Splunk Observability Cloud Reviews

Our use cases are basically just bringing log aggregation like application logs into Splunk, working on the integrations with observability. Unfortunately, with our current setup, we just have to implement Log Observer in a couple of instances so that we can have that integration with Splunk Observability Cloud. But mostly, we are working on getting logs into Splunk, so one of the primary things we've been working on is ingesting Azure logs through Event Hub into Splunk and trying to correlate across our disparate platforms.

I don't use any of those. We actually have a security team that works with Splunk Observability Cloud, and we have SOAR, but that's not me. I'm more focused on Splunk Cloud.

My understanding was it was just Splunk. A review of Splunk in general was supposed to be conducted, but there was nothing that specified Splunk Observability Cloud, because I'm not involved with that.

Probably my favorite feature is just the integration through Log Observer, but unfortunately, the PCI requirements we have working with WestJet prevents us from fully implementing Log Observer just because when you do Log Observer, you have to sign a document that states your responsibility for PCI compliance could be broken. That was a hard sell, but we were able to work around it. Other than that, the visibility to track observability traces directly to the application logs was really cool.

It has helped improve the operational performance of our operations. As we start getting our services mapped out in observability, we've been able to bring insights into aspects of the WestJet operations that have surfaced. We recently had an outage that showed up in observability but didn't appear elsewhere. In hindsight, we were able to identify the error from inferred services with visibility into them and now we have alerting set up to notify the team. Just because of the third-party vendor that went down, we were able to show in our observability that this inferred service was not working properly. That was a huge win.

From our experience, the quality of the out-of-the-box dashboards and detectors is okay as a starting point, but we've had to do a lot more custom work. We are working on templating our observability setup for Kubernetes so that when new applications are implemented, they auto-populate existing dashboards and all related components. We're putting in significant effort to build that template out.

Looking at other tools and comparing them to Splunk, the ability to curate the data that is being ingested is a lot more labor-intensive and not as intuitive as some of the competitors. The Edge Processor that Splunk has really needs a redo to be easier to use and more intuitive for setting up custom ingestion rules to ensure PCI information such as payment card details is masked. We've seen other tools that do it well, but I am looking forward to the new Splunk upgrade, which appears to be adding a bunch of new features to the Edge Processor.

We don't have any other observability solutions, but we are kind of aware and looking at the market. The Edge Processor has been the biggest issue, and we've noticed that the integration with Microsoft isn't as strong as it could be, with limited visibility into function apps and integration with other Azure components needing improvement.

I've been using Splunk Cloud for just over three years.

Splunk Cloud has been quite stable. We did experience an outage during the Victoria upgrade, which didn't go well and caused some downtime, but other than that, it's been good.

From our perspective, it's scalable since it's a hosted solution. We haven't run into any limits based on our licensing; everything has been fine. As we increase our observability, we may have to look at expanding our licensing as more teams adopt it. A lot of our storage issues are due to not curating data, and we're currently doing a Splunk cleanup to better leverage the tool after inheriting some poor configuration.

I have contacted technical support.

Quality and speed in my case have been quite good; I've had no concerns with Splunk support.

For support, I would rate them an eight.

Splunk Cloud was already in place when I joined the company, and I got hired because of my previous experience with on-prem Splunk.

It's starting to help reduce our Mean Time to Detect (MTDD) because the visibility we gain is unprecedented, allowing us insight into applications that we've never had before.

Splunk is a very expensive tool, and I think that's one of the problems they face as competitors in the marketplace offer better value. They might need to reevaluate their pricing since competitors are catching up, and the cost is very high.

Splunk Cloud doesn't require maintenance from our end since it's hosted, but some maintenance doesn't get coordinated well with us. Maintenance is often scheduled without giving us enough time for proper change management on our side, which could be improved.

At this point, we're still in the early stages of implementing observability. We definitely see the value and potential it has, but leveraging it effectively will be crucial to justify its cost.

I am not involved with using Splunk Observability Cloud; that's a different team.

I would rate this review a seven.

I work for a managed service provider, so I have different clients that require help in assessing various tools. I work with Splunk, ScienceLogic, and Nagios most frequently because I have small clients as well.

We have Splunk Observability Cloud for some customers. The dashboards are good, and everything is nice, but unfortunately, it doesn't have long-term storage of the logs. So you need to use a data lake to store the logs.

