Splunk Observability Cloud

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.

My experience with Splunk Observability Cloud  involves monitoring infrastructure, application performance monitoring, and real-time alerting. Although I am no longer working with Splunk Observability Cloud  due to a recent position change that occurred approximately two months ago, I previously monitored servers, containers, Kubernetes , application performance, and Docker  images. In terms of monitoring, I tracked response time, error rate, and latency. This capability helped in identifying performance issues or infrastructure issues before users were impacted. For instance, if Kafka failed, we knew about it before users experienced an impact and could resolve it before it caused maximum damage to our systems. I also used dashboards and alerts to monitor critical services and received notifications whenever issues arose.

The features of Splunk Observability  Cloud that I found most valuable included application performance monitoring and distributed tracing, particularly when monitoring distributed systems or applications. Real-time alerting and Kubernetes  monitoring were essential since Kubernetes is quite complex. I could effectively monitor Kubernetes using Splunk Observability  Cloud. Additionally, the Smart Attack Detector, which I tried at the last moment, was a good feature, although I did not work extensively with it. The Log Observer was very fast and reliable, and the dashboards provided good visualization for troubleshooting and monitoring. If there was a network outage, I received notifications very quickly.

Splunk Observability Cloud helped me detect performance issues faster and reduce downtime in my organization. Earlier, I had limited visibility into my application performance. After implementing observability, I could see end-to-end transaction tracing and quickly identify where issues arose, which reduced troubleshooting time and improved overall application stability and availability for our customers and systems. This capability also helped in proactive detection.

I believe that areas of Splunk Observability Cloud that could be improved include the initial setup and instrumentation costs, which take more time for APM. Some dashboards and detectors require tuning, and I think the visualization needs enhancement. Additionally, alert noise remains an issue, and we need suppressions for when systems go down for short periods. Better integration with third-party tools and easier onboarding of data would also be beneficial.

When evaluating the stability and reliability of Splunk Observability Cloud, I can confirm it has been reliable. I would rate it eight out of ten for reliability.

Splunk Observability Cloud scales very well with the growing needs of my organization. I can demonstrate the scalability of our system to our customers, which is advantageous for business. This capability helped us secure business as we provide real insights to customers who were happy to purchase our systems and applications. The ROI has been good for us.

I communicated with the technical support of Splunk Observability Cloud regarding our issues, specifically when I was unable to monitor or set up Kubernetes to monitor our infrastructure. They were able to help us, and we purchased an on-demand call for assistance, which they provided.

I did not participate significantly during the initial setup and deployment of Splunk Observability Cloud, but I was part of the team. I know the process is straightforward. We simply needed to ensure that all data was in the correct format, matched current dashboard setups, and included all necessary fields for insights.

My experience with lowering the cost of unplanned digital downtime using Splunk Observability Cloud has been positive, as it helped us significantly. Our system was bottlenecking and consuming excessive resources, but with the ability to detect and resolve that issue, overall system usage was reduced without further bottlenecking.

Regarding metrics or data points confirming performance improvement and resilience, I found that during certain times, we experienced the most significant spike in our systems due to multiple users requesting the same service. We needed to change our overall architecture as we were not scaling adequately, and this was bottlenecking our systems. By observing this from the dashboards, I realized improvements could be made. After implementing the solution, our application's stability improved significantly. I can confidently say our availability improved by forty percent, and downtime was reduced by approximately seventy to eighty percent.

My impression of the No-Sample Tracing feature in Splunk Observability Cloud is that it helped us detect key metrics and real use cases, particularly in tracking and monitoring. I primarily tracked server uptime, application response time, API latency, and similar metrics. Combining these parameters instead of relying on a single factor improved our system. Specifically, I used distributed tracing to understand how requests flowed through our network and how different systems responded, which helped determine if any particular system impacted all our systems.

Regarding the AI-powered analytics and guidance provided by Splunk Observability Cloud, I have not actually used the AI features, particularly with ITSI, as I did not utilize that aspect for observability.

My teams effectively utilized the ability to enrich data with custom metrics in Splunk Observability Cloud. They found valuable insights from our systems and created reports that the application and infrastructure teams used to decide their workarounds and solutions. They developed different solutions, experimenting and improving our systems by relying on observability to understand what happens when we adjust parameters or change configurations.

When evaluating the effectiveness of the out-of-the-box customizable dashboards provided by Splunk Observability Cloud, I note that we mostly used the default dashboards. While we created a custom dashboard to track our overall system flow, we relied on pre-built dashboards for monitoring and representing our business perspective. When we needed to showcase our environment to customers, we demonstrated our scalability and system performance, including response time and downtime, providing insightful details from the dashboards for business use cases.

I would rate Splunk Observability Cloud an eight out of ten, where ten is the best and one is the worst.

I work with Splunk Observability Cloud .

Splunk Observability Cloud  is effective for detecting anomalies and preventing system outages.

There are pre-built dashboards where I can check service centers and monitor spikes in errors and traces. I can also check error logs, and everything is consolidated while providing anomaly alerts in case there is any deviation from the baseline.

The personalized dashboard helps my team. Splunk Observability  Cloud has its own query language that can be used to build easy dashboards. Multiple teams can build their own, replicate them, and also have role-based access control, which is beneficial.

