Datadog and Prometheus compete in infrastructure monitoring. Datadog seems to have the upper hand with its comprehensive integrations and hosted services, offering a more intuitive and seamless experience.
Features: Datadog's strengths lie in hosted infrastructure, seamless integrations with AWS, Docker, and Slack, and visually appealing dashboards. Prometheus stands out for its robust metrics collection, open-source flexibility, and dynamic configuration options.
Room for Improvement: Datadog users suggest enhancements in customization, pricing visibility, and documentation. Prometheus struggles with query language complexity, lacks in advanced visualization, and could improve its security monitoring features.
Ease of Deployment and Customer Service: Datadog provides flexible cloud deployments and exceptional customer service, known for responsiveness and support. Prometheus, as an open-source solution, presents challenges with on-premises deployment and relies on community support.
Pricing and ROI: Datadog's subscription pricing, though complex, delivers value through efficiency improvements and reduced downtime. Prometheus offers a cost-effective open-source alternative, but demands more user management and provides less functionality.
Using open-source Prometheus saves me money compared to AWS native services.
Prometheus does not offer traditional technical support.
Prometheus is scalable, with a rating of ten out of ten.
Deploying it on multiple instances or using Kubernetes for automatic management has enhanced its stability.
In future updates, I would like to see AI features included in Datadog for monitoring AI spend and usage to make the product more versatile and appealing for the customer.
The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data.
There should be a clearer view of the expenses.
The setup cost for Datadog is more than $100.
Prometheus is cost-effective for me as it is free.
Our architecture is written in several languages, and one area where Datadog particularly shines is in providing first-class support for a multitude of programming languages.
The technology itself is generally very useful.
It allows me to save money by avoiding costs associated with AWS native services like CloudWatch or Amazon Prometheus.
Company Size | Count |
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Small Business | 78 |
Midsize Enterprise | 42 |
Large Enterprise | 82 |
Company Size | Count |
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Small Business | 14 |
Midsize Enterprise | 8 |
Large Enterprise | 12 |
Datadog is a comprehensive cloud monitoring platform designed to track performance, availability, and log aggregation for cloud resources like AWS, ECS, and Kubernetes. It offers robust tools for creating dashboards, observing user behavior, alerting, telemetry, security monitoring, and synthetic testing.
Datadog supports full observability across cloud providers and environments, enabling troubleshooting, error detection, and performance analysis to maintain system reliability. It offers detailed visualization of servers, integrates seamlessly with cloud providers like AWS, and provides powerful out-of-the-box dashboards and log analytics. Despite its strengths, users often note the need for better integration with other solutions and improved application-level insights. Common challenges include a complex pricing model, setup difficulties, and navigation issues. Users frequently mention the need for clearer documentation, faster loading times, enhanced error traceability, and better log management.
What are the key features of Datadog?
What benefits and ROI should users look for in reviews?
Datadog is implemented across different industries, from tech companies monitoring cloud applications to finance sectors ensuring transactional systems' performance. E-commerce platforms use Datadog to track and visualize user behavior and system health, while healthcare organizations utilize it for maintaining secure, compliant environments. Every implementation assists teams in customizing monitoring solutions specific to their industry's requirements.
Prometheus-AI Platform offers flexible solutions for collecting, visualizing, and comparing metrics, appreciated for its scalability, rich integrations, and open-source adaptability.
Prometheus-AI Platform provides a reliable framework for monitoring and analyzing metrics across diverse environments. With extensive API support, it supports data collection, querying, and visualization, integrating seamlessly with tools like Grafana. High availability, scalability, and lightweight configuration make it suitable for traditional and microservice environments, while community support enhances its utility. Though its query language and interface require improvements for better ease of use, and with calls for stronger integration options, the platform remains a leading choice for comprehensive metric analysis.
What are Prometheus-AI Platform's main features?Companies leverage Prometheus-AI Platform across various industries, utilizing it to monitor and analyze metrics from applications and infrastructure. It is extensively used in financial services and IT sectors for collecting, scraping logs, and monitoring Kubernetes deployments. Deployed both on-premise and in cloud environments like Azure and Amazon, it supports system and application metrics analysis, ensuring a comprehensive view for developers.
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