I would like to see agentless deployment and better integration with ticketing systems like ServiceNow, which is the biggest.

We utilize the ability to enrich data with custom metrics in Splunk Observability Cloud to create tickets in ServiceNow. It is integrated with ServiceNow, but we enrich the tickets by putting the logs in the tickets and things of that nature, so it helps us. However, even that is a mixed approach. From Splunk Observability Cloud, you cannot put the logs directly in the tickets. Instead, it will create a ticket and send you an email with the logs. That integration could be improved.

Splunk Observability Cloud has helped me improve my operational performance and my customer's operational performance because we use alerting, so we find when things are not working.

I think it is very good for evaluating the effectiveness of Splunk Observability Cloud in improving digital resilience within my customer's environment.

It does provide some return on investment. It is beneficial in terms of finance to use it.

The dashboards in Splunk Observability Cloud are amazing. If you configure them correctly, they are amazing, and it is quite fast as well.

That is a very good feature of Splunk Observability Cloud because it helps us and it gives more trust in the alerts.

There are not complexities with the installation of Splunk Observability Cloud, but with the configuration of alerts and everything because Splunk has its own language in the background. You need to know Splunk in order to configure everything that you want.

It requires some in-depth knowledge of the product. It should be more plug-and-play, similar to ScienceLogic. ScienceLogic uses whatever it finds. You can use PowerShell, you can use scripts that you make. Splunk is more on the old style. It uses agents, and you have to deploy the agents.

The out-of-the-box customizable dashboards provided by Splunk are okay, but usually, I have to create new dashboards because every user wants to see something else. The out-of-the-box dashboards help to get started faster, but in the end, I will have to redo them.

I would like to see agentless deployment and better integration with ticketing systems such as ServiceNow, which is the biggest.

We utilize the ability to enrich data with custom metrics in Splunk Observability Cloud to create tickets in ServiceNow. It is integrated with ServiceNow, but we enrich the tickets by putting the logs in the tickets and things of that nature, so it helps us. However, even that is a mixed approach. From Splunk Observability Cloud, you cannot put the logs directly in the tickets. Instead, it will create a ticket and send you an email with the logs. That integration could be improved.

I have been working with Splunk Observability Cloud for about two years.

I cannot speak to lowering the cost of unplanned digital downtime using Splunk Observability Cloud because the client will get the bills. However, it reduces the downtime for systems. It improved visibility when you do changes and you do patching and you do emergency changes, so you can see if they were applied correctly or not, if the servers are still down.

If it is a new deployment and you have a medium client with about 2,000 users or computers or servers, it will take about six months just to install and configure.

The technical support is very good with Splunk.

Positive

I worked with ScienceLogic before actually working with Splunk.

There are not complexities with the installation of Splunk Observability Cloud, but with the configuration of alerts and everything because Splunk has its own language in the background. You need to know Splunk in order to configure everything that you want.

I do not spend any time personally because I have a team that does it. I have 27 people in my team.

It does provide some return on investment. It is beneficial in terms of finance to use it.

I think the pricing for Splunk Observability Cloud is still at a good price. If you are looking at Dynatrace, it is way higher.

I am familiar with the Dynatrace operator but I am not actually working with them. I am just looking into differences and tooling and what will benefit my clients better.

You need to know Splunk in order to configure everything that you want.

The out-of-the-box customizable dashboards provided by Splunk are okay, but usually, I have to create new dashboards because every user wants to see something else. The out-of-the-box dashboards help to get started faster, but in the end, I will have to redo them.

We utilize the ability to enrich data with custom metrics in Splunk Observability Cloud to create tickets in ServiceNow. It is integrated with ServiceNow, but we enrich the tickets by putting the logs in the tickets and things of that nature, so it helps us. However, even that is a mixed approach. From Splunk Observability Cloud, you cannot put the logs directly in the tickets. Instead, it will create a ticket and send you an email with the logs. That integration could be improved.

I would rate this product an 8 overall.

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.

My main use cases for Splunk Observability Cloud include Application Performance Monitoring, synthetic monitoring, and dabbling in infrastructure and what comes along with it; however, we do already have a tool that does infrastructure. We're debating about just switching it all over to Observability.

The features of Splunk Observability Cloud that I prefer the most are its all-encompassing licensing model, which is comparatively better than others in the market. We're switching off AppDynamics, and the licensing model always constrained us, so that is our main reason for switching to observability, as the licensing is all-encompassing.

