Splunk AppDynamics and Datadog are competitors in the monitoring and performance management category. AppDynamics has the edge in detailed transaction insights and alert accuracy, while Datadog offers comprehensive monitoring and ease of use in transaction breakdown.
Features: AppDynamics offers code-level deep dive analysis, dynamic baselining, and byte code instrumentation which provide detailed transaction insights and improve alert accuracy. Datadog, on the other hand, is lauded for its comprehensive monitoring capabilities and user-friendly transaction breakdown features, making it accessible for diverse business needs.
Room for Improvement: AppDynamics would benefit from enhanced network monitoring and easing the maintenance of agents. There is also a need to expand support for newer technologies and languages. Datadog users point to a need for better documentation, streamlined third-party tool integration, and an improved query language alongside cost-effective enhancements.
Ease of Deployment and Customer Service: AppDynamics shines in on-premise and hybrid cloud deployments with a quick installation process and robust customer support. However, Datadog offers excellent flexibility, especially in public and hybrid cloud environments, though its complex UI can challenge new users. Both solutions provide responsive customer service, but Datadog faces occasional challenges related to acquisition turnovers.
Pricing and ROI: Pricing is a notable consideration, with AppDynamics being more expensive, leading to challenges in licensing and cost management, although it delivers good ROI through reduced MTTR and increased customer satisfaction. In contrast, Datadog offers usage-based pricing that provides flexibility but can result in unexpectedly high costs if not monitored closely. While AppDynamics justifies its higher pricing with significant business value, Datadog's costs can be burdensome for smaller firms despite its visibility benefits and potential cost savings through efficient monitoring.
According to errors, exceptions, and code-level details related to their application performance on a daily basis, the application development team tries to help with Splunk AppDynamics to reduce errors and exceptions, which helps the end users get application availability and feel more confident.
To understand the magnitude of it, when the company asked to replace Splunk AppDynamics with another tool, I indicated that for the proposed tool, we would need five people to do the analysis that Splunk AppDynamics enables me to do.
It's very hard to find ROI because we are currently focused only on the on-premises applications.
AppDynamics is much more helpful.
We got a contact, an account manager, to work directly with for technical support.
They help us resolve any issues raised by our team relating to operations, application instrumentation, or any other issues.
We have reached maximum capacity in our tier, and extending capacity has not been cost-effective from Splunk's perspective.
I would rate the scalability of Splunk AppDynamics as a nine out of ten.
I did not find any Docker solution available with it, and a separate instance has to be installed.
It is necessary to conduct appropriate testing before deploying them in production to prevent potential outages.
There are no issues or bugs with the 20.4 version; it is very stable with no functionality or operational issues.
I can rate it nine out of ten.
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.
Splunk AppDynamics does not support the complete MELT framework, which includes metrics, events, logging, and tracing for the entire stack.
If AppDynamics could develop a means to monitor without an agent, it could significantly improve application performance and reduce potential problems.
A good integration with Splunk would be very interesting, as Splunk is a good product for logs, and that part is currently missing in Splunk AppDynamics.
The setup cost for Datadog is more than $100.
Customers have to pay a premium price, however, they receive considerable value from the product.
All these solutions at the moment are cheap, but it is like paying for insurance; you pay insurance to avoid major damage.
We find its pricing reasonable and competitive.
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.
We have multiple tools, but end users prefer to use Splunk AppDynamics because their portal navigation is very simple and clear.
The feature that I appreciate in AppDynamics Browser Real-User Monitoring is the intuitive and user-friendly dynamic mapping it creates for workflows.
What I like the most about Splunk AppDynamics is the end-to-end observability for the application, along with traces.
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.
Splunk AppDynamics enhances application performance monitoring with advanced diagnostics and real-time insights, offering seamless end-to-end transaction tracking and infrastructure visibility.
AppDynamics provides critical tools for businesses to analyze application behavior and performance. Through innovative features like transaction snapshot analysis and adaptable dashboards, users can quickly identify and address issues, ensuring high levels of system uptime and efficiency. It is designed to support complex environments including Kubernetes and AWS, enhancing user experience by detecting performance issues early. Despite needing improvements in network monitoring and integration, it remains a robust option for tracking application health.
What are the key features of AppDynamics?In industries like financial services and e-commerce, AppDynamics facilitates performance tracking across distributed systems, optimizing infrastructure to meet consumer demands. It excels in environments needing precise transaction monitoring and is pivotal in delivering high value and satisfaction.
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