Inside
Datadog
Zabbix
Dynatrace
NewRelic
AppDynamics
Nagios
LogicMonitor
Prometheus
Queryinside is your AI-powered platform for fast, smart data search and system monitoring. Easily analyze logs, track user activity, and gain real-time insights from your web apps or cloud platforms. With advanced search (keyword, semantic, hybrid), web analytics, and seamless integrations, Queryinside helps developers and teams make smarter decisions—faster. Subscribe for tutorials, updates, and expert tips!
Datadog is a monitoring and analytics platform for cloud-scale application infrastructure. Combining metrics from servers, databases, and applications, Datadog delivers sophisticated, actionable alerts, and provides real-time visibility of your entire infrastructure. Datadog includes 100+ vendor-supported, prebuilt integrations and monitors hundreds of thousands of hosts.
Query Inside DatadogQuery Inside's answer
Queryinside combines AI-powered search, real-time analytics, and seamless log monitoring into one intuitive platform—making it fast, smart, and easy to find actionable insights from your data.
Query Inside's answer
A person should choose queryinside over its competitors because it offers a seamless, AI-powered data search and monitoring experience. With real-time analytics, fast root cause detection, easy integrations (like AWS CloudWatch), and intuitive dashboards, queryinside makes it incredibly simple to manage logs, understand user behavior, and get actionable insights—saving both time and effort. It's built for speed, scalability, and clarity.
Query Inside's answer
The primary audience for queryinside includes developers, data analysts, DevOps teams, and tech-driven businesses that need fast, reliable tools for log monitoring, system analytics, and user behavior tracking. Whether you're managing a SaaS app, a web platform, or cloud infrastructure, queryinside is designed to help technical teams search, analyze, and optimize their data workflows more efficiently.
Query Inside's answer
The Story Behind queryinside
queryinside was born out of a common frustration—digging through endless logs and scattered data just to find one useful insight. The goal was simple: build a powerful yet easy-to-use platform that helps teams search, analyze, and monitor their data in real-time without complexity.
With AI at its core, queryinside was designed to make sense of massive datasets, reduce time spent on root cause analysis, and deliver meaningful results fast. It empowers developers, analysts, and product teams to spend less time troubleshooting and more time building.
From integration with platforms like AWS CloudWatch to real-time web analytics and hybrid search, queryinside has evolved into a comprehensive solution for modern data and system visibility
Query Inside's answer
Primary Technologies Behind queryinside queryinside is built using a modern tech stack designed for speed, scalability, and real-time performance. Some of the core technologies include: Python & Django: For robust backend development and API handling. React: For a fast, interactive, and intuitive frontend experience. Elasticsearch: Powers the fast and flexible search capabilities across massive datasets. PostgreSQL: Used for structured data storage and reliable data management. AWS Services: For scalable cloud infrastructure, including integration with AWS CloudWatch for real-time log monitoring. Redis & Celery: To support background tasks and fast data processing. Docker & Kubernetes: For containerization and smooth deployment across environments. This modern stack allows queryinside to deliver an AI-powered, lightning-fast, and secure data monitoring experience.
Query Inside's answer
As of now, queryinside is actively growing and building partnerships with startups, mid-size tech companies, and development teams across industries. While we’re not listing specific brand names publicly just yet, our primary customers include:
SaaS companies looking to track user activity and system logs in real time Ecommerce platforms needing advanced page analytics and performance insights DevOps teams integrating log monitoring and alerts from services like AWS CloudWatch Product teams using queryinside for behavioral analytics and event tracking AI and data-driven startups who need fast, scalable search across massive datasets We’re proud to support businesses that rely on intelligent, real-time data insights to move faster and make smarter decisions.
Based on our record, Datadog seems to be more popular. It has been mentiond 5 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.
Ideally, if we had access to the underlying infrastructure, we could probably install the Datadog Agent and configure it to send our logs directly to Datadog, or even use AWS Lambda functions or Azure Event Hub + Azure Functions in case we were facing some specific cloud scenarios. - Source: dev.to / over 1 year ago
Currently supported : Datadog, Jenkins, DNS, HTTP. Source: over 2 years ago
Datadog is a powerful monitoring and security platform that gives you visibility into end-to-end traces, application metrics, logs, and infrastructure. While Datadog has great documentation on their Kubernetes integration, we've observed that there's some missed nuance that leads to common pitfalls. - Source: dev.to / almost 4 years ago
.. Is to see you email address being silently distributed to every single company that I've watched a talk from. And now suddenly get several promotional spam emails per day from some 4-5 different domains like instana.com, datadoghq.com, snyk.io, cockroachlabs.com (some of them send even multiple emails per day!). Source: almost 4 years ago
We're commonly doing this with logging, using services such as Loggly or DataDog. We're using managed databases, be it on AWS, Heroku or database-vendor-specific solutions. We're storing binaries on S3. Externalising user authentication and authorization might be a good candidate as well. - Source: dev.to / about 4 years ago