The benefits of these features for my organization are significant. The license is all in one, meaning infrastructure, APM, synthetics, RUM, and the logs are all under one license, allowing us to offer that to our application teams more so than we were ever able to do before. 

We're currently trying to implement RUM, Real User Monitoring, with two applications just to get a feel for it, which we were never able to do before, since it was a completely separate license that we needed to purchase. So we're able to offer more of a full suite, more of a one-stop shop sort of thing, versus what we were able to do before.

The user interface of Splunk Observability Cloud needs a lot of work. I have been known to describe it as slapping lipstick on a pig. The pretty colors draw in everybody, however, the actual functionality of it has a lot that you cannot do, and how the user interface is organized is very difficult to navigate. This is a driving factor for us not to use the product.

The next release of Splunk Observability Cloud should include a feature that makes it so that when looking at charts and dashboards, and also looking at one environment regardless of the product feature that you're in, APM, infrastructure, RUM, the environment that is chosen in the first location when you sign into Splunk Observability Cloud needs to stay persistent all the way through. There's no reason that a user should have to keep having to restart all of their filters and select their environment anytime that they switch to a different area of the tool.

I have been using Splunk Observability Cloud for one year exactly.

I have not experienced downtime, crashes, or performance issues with Splunk Observability Cloud yet.

Splunk Observability Cloud scales with the growing needs of my organization, however, we very quickly always run into hitting the limit for custom metrics. This is something we've discovered that we have to manually manage, which is not fun, especially for large applications such as our huge tracking system, since we're a logistics company, as well as the two main revenue-generating applications. We are probably going to hold off putting them into Splunk Observability as we're constantly bumping the limit already.

I would evaluate customer service and technical support as hit or miss as I get the impression that the support folks assigned to our account might be spread a little too thin. They are good people and do good work; however, I get the impression they're spread a little too thin. If we put in a ticket, we do get a response in a decent amount of time, so that's not a problem.

Positive

Prior to adopting Splunk Observability Cloud, I used several solutions. The solutions we used include Zabbix, Splunk Core, Grafana, Prometheus, and AppDynamics, so a whole suite of things.

The deployment has been fine for cloud applications. It is very tumultuous for on-prem. That is supposed to be getting fixed over the next year. Right now, it's not there. So I always tell my management we're a year and a half too early for this tool.

I have seen ROI for our cloud applications, as we've been able to fully integrate with one application, which is a big revenue producer for the post office, and it's something that they were not able to do before, so we have been able to see that. In terms of ROI, I would say 100%.

We don't currently use the out-of-the-box customizable dashboards provided by Splunk Observability Cloud to showcase IT performance to business leaders. 

I will say we have not expanded usage to other applications since we're still stuck where we are. 

My advice to other organizations considering Splunk Observability Cloud is to wait until next year. 

On a scale of one to ten, I would rate this solution five or six.

My main use case for Splunk Observability Cloud is application monitoring.

The features of Splunk Observability Cloud that I appreciate the most are ops intel and the community support. These features have benefited my organization because they help us find the root cause of any issue quickly and pinpoint the exact location where the issue exists.

We have not yet completely gone into production, so I do not have any metrics or data points to share. To evaluate the effectiveness of Splunk Observability Cloud in improving digital resilience within my organization, we have various client applications, such as the teller application and our online banking applications. 

Initially, before Splunk, we had a long time to resolve issues. Now, with Splunk Observability Cloud, we will be able to solve them quickly and know exactly where the issue is. Previously, we needed to go to the war room to find where the issue was. Now, with Splunk Observability Cloud and all its agents and data, we know exactly where the issue is located.

Regarding the no-sample tracing feature, all the data fed by the agents to Splunk Observability Cloud means we do not have to worry about missing any issues during sampling. We have not yet explored the AI-powered analytics feature, but we have partially explored MLTK.

My teams have utilized the ability to enrich data with custom metrics by writing custom agents in Java and Python to collect those custom metrics and feed them into Splunk Observability Cloud. This is particularly useful for applications without direct Splunk agents.

The out-of-the-box customizable dashboards are helpful in showcasing IT performance to business leaders. They provide guidance on requirements we may not have visualized and help us build custom dashboards to include our company-specific metrics. We have not yet expanded usage since we haven't started using it extensively.

To improve Splunk Observability Cloud, we need more applications to be included in the observability so that more applications can have agents to monitor them and bring that information to the cloud. 

