Cisco Cloud Observability Operators

I mostly work with the performance metrics of the CPU, or host metrics, as well as application metrics and traces. Overall, I use these mostly for real-time monitoring based on the application to track application performance.

For the monitoring of infrastructure, it is quite insightful because in-depth, I can see what is going on in the infrastructure. If something goes down or some crons fail inside the infrastructure, the alerts are quite helpful for more visibility on the cloud-native side.

This is quite helpful for improving the application observability and the infrastructure side as well. I would rate observability above an eight.

I am not that much involved in the business side because I work as a DevOps engineer, so I do not know how much it helps on that front. However, it helps in tracking traces and metrics quite generously well and helps us improve the application side for more reliability on the business side.

It is very helpful and really enhances the AI-powered analytics, which helps us for troubleshooting the application and to get more insightful information while troubleshooting application error rates.

AI-powered guidance is really helpful because it provides more actionable insights and highlights anomalies automatically. I do not need to go through it manually, and it also helps us with smart alerting and recommendations.

It helped operationally because due to the insights of the applications, I get more insight for our application to enhance it further. It detects anomalies and correlates data while guiding us to the root causes, so we can enhance our application accordingly.

I have seen that mean time to resolution was reduced around 30 to 50 percent. The main reason for this combination is because of real-time monitoring and AI-powered anomaly detection and distributed tracing. Instead of manually checking the logs and metrics across multiple tools, the platform quickly highlights the issues, correlates data, and points us towards the root cause.

After implementing Splunk Observability Cloud , there was a deep learning curve for the new tool. It took one or two months to get proper insights from it. After configuring, I have seen that it is very useful for tracking traces and metrics of our application, servers, and clusters. Adoption time is usually after two months, or after a few weeks of getting Splunk Observability Cloud .

Splunk Observability  Cloud is highly effective in improving digital resilience. Real-time visibility and proactive alerting and fast root cause analysis, distributed tracing, and AI-driven insights enable anomaly detection, which allows us to quickly understand failures and recover faster. This is critical for maintaining system availability and helps us handle failures in complex distributed environments since we can see how services interact and where breakdowns occur.

Regarding features, it helps us for better understanding of how the application works and in-depth tracking of application monitoring.

It can be more enhanced using additional AI power. I can get more reliability using AI because AI-driven guidance is more useful nowadays. It can really improve more on the AI side because it will help us to reduce manual intervention with the system and root cause analysis will be much better with AI over human analysis.

I would say that it is quite helpful, but for different kinds of applications, it could be improved because sometimes it might provide a cloud judgment of the root cause analysis. I need to do manual intervention using a dedicated human for root cause analysis for better understanding of the root cause. This is how the agentic side can be improved.

I have been working with Splunk Observability  Cloud for around a year.

It is quite scalable. Right now, it is providing much better insights and can be more enhanced over several aspects. I would rate scalability an eight to eight point five.

I have tried other solutions, but they were not that great in terms of functionalities and overall performance. Splunk Observability Cloud is much better than the others because it provides AI alongside the solution. This is very helpful due to the AI-driven solutions and guidance for root cause analysis. Splunk Observability Cloud goes through the details of application traces and metrics in depth, so I get better observability over the application. This is why I have preferred Splunk Observability Cloud over other monitoring tools.

I have tried SignalFx, but it was not quite insightful. I have tried Splunk Observability Cloud over SignalFx.

Splunk Observability Cloud is quite insightful and helpful for improving the observability side. I provide this solution an overall rating of eight.

I mainly use Splunk Observability Cloud  to monitor the performance of our cloud-native infrastructure. Because we have created multiple infrastructures, we use it to handle and monitor everything.

Splunk Observability Cloud  helps us manage latency across any of our projects and APIs. It is particularly valuable for detecting issues before they occur. We can predict features and errors in advance. Recently, we discovered problems in seven of our APIs that we were able to solve because of this predictive capability.

The best feature of Splunk Observability  Cloud is that I can identify the root cause of any problem, including API latency. The real-time alerts and smart alerting system are exceptional, allowing me to know what is happening in real-time.

Detectors in Splunk Observability  Cloud are very useful, and I have recently used them with great results.

Regarding the no-sample tracing feature, we collect multiple data from various sources. This feature is very useful since we recently shifted to it, and it is working very well.

The AI-powered analytics that Splunk provides allows me to get a smart analyzed version of any report.

Splunk Observability Cloud has greatly impacted our operations by reducing timing requirements. We get smarter solutions and overall use cases in a smart way. I have reduced our manpower requirements and time commitment significantly. Splunk Observability Cloud reduces our mean time to detect by approximately one to two hours.

The LLM in Splunk Observability Cloud is very powerful, and the vector database infrastructure is excellent. This is why we switched from our previous tools, and I believe it was a very good decision that has resulted in better outcomes.

The AI-powered analytics that Splunk provides delivers a smart analyzed version of reports, and it is quite good, but it is very generic. The issues identified could be better addressed through deeper AI thinking to provide a more effective solution.

I have been using Splunk Observability Cloud for more than eight or nine months.

Splunk Observability Cloud experienced a significant outage recently when it went down for approximately five to six hours. This impacted us considerably because we were actively working during that time.

