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.

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.

Currently, we are in the process of migrating from on-premises to Splunk Cloud as well as Observability. For metric-based monitoring, we can monitor via Observability and are migrating it there. We are setting up private locations to monitor synthetic tests, such as ping checks, port checks, and URL monitoring. The rest is metric-based monitoring, which is being done by Splunk using Splunk OTeL, which is an OpenTelemetry agent for Observability. This agent brings metrics from end devices to Observability. Based on these metrics, we set detectors and rules to trigger alerts.

Our observability is not yet live in production with Splunk Observability Cloud. It is currently being built, and we are adding new components, but it is not yet fully ready.

Comparing to Cloud, Splunk Cloud, or any other solution, the most valuable feature of Splunk Observability Cloud is that it is entirely based on metrics. The agent is also very lightweight compared to Splunk UF and does not consume much compute resources on the end server or host from which we are pulling data. However, it can only monitor metrics and cannot monitor logs.

Regarding how Splunk Observability Cloud has benefited our organization, we are yet to go live, but most of the configuration that requires conditions and triggers on Splunk Cloud involves writing queries. With Splunk Observability Cloud, the process is quite simple. We can directly get metrics flowing, set thresholds, and everything is UI-based. This requires less time to set up and use. I do not have that much visibility with Splunk Observability Cloud at this time as I am working as an administrator. It has helped us create dashboards for visualization purposes.

There is one thing that could be improved in Splunk Observability Cloud. We have the capability in Splunk to connect to Splunk agents such as Splunk forwarders from a deployment server and update the end agents and forwarders using server classes. We can push and update configurations from our own hosted servers without needing to access the end device. In Splunk Observability, the OTeL agent cannot be updated from our end. Every time we need to update, we have to reach out to users or gain access to the host to update the configurations. There should be a solution to update OTeL agents from Splunk Observability Cloud itself.

I have been working with Splunk Observability Cloud for approximately five to six months.

Splunk Observability Cloud is reliable based on my experience with stability and reliability so far.

We were facing some challenges with the stability of Splunk Observability Cloud regarding the login page. It was not working several times and was not accepting SSO authentication. The observability team found a solution for this issue, though I am not fully aware of the details. There were several times when opening the page did not directly log in and showed some errors.

I have not encountered any scenarios regarding the scalability of Splunk Observability Cloud. It should be good because it is cloud-based. I am not aware of the licensing model and how it scales or what the rules are for scaling.

I was not directly involved with technical support for Splunk Observability Cloud, but I am aware that my teammates reached out to support. They were finding issues regarding configuration, installation, and deployment of Observability for specific components. Since Observability is cloud-based and hosted by Splunk, the components we own on-premises are the OTeL gateways, agents, and private locations. They reached out to the vendor regarding these components, and the support was quite smooth. They have raised some bugs as well for the vendor to fix. I would rate the technical support from Splunk an eight out of ten.

Since it is cloud-based, Splunk Observability Cloud was ready to use upon deployment. The OTeL gateways were built by our team and required configuration. I was not part of that process but am aware that we needed to configure the OTeL gateways to route data to them as an endpoint and from there it would be ingested to Observability or forwarded to Observability. There were no significant issues with this process and it was quite smooth. However, configuring private locations on a few gateways was quite difficult to set up and maintain because Docker was going down at times. There were some issues that were discussed with Splunk vendor, and they provided guidance on how to fix them.