Splunk Observability Cloud Reviews

We are IT consultants and our primary use case for this solution is for analyzing machine data.

The feature I find most valuable is the data integration.

I would like to see an improvement and some innovation in the customer interface, which puts something in your design. If we able to customize more parts of the user interface, it would be great. I also think the scalability should be improved.

The solution is stable and I haven't seen any box glitches or crashes.

We have a hundred users. I really do not know how scalable the solution is. I couldn't find any submission for a flat fee and for expanding the installation in my last project. So perhaps this is also something that can be improved.

I am happy with the support, how they respond and help to solve issues.

The initial setup is straightforward and you can do it yourself.

My experience with this solution in analyzing machine data is really good. The interface could be simplified and I would like to have more clustering. On a scale of one to ten, I rate this solution a seven. 

Splunk sends security alerts. It's being used on two levels. One for the analysis of the data by the data scientists. Two, for the engineers to troubleshoot if there are any issues happening, like any security bugs, or anything that needs to be addressed and never mediated across. 

In terms of the application performance security, application performance tooling has been a key factor for me using Splunk. We are also looking into options, like other third parties or even open-source tools that help the capturing the application performance, fine-tuning, which leads to the security aspect.

We haven't faced any problems yet. It's working as expected. We are using the enterprise-grade, strong products and we're just paying a lot for it right now.

People intend to go for automation. We are following the works process and we are inculcating the engineers to ensure everything is automated. Whatever needs to be mitigated, has to be followed up on ticketing tools, this tool would come in. It handles the issues going on and what needs to be remediated in this single tool.

We need multiple tools in order to accomplish what we need. It's kind of a medium across multiple products. It would be better if we have a dedicated tool, that takes care of the entire work process, including automation as well.

They do not have all the features that I expect right now.

I have been using Splunk for three years. 

We haven't really experienced any glitches or bugs. It depends on the use cases and so far I haven't seen any as of now.

It is scalable.

They're good. They're quite good at providing the service for technical engineers as well.

I would say that the setup is pretty straightforward because they have their own documentation that you can follow. It takes an associate's capability in order to accomplish it. They have good documentation and dedicated support to take care of any issues that come forward.

As a newbie, I wouldn't prefer Splunk. The reason being that it's a completely enterprise-grade solution. As a startup, you don't implement Splunk for the first time. We'd put an open-source product. With us, we have many of the Italian products, which proves to be a good open-source solution. In the end, people intend to go for enterprise support for the vulnerability patching, report generation, and enterprise support. People go for licensing based on that. I wouldn't refer any newbies to go for a weak enterprise-grade solution as they barge into any technology.

I would rate Splunk a seven out of ten. 

The solution involves observability in general, such as Application Performance Monitoring, and generally addresses digital applications, web applications, sites, and mobile applications. I worked with it in two companies: one in the energy sector and one in the hotel sector.

The Splunk teams helped us with data collection, instrumentation, and many other options.

The testing and monitoring of infrastructure is useful. We also use it for many metrics and can use it effectively for troubleshooting and for detection. It's very helpful. 

With Splunk Observability Cloud, I appreciate working with open telemetry. The standards of open telemetry are especially useful for collecting data such as traces, matrices, and logs. Splunk respects the standards of open telemetry. This is beneficial. Many clients work with AWS and the cloud in general with multiple solutions such as Datadog, Dynatrace, and Splunk. Working with the standard open telemetry is very advantageous. Splunk Observability Cloud is very simple for users in general, including developers, DevOps, and data teams. It's more straightforward compared to Dynatrace.

There are many out-of-the-box solutions proposed by Splunk, such as dashboards for AWS instances, EC2, Fargate, and Lambda. It's very helpful for beginning, especially for monitoring, and the detectors for alerting help understand how the platforms work.

The no-sample feature is great. It eliminates blind spots.

After completing the instrumentations, we have many dashboards and tests for monitoring infrastructure, particularly CPU and memory. We also use applicative metrics such as JVM, Java Runtime, and many other applicative metrics and testing. For troubleshooting, we can detect problems in seconds, which is particularly helpful for digital teams.

AI analytics have the potential for a lot of functionality. The detectors for alerting may prove useful.

When we deploy the instrumentation in the application, we can start using the dashboards immediately. The dashboard building is very helpful for starting work.

It's beneficial for monitoring performance and infrastructure, especially when deploying applications with multiple versions with Git. It's important to detect performance issues, such as CPU consumption or memory consumption, particularly over time in Java and Python.

For other teams, they need help and guidance to use custom metrics. For observability engineers and specialists, it's straightforward, but for others, it can be challenging.

The solution overall is very valuable for me.

The time to value was immediate. Once we deployed, we started to use the dashboard directly and began detecting issues. 

Saving time with automation can save us weeks. It's improving our resilience. It helps us detect issues and increase performance. 

The solution has been very useful for helping us focus on business-critical initiatives.

Regarding dashboard customization, while Splunk has many dashboard building options, customers sometimes need to create specific dashboards, particularly for applicative metrics such as Java and process terms. These categories of dashboards would be very helpful for customers.

I started working with Splunk Observability Cloud in 2023.

The system is relatively stable. We rarely have problems accessing the dashboard or the page. We encounter problems in the Splunk platform very rarely.

It's very scalable. We haven't experienced any problems with the instrumentation or scalability. On a scale of one to ten, I'd rate it a ten.

We've used the solution across more than 250 people, including engineers.

I would rate Splunk technical support at six out of ten.

When we have a problem and need to create a case, the response isn't quick. They often require multiple questions, with five or six emails to get a response. Problem resolution typically takes between two and five days, which isn't very helpful. However, sometimes we do receive quicker solutions.

Neutral

We used legacy solutions such as Grafana and Prometheus. There are several differences between Splunk Observability Cloud and these solutions. We used Grafana as a monitoring solution, however, it's not truly observability. We used OpenSearch for logs, Prometheus for metrics, and Grafana to work with Prometheus. That said, it's not equivalent. Observability is different.

We're also familiar with Datadog and Dynatrace.

The implementation took between two and three weeks.

For cloud deployment, it's straightforward. We can use GitLab and DevOps CI/CD. For on-premise deployment, such as Linux and deployment with satellite, it's easy yet requires some work to configure the configuration files.

Updates are generally needed, especially for the open telemetry version or SDK. However, regarding the platform itself, we don't need to do anything.

I worked with my company when they used the solution, so I'm not certain about the history of how long it took to detect problems. However, for mean time to detect, and mean time to respond, I'm sure it's very helpful, and we can estimate a minimum improvement of 20%.

We're a customer and end-user.

Currently, in France, we cannot use the artificial intelligence option. While this option is enabled for the United States and many countries, it's not yet available in France. However, the solution with detectors, especially for alerting, is important for us.

I recommend it, especially for teams using legacy monitoring.

I would rate Splunk Observability Cloud nine to ten out of ten.