Splunk AppDynamics and Elastic Observability compete in the application performance monitoring and observability category. Splunk AppDynamics holds the advantage due to its comprehensive application and transaction tracking capabilities, providing deeper application-specific insights.
Features: Splunk AppDynamics is notable for its comprehensive transaction tracing, deep dive component monitoring, and exceptional business transaction tracking. It captures detailed metrics across Java and .NET applications, helping users to diagnose and resolve issues efficiently. Elastic Observability excels at logging and monitoring capabilities, particularly within large-scale, system-wide observability, but lacks the deep application insight provided by AppDynamics’ rich metrics.
Room for Improvement: Splunk AppDynamics users highlight areas such as enhanced network monitoring, more intuitive user interfaces, and improved mobile and cloud integrations. Its licensing model is also noted for being complex and expensive. Elastic Observability requires better predictive analytics, easier metric visualization, and more efficient integration features, alongside a simplified setup process.
Ease of Deployment and Customer Service: Splunk AppDynamics offers versatile deployment options across public, hybrid, and on-premises environments, with a strong customer support focus. Elastic Observability also provides flexible deployment options and is noted for its affordability, leveraging its open-source components to support larger enterprises effectively.
Pricing and ROI: Splunk AppDynamics is recognized as a premium product with high costs and complex licensing requirements. However, it delivers significant ROI through enhanced efficiency and problem resolution. Elastic Observability offers a more cost-effective solution for large-scale implementations, though its extensive features may not meet the depth required for some application-specific evaluations.
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.
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.
Elastic Observability seems to have a good scale-out capability.
What is not scalable for us is not on Elastic's side.
We have reached maximum capacity in our tier, and extending capacity has not been cost-effective from Splunk's perspective.
I did not find any Docker solution available with it, and a separate instance has to be installed.
We have tried it with a maximum of 500 concurrent users, and it provided all the required information.
It is very stable, and I would rate it ten out of ten based on my interaction with it.
Elastic Observability is really stable.
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.
For instance, if you have many error logs and want to create a rule with a custom query, such as triggering an alert for five errors in the last hour, all you need to do is open the AI bot, type this question, and it generates an Elastic query for you to use in your alert rules.
It lacked some capabilities when handling on-prem devices, like network observability, package flow analysis, and device performance data on the infrastructure side.
One example is the inability to monitor very old databases with the newest version.
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 license is reasonably priced, however, the VMs where we host the solution are extremely expensive, making the overall cost in the public cloud high.
Elastic Observability is cost-efficient and provides all features in the enterprise license without asset-based licensing.
Observability is actually cheaper compared to logs because you're not indexing huge blobs of text and trying to parse those.
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.
The most valuable feature is the integrated platform that allows customers to start from observability and expand into other areas like security, EDR solutions, etc.
the most valued feature of Elastic is its log analytics capabilities.
All the features that we use, such as monitoring, dashboarding, reporting, the possibility of alerting, and the way we index the data, are important.
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.
Elastic Observability is primarily used for monitoring login events, application performance, and infrastructure, supporting significant data volumes through features like log aggregation, centralized logging, and system metric analysis.
Elastic Observability employs Elastic APM for performance and latency analysis, significantly aiding business KPIs and technical stability. It is popular among users for system and server monitoring, capacity planning, cyber security, and managing data pipelines. With the integration of Kibana, it offers robust visualization, reporting, and incident response capabilities through rapid log searches while supporting machine learning and hybrid cloud environments.
What are Elastic Observability's key features?Companies in technology, finance, healthcare, and other industries implement Elastic Observability for tailored monitoring solutions. They find its integration with existing systems useful for maintaining operation efficiency and security, particularly valuing the visualization capabilities through Kibana to monitor KPIs and improve incident response times.
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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