I would rate the technical support for Splunk Observability Cloud as 9.5 out of 10 because we received their support during our deployment. They were very helpful in assisting us to create a good infrastructure.

I find Splunk Observability Cloud to be very good. I previously used DataDog for observing everything, but Splunk Observability Cloud is more accurate and a better solution.

Previously with other applications, analyzing and controlling our API latency required almost five to six hours a day of resources. With Splunk Observability Cloud, I only need to allocate one to two hours maximum per day to accomplish the same tasks.

I highly recommend Splunk Observability Cloud. If you are using any other third-party tool, Splunk Observability Cloud is significantly better than the alternatives.

I highly recommend creating better documentation for Splunk Observability Cloud. This documentation could be integrated with AI to provide specific use case solutions so that users do not have to search through Splunk documentation every time. Instead, users could directly ask about the issues they are facing and receive targeted solutions. My overall review rating for Splunk Observability Cloud is 9 out of 10.

Our primary use case for Splunk Observability Cloud  is to monitor our infrastructure and applications, and it helps us troubleshoot issues related to any failures.

The feature we appreciate most about Splunk Observability Cloud  is their distributed tracing. We also value the ability to create real-time dashboards and their alerting system is exceptional. The main best feature of that observability is their distributed tracing.

We are very satisfied with the out-of-the-box dashboards and detectors in Splunk Observability  Cloud. In distributed tracing, we have banks as our clients, so if anything goes wrong with transactions, we directly go to the trace and troubleshoot those issues faster.

The AI-powered analytics and guidance in Splunk Observability  Cloud is very useful. You can observe your LLM models and monitor the usage of your APIs in that cloud.

Splunk helps improve our operational performance and resilience significantly. Before we used Splunk Observability Cloud, if any failures occurred, we had to go to servers and check all the log files to find the failure. Now in Splunk, we go to that single dashboard and filter with the timestamp of failure to directly find the log, allowing us to troubleshoot issues faster. In terms of optimization, before using Splunk, we could not measure why our API was taking 100 ms, but now through distributed tracing, we can see where the bottleneck of that API is. If that bottleneck is the database, we optimize our database queries, and our application is now optimized.

Splunk Observability Cloud has reduced our mean time to detect by approximately 25 to 30 percent because it offers real-time monitoring and intelligent alerting, allowing us to troubleshoot issues faster and enhancing detection by approximately 30 to 40 percent.

In terms of pricing, I have one issue with Splunk Observability Cloud. In a large-scale organization, it does not have features such as cost optimization or budgeting for observability spend. I think they need to improve that so that I can optimize our observability. For instance, if our thousands of server applications are running, I should be able to set a budget, such as only spending $100 per month for a specific environment. They need to introduce that feature because it is very important for budgeting.

In terms of areas for improvement in Splunk Observability Cloud, the first is cost budgeting. The second is that they have many integrations, but if you are new to Splunk or new to observability, you must dive deep into more concepts. They can improve user-friendly features so that new users can set up their observability in their environment more smoothly. I think they need to improve in that integration part so that end users can onboard their infrastructure or applications very effectively.

I would appreciate more simplicity in the platform.

I have been using Splunk Observability Cloud for the past eight or nine months.

I rate the stability of Splunk Observability Cloud as ten out of ten because it is very stable, especially since we are using their cloud environment, and Splunk Observability Cloud is built for cloud-native systems.

We have not explored enriching data with custom metrics in Splunk Observability Cloud because their ready-to-use dashboards are well designed, and every organization can benefit from them. However, if you have a very large organization with over ten thousand servers running applications, you may need to build a team to create custom metrics for your specific use case.

I would rate their technical support in Splunk Observability Cloud a nine.

I have used other vendors such as Elastic Stack  and Grafana  Stack, but in Splunk Observability Cloud, there are so many integrations and useful features that no other vendor can offer. In Grafana , the logs and tracing features are almost nonexistent. You can use Grafana only for monitoring your infrastructure, but Splunk provides end-to-end visibility with infrastructure monitoring, tracing, and overall observability of our application.

Deploying Splunk Observability Cloud is an intermediate task for new users, but if you have been in this space for one or two years or longer, then it is easy to deploy their products.

It can take up to one week to deploy Splunk Observability Cloud.

We are not using the NoSample tracing feature in Splunk Observability Cloud.

In our organization, we have approximately 25 to 30 users using the solution daily.

We do not require any maintenance for Splunk Observability Cloud since we are using their cloud solution, which means that all patching and updates are done by them.

I recommend Splunk Observability Cloud to other organizations because we are currently saving our engineers time by 20 to 30 percent, and for infrastructure alerting, we can use it to ensure that servers will not go down. Every organization should use this because it will reduce your engineering team's effort and the downtime of your application, and in terms of any failure or APIs, you can troubleshoot your issues faster.

End-to-end visibility into our cloud-native environment is very important. If an organization is building a SaaS or B2B software, then end-to-end visibility is crucial in terms of security, failures, and compliance. The end-to-end visibility of our infrastructure and applications is extremely important.

I recommend Splunk Observability Cloud to every user because they offer trials. If you do not just read the reviews, you should try it out. Understanding the biggest features and why others are using it can be beneficial, and I always recommend Splunk Observability Cloud for end-to-end visibility in your application.

I gave this review an overall rating of ten out of ten.