Splunk Observability Cloud has not yet completely improved our operational performance for our company's resilience as we are just starting out, however, it will help us ultimately to reduce incident time.

I have been using Splunk Observability Cloud for one year now.

In my experience until now, I have not experienced any stability issues with Splunk Observability Cloud.

Splunk Observability Cloud scales effectively with the growing needs of my organization. As we are a growing company transitioning all our applications to the cloud, and with the increasing number of cloud-native applications, Splunk Observability Cloud will help us achieve digital resiliency and reduce our mean time to resolution.

I would evaluate customer service and technical support as excellent, as Splunk has been quite responsive to our service requests, with their team providing good support.

Positive

Prior to adopting Splunk Observability Cloud, we were using Splunk Enterprise, and we had custom monitoring tools developed in-house.

The installation of Splunk Observability Cloud worked smoothly once we figured out the initial issues. The agents do not consume many resources, and the type of metrics they collect is helpful.

Since we have not progressed far into the implementation of Splunk Observability Cloud, I cannot comment on the return on investment at this time.

I am not involved in the experience with pricing, setup cost, and licensing.

I rate Splunk Observability Cloud eight 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.

My main use case for Splunk Observability Cloud is end-to-end tracing of business processes.

Splunk Observability Cloud has helped improve my operational performance and my company's resilience.

What I appreciate most about Splunk Observability Cloud is the correlation feature, specifically the ease of correlating logs and issues to those traces to see where within the path of the business function is failing. 

One significant way these features benefit my organization is through mean time to resolution. Taking away that first instinct of where we're trying to figure out what's wrong will drop that time significantly, so rather than a few hours, potentially we're looking at a few minutes before we can start resolving an issue.

In terms of operation performance and resilience, I have experienced improvements in mean time to resolution and the ability to detect issues that we weren't detecting ahead of time. I can give a specific case scenario; while we were in a POC situation, it was able to find issues we were having with servers just by random chance. We implemented it and it happened to let us know about an issue before our NOC even knew that it was occurring.

My teams have utilized the ability to enrich data with custom metrics in Splunk Observability Cloud during the implementation process, and we're definitely seeing a huge difference in what data we have, and the teams are extremely excited by the new amount of data we're getting in. I find the out-of-the-box customizable dashboards provided by Splunk Observability Cloud extremely beneficial since they give you a lot of information already, and the ability to customize and do your own is even better.

I ran into a small security incident. Splunk Observability Cloud was able to help us with that along with Splunk's core process or core offering. Between the two, we were able to use it for correlations, which helped with mean time to resolution and getting us back up and running much faster.

Splunk Observability Cloud could be improved with better integration with AppDynamics, as we know that's coming, however, it is an issue we've had between the OpenTelemetry and the AppDynamics collector. We saw a complete difference in what data was being brought in, however, we know that issue is being resolved and that's a big one for us.

I would assess the stability and reliability of Splunk Observability Cloud as okay. We've been experiencing an issue with the cloud console, and we're working with support to get through that. We're assuming it's just a growing pain at this point. Particularly what we're having is disconnection from the cloud console, where we will be working in it and receive a message saying that we've been disconnected and have to wait for it to come up. It's been painful and seems to be a new issue, and they're trying to figure out what's going on,however, I haven't heard of anybody else having that issue.

Splunk Observability Cloud seems to be scaling quite well with the growing needs of my organization.

I would evaluate the customer service and technical support for Splunk Observability Cloud as fantastic. On a scale of 1 to 10, the customer service and technical support deserve a 10.

Positive

I have seen a return on investment with Splunk Observability Cloud.

I would advise other organizations considering Splunk Observability Cloud to definitely POC it to see if it's going to work for their situation. It may not be for everybody. That said, definitely give it a chance and see what it can do for you and the kinds of new information it can bring in for you.

On a scale of one to ten, I rate Splunk Observability Cloud nine.

In our organization, we are using Splunk Observability Cloud for real-time monitoring and troubleshooting of our applications and the infrastructure performance, tracking metrics such as CPU usage, memory, latency, and the services of different microservices which we run for our applications and products.

The best features from Splunk Observability Cloud include the high-level dashboard for clear visibility of our infrastructure and the product, as well as the detailed traces for the request flow of our APIs and the in-between application communication. From the detailed traces, we can know where our application fails, allowing us to solve incidents very easily, which has drastically reduced the MTTR of our application.