The application management feature helps with end-user experiences because front-end monitoring helps track user issues and any back-end issues that may be causing them. It shows how the user experience is overall and identifies any outages. Front-end monitoring is very useful.

As an integrator, I think the biggest advantage of Splunk Observability  Cloud is because it is part of the Splunk ecosystem, it is good to correlate logs with application data through traces and metrics. Overall, it is an evolving product, not top class, but it is getting there.

I see many good things about the product and many advantages. Regarding the negative side, I think the licensing can be much better because it is based upon host units and there is additional licensing for the number of traces that I can bring in. A simplified licensing model would be much better, similar to what other tools offer. Pricing could be either based upon ingestion or directly based upon host units, rather than multiple different trackers. There are licenses for custom metrics, licenses for the number of traces that I can ingest, and host unit licensing. A better licensing plan would be beneficial.

I have been using Splunk Observability Cloud for more than two years.

I have not seen any issues with stability. The solution is very stable.

Regarding scalability, I do not think there is an issue with scaling. I have never encountered any issues with that.

Support is good.

I have worked on Coralogix , which is also an observability tool. I worked in the product company itself. I have also worked on Dynatrace , and now I am working on Cribl .

The installation and deployment process is somewhat challenging, but there are multiple ways of deployment that give me a lot of options. I would say it is acceptable and not that complicated. I can deploy agents with Splunk deployment server, which is beneficial. However, there is some dependency on the deployment server.

As an integrator, I deployed it and made it workable with OpenTelemetry .

I am able to observe significant ROI with Splunk Observability Cloud. When I worked with a previous solution, it was one-third of the cost of Dynatrace , so there was definitely an exceptional return on investment. It helped reduce costs by almost 50%.

Splunk Observability Cloud is affordable. I have visited the PeerSpot  website and downloaded reports on Azure , Grafana , and Splunk Observability Cloud.

When I compare Splunk Observability Cloud to other vendors, the good part is the branding because the support is good. There is a large community where I can look for known issues. However, experience-wise, DataDog is far more superior and easier to use. DataDog has its own agent for tracing, so I just deploy one trace. With Splunk Observability Cloud, they are dependent upon OpenTelemetry , and there is a learning curve because it is open source. The onboarding is not as smooth as DataDog or Dynatrace.

I deploy both on-cloud and on-premise options for clients. I have deployed Splunk Observability Cloud on Splunk Cloud. I have not used threat detection because there is a separate tool for it. I have not deployed a solution on AWS  Cloud or purchased it from AWS Marketplace  in my career. I would rate this review 7.5 out of 10.

My main use case for Splunk Observability Cloud  involves performing visualized performance metrics and tracing capability, making sure that all troubleshooting is faster during incident response. We also integrate it to ensure that every data point and operational data is monitored.

A specific example of how I have used Splunk Observability Cloud  in a real situation is that we make use of it to ensure that every operational data point is being monitored, traceable, and visible.

Regarding my main use case for Splunk Observability Cloud, I would add that we really utilize it in the area of cost management, along with the smarter alerting system and the log search performance.

The dashboard and lead time metrics from Splunk Observability Cloud really improve our workflow, making every workflow more visible and understandable for our stakeholders as well.

Splunk Observability Cloud has positively impacted my organization. Although we have not noticed any specific outcomes, we really recommend it for handling higher data volumes effectively, especially its scalability, which is suitable for us during enterprise environments, monitoring, and alerting.

The best features that Splunk Observability Cloud offers include APM monitoring, the fast alerting system during incident response, and the dashboard that provides real-time metrics.

To improve Splunk Observability Cloud, I wish they could develop more in the area of pricing and cost transparency, provide a smoother learning curve, and enhance the log management experience, ensuring that log navigation is not solely focused on metrics and tracing but also has good search performance to understand larger data sets.

I would also like to see a very good user interface and onboarding experience that is smoother for new users.

Before we wrap up, I want to emphasize the need for improvements in the log search performance and the smarter alerting system.

I have been using Splunk Observability Cloud for over a year.

In my experience, Splunk Observability Cloud is very stable in the area of real-time monitoring and analytics.

When handling higher data volume and scalability, I can say that we have over 70% efficiency now.

For scalability, I would rate it an eight, as it is very good in responding faster and monitoring larger data sets.

We have great feedback from the customer support of Splunk Observability Cloud, as they help solve and make bug alert management easier, respond quickly to incidents, and monitor data sets effectively.

I have not used any different solution before Splunk Observability Cloud.

I have seen a return on investment with Splunk Observability Cloud, with current metrics showing over 75% efficiency. It has really helped our workflow, saved time, reduced costs, and also saved employees' time.

My experience with the pricing, setup cost, and licensing for Splunk Observability Cloud was acceptable at first, though I believe they need to improve more in this area. I would rate it a seven, but overall, the costing and licensing were fine for me.

Before choosing Splunk Observability Cloud, I was recommended to it specifically.

Splunk Observability Cloud deserves an eight out of ten rating. I choose an eight because of their fast response and the monitoring of strong infrastructures.

I would advise others looking into using Splunk Observability Cloud because I am a witness to its effectiveness. It is very beneficial for workflow, making tasks easier and flexible while being able to track and monitor all data sets.