Splunk Observability Cloud Reviews

I have been using Splunk Observability Cloud for five years now.

My main use case for Splunk Observability Cloud includes infrastructure monitoring, synthetics, APM, and RUM. For infrastructure monitoring, Splunk Observability Cloud serves as my primary tool for monitoring all the hosts or VMs that my technical organization provides for the rest of the company.

Regarding synthetics or RUM, they are used on a case-by-case basis because I do not want to turn them on for everybody. That is not part of my standard offering, where any applications that are internet facing that require more traceability and more digital experience are when those parts are used. IAM is my primary module that caters to almost 15,000 plus servers in the company.

The best features that Splunk Observability Cloud offers are the ease of configuration and the UI, along with the navigation which is simple to grasp for a non-Splunk user.

What makes the UI and navigation easy to use in Splunk Observability Cloud is the simplicity and the layout of the modules.

Splunk Observability Cloud positively impacts my organization as it is my primary infrastructure monitoring tool, and I believe it helps avoid outages related to OS and systems. It saves hours and hours of potential downtime or efficiencies using that platform.

Since using Splunk Observability Cloud and the Ansible Automation Platform, the overall mean time for resolve has reduced by 30x, averaging from 120 minutes down to 6.2 minutes, and I have potentially avoided 12,000 hours of downtime.

I believe that the ability to implement RBAC eventually will be a good thing for Splunk Observability Cloud. I know the product is slowly evolving in that area, but the sooner the better.

To improve Splunk Observability Cloud, I think seamless integration into Splunk Cloud, ensuring that there is a single UI for customers to use both products, is what will be a winner.

I think being able to deploy and manage the OpenTelemetry agents at scale is important for Splunk Observability Cloud. Upgrading or fixing and restarting that agent takes a lot of time and effort, so being able to make that experience seamless would help.

I chose eight out of ten because I think there are still improvements needed, such as fleet management and unified navigation. Once those are in place, I think it would definitely be a solid ten.

I find Splunk Observability Cloud stable so far.

Splunk Observability Cloud's scalability is pretty flexible and good.

Splunk Observability Cloud's customer support could be better as I think response times are pretty slow compared to the Splunk Cloud product. I would rate Splunk Observability Cloud's customer support a three out of ten.

I previously used an old tool called OSS that was rudimentary and utilized old industry standards, so I changed to Splunk for more modern industry standards.

Regarding my experience with pricing, setup cost, and licensing for Splunk Observability Cloud, I think pricing could be a little more flexible in terms of being able to draw from a pool license or being charged for what I consume versus buying a fixed count ahead and having to revise the licensing over and over year-on-year.

Before choosing Splunk Observability Cloud, I evaluated other options including Sumo Logic, ScienceLogic, LogicMon, and a couple of others.

I have not used Splunk Observability Cloud's AI capabilities enough to comment on its governance and security.

I have not used that product related to its AI capabilities, so I do not have any thoughts or experiences regarding accuracy and reliability of output.

I use AWS as my cloud provider.

I did not purchase Splunk Observability Cloud through the AWS Marketplace.

Splunk Observability Cloud has helped improve my operational performance and my company's resilience. Since it is my primary infrastructure monitoring tool, it is how operations get notified about potential degradations to the servers they cater to.

It is important for my organization that Splunk Observability Cloud has end-to-end visibility into my cloud-native environments, though I am more focused on-premise versus cloud-native. Cloud-native is still important, but my focus is primarily on-premise.

I assess Splunk Observability Cloud for helping my organization scale as effective because it determines what growth I would expect in terms of OS and server counts year on year. Being able to have a flex license pool model which does not restrict me to a certain number of hosts would be helpful.

I am not sure how I could calculate if Splunk Observability Cloud has helped reduce my mean time to detect (MTTD), so I do not know.

My impression of Splunk Observability Cloud's out-of-the-box dashboards and detectors is that they are good and easy to set up with less learning curve to get those up and running.

I assess this solution with an overall rating of eight out of ten.

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.

Splunk Observability Cloud is primarily used for expanding APM and IM monitoring, correlating metrics, traces, and logs for better insights, and reducing MTTRs and MTTDs. In the SRE space, especially for digital mobile technology, it is set up to gather metrics and traces from applications. The RUM component is used to understand customer behavior, with traces and metrics ingested via the Splunk OTEL collector and RUM embedded through the Splunk RUM SDK. Dashboards have been built on this data to enhance correlation.

The main use case is seeing the correlation of metrics, logs, and traces in a single pane of glass, which we have successfully achieved.

The best features of Splunk Observability Cloud include the APM service map and real-time RUM monitoring, which track customer experiences. The service map helps understand the journey and identify pain points in services during issues. Correlating real user monitoring through RUM has significantly enhanced our understanding of customer pain points, allowing visualization of issues under APM.

Splunk Observability Cloud positively impacts our organization by enabling us to build dashboards around customer experiences and API backend layers. Previously, there were many hops with open-source tools like Grafana and Prometheus. Now, with metrics and traces consolidated, we gain a single view for correlation, significantly reducing MTTD, enabling detection of issues in under five minutes.

From an admin perspective, there are pain points such as difficulty exporting data into Excel and a cumbersome setup process. Improving the setup for data uploads via Excel and providing an option to download RUM data for analysis would be valuable improvements.

I have been using Splunk Observability Cloud for the last eight months.

Splunk Observability Cloud is stable.

We are expanding from one to ten applications next year. While there are no performance issues in viewing dashboards or when detectors fire, challenges remain with repetitive, manual setup processes. There is no templatization for quick growth, a feature we would love to see.

Customer support for Splunk Observability Cloud is adequate; it meets our needs but is not exceptional.

We previously used a combination of Grafana and Prometheus along with