Datadog and New Relic compete in the monitoring solution category to enhance visibility and operational efficiency for businesses. Datadog holds an advantage due to its features, extensive integrations, and custom dashboard capabilities.
Features: Datadog offers sharable dashboards, intuitive AWS integration, and comprehensive logging tools. New Relic excels in application monitoring through APM, insightful error tracking, and detailed transaction analysis.
Room for Improvement: Datadog could improve its API consistency and reduce logging costs. New Relic can enhance its alert functionalities and performance monitoring flexibility. Both tools face user-experience challenges, needing improvements in UI intuitiveness and integration depth.
Ease of Deployment and Customer Service: Datadog supports versatile deployment across clouds and receives mixed customer service reviews. New Relic also offers broad deployment but needs improved support responsiveness and quicker response times.
Pricing and ROI: Datadog's flexible pricing may lead to high costs when scaling, yet users report decent ROI with productivity gains. New Relic is criticized for high pricing despite its analytical value, offering scalable options for startups but requiring strategic budget allocation for larger applications.
Previously we had thirteen contractors doing the monitoring for us, which is now reduced to only five.
Datadog has delivered more than its value through reduced downtime, faster recovery, and infrastructure optimization.
I believe features that would provide a lot of time savings, just enabling you to really narrow down and filter the type of frustration or user interaction that you're looking for.
When I have additional questions, the ticket is updated with actual recommendations or suggestions pointing me in the correct direction.
Overall, the entire Datadog comprehensive experience of support, onboarding, getting everything in there, and having a good line of feedback has been exceptional.
I've had a couple instances where I reached out to Datadog's support team, and they have been really super helpful and very kind, even reaching back out after resolving my issues to check if everything's going well.
Issues that could be solved quickly sometimes take longer because they go around in circles.
I have reached out to customer support multiple times for various cases, particularly for customization such as creating dashboards, and my experience has been good.
Datadog's scalability has been great as it has been able to grow with our needs.
We did, as a trial, engage the AWS integration, and immediately it found all of our AWS resources and presented them to us.
Datadog's scalability is strong; we've continued to significantly grow our software, and there are processes in place to ensure that as new servers, realms, and environments are introduced, we're able to include them all in Datadog without noticing any performance issues.
Datadog is very stable, as there hasn't been any downtime or issues since I've been here, and it's always on time.
Datadog seems stable in my experience without any downtime or reliability issues.
These incidents are related to log service, indexes, and metric capturing issues.
The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data.
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 AI aspect would be great where we would not need to go and look at different transactions or different modules of Datadog, as AI can actually provide the data to us on Datadog UI.
Using real-time data, if there are any malicious patterns or something happening, they can identify those.
Email alert customization is limited; it cannot be tailored much, which makes the system more rigid than optimal.
New Relic can get pricey for larger organizations.
The setup cost for Datadog is more than $100.
Everybody wants the agent installed, but we only have so many dollars to spread across, so it's been difficult for me to prioritize who will benefit from Datadog at this time.
My experience with pricing, setup cost, and licensing is that it is really expensive.
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.
Having all that associated analytics helps me in troubleshooting by not having to bounce around to other tools, which saves me a lot of time.
Datadog was able to find the alerts and trigger to notify our team in a very prompt manner before it got worse, allowing us to promptly adjust and remediate the situation in time.
Using New Relic speeds up troubleshooting and resolution, giving us a clearer picture of where issues are, thus saving time and effort.
The best features in New Relic include its numerous API integrations and a good source of support.
Another aspect I appreciate is its good alerting mechanism, which can throw alerts and can be configured with PagerDuty or Slack, allowing easy checks on triggers and troubleshooting using New Relic.
Product | Market Share (%) |
---|---|
Datadog | 7.4% |
New Relic | 5.1% |
Other | 87.5% |
Company Size | Count |
---|---|
Small Business | 80 |
Midsize Enterprise | 46 |
Large Enterprise | 93 |
Company Size | Count |
---|---|
Small Business | 65 |
Midsize Enterprise | 50 |
Large Enterprise | 61 |
Datadog integrates extensive monitoring solutions with features like customizable dashboards and real-time alerting, supporting efficient system management. Its seamless integration capabilities with tools like AWS and Slack make it a critical part of cloud infrastructure monitoring.
Datadog offers centralized logging and monitoring, making troubleshooting fast and efficient. It facilitates performance tracking in cloud environments such as AWS and Azure, utilizing tools like EC2 and APM for service management. Custom metrics and alerts improve the ability to respond to issues swiftly, while real-time tools enhance system responsiveness. However, users express the need for improved query performance, a more intuitive UI, and increased integration capabilities. Concerns about the pricing model's complexity have led to calls for greater transparency and control, and additional advanced customization options are sought. Datadog's implementation requires attention to these aspects, with enhanced documentation and onboarding recommended to reduce the learning curve.
What are Datadog's Key Features?In industries like finance and technology, Datadog is implemented for its monitoring capabilities across cloud architectures. Its ability to aggregate logs and provide a unified view enhances reliability in environments demanding high performance. By leveraging real-time insights and integration with platforms like AWS and Azure, organizations in these sectors efficiently manage their cloud infrastructures, ensuring optimal performance and proactive issue resolution.
New Relic offers real-time application monitoring and insight into performance bottlenecks. Its customizable dashboards and APM integration provide efficient operational support, while server performance alerts ensure quick issue detection.
New Relic provides comprehensive monitoring of application performance, tracking bottlenecks across databases and front-end components. Users employ it for server and infrastructure monitoring, as well as analyzing key metrics such as CPU and memory usage. The solution's ability to integrate with tools like PagerDuty enhances incident management capabilities. However, users have expressed a need for improvements in query language simplicity, more detailed historical insights, and better mobile app monitoring support.
What are New Relic's most important features?In industries like e-commerce and financial services, New Relic supports application performance monitoring to enhance user experience and system reliability. Organizations leverage its insights for optimizing performance, particularly in server operations and infrastructure management. Its ability to monitor API failures through synthetic monitoring is crucial for maintaining high service levels.
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