I find the out-of-the-box dashboards very helpful. Although we have not done much customization yet, the out-of-the-box dashboards and detection capabilities include pre-built dashboards for common services and infrastructure components. We have not used them extensively, but we customize them for our organization's needs, and we also adapt the detectors for alerting purposes.

I find the AI-powered analytics very helpful because we have also used other observability platforms such as SignalFX, where the AI-powered analytics is not built into the application. Here, the AI provides intelligent insights and very early anomaly detection and pattern recognition, automatically informing us of highly unusual behavior in the application before any incident or outage occurs during production.

One area that has room for improvement is the pricing; as I mentioned, it can be expensive due to large data volumes. Also, the pricing can be unpredictable, and if it were more predictable, the organization would be more comfortable with it. Additionally, I found the learning curve quite steep when I started using Splunk Observability Cloud; it took me some time to learn it. I also think that while our team is large enough to utilize it, smaller teams might not prefer this solution.

We have not started customizing Splunk Observability Cloud yet according to our needs, but we plan to in the next weeks. We have used the basic customization features, and I believe it is customizable.

I have been using Splunk Observability Cloud for the last one year; I have joined my recent organization from the last three to four months, where I have been using it from the last three to four months.

The stability and reliability of Splunk Observability Cloud is top-notch, as we have not faced much downtime, so I would rate it nine.

The scalability of Splunk Observability Cloud is also very good; we can ingest any data we desire, so I would rate that nine as well.

I rate the technical support as very proactive, and our doubts and queries are resolved properly, so I would give it a rating of five.

Before using Splunk Observability Cloud, we had used SignalFX and considered vendors such as Datadog and New Relic. We chose Splunk Observability Cloud because of its vast features, the visibility we gain from the dashboard, the AI integrated into the platform, detailed traces, and logging capabilities. While Datadog and New Relic are also good, Splunk Observability Cloud is better in certain areas.

The deployment part was handled by the other developers and ops engineers in my organization, but I know the initial setup for Splunk Observability Cloud is simple and very easy.

The deployment part was handled by the other developers and ops engineers in my organization.

From an ROI perspective, Splunk Observability Cloud offers much higher value because, as I mentioned earlier, our MTTR has reduced by more than 50%, which decreases the overall downtime for our application. When there is an outage, the time to resolve is shorter, and application uptime has also increased because of it. This improvement is the main reason for using Splunk Observability Cloud; we wanted to decrease our application downtime. Additionally, the visibility provided by the dashboard helps us understand where our application has failed.

Before using Splunk Observability Cloud, we had used SignalFX and considered vendors such as Datadog and New Relic. We chose Splunk Observability Cloud because of its vast features, the visibility we gain from the dashboard, the AI integrated into the platform, detailed traces, and logging capabilities. While Datadog and New Relic are also good, Splunk Observability Cloud is better in certain areas.

I have not used the no-sample tracing feature yet, so I am not sure about that.

I would say it takes around one month to learn Splunk Observability Cloud; it varies from person to person, but that was my experience in learning all the features and use cases our organization employs.

Our company is not deeply involved in LLMs and GPUs for AI applications; our applications mainly run on normal Java processes on standard servers, not on GPUs or LLMs yet. We are in the process of developing our capabilities in AI later on.

We are using normal servers as a cloud-based solution, but we still have some drawbacks, mainly the pricing part, as smaller teams may not find it suitable, and the pricing model is complex while the learning curve is steep, particularly for the SignalFlow query language.

My advice for anyone considering this solution is to opt for Splunk Observability Cloud without any hesitation, as it can drastically decrease the mean time to resolution and mean time to detect any issues in their applications. The overall visibility of the organization, including application usage and memory metrics, is clearly presented on the dashboard, allowing insights into what went wrong and when. Although the learning curve can be challenging initially, users will adapt and find it very beneficial for their organization.

I would describe the pricing as neither too high nor too low; however, if it could be cheaper, it would be beneficial for us since sometimes due to large data volumes, it can be expensive for the organization to track large datasets, as it charges for large volumes of data. Sometimes it can be costly if the data we are receiving is irrelevant.

Our organization has between 200 to 500 people, and I believe that more than 100 people are using Splunk Observability Cloud, including developers, ops engineers, security engineers, and others. I am not certain of the exact number, but it is definitely more than 50.

I would rate this product overall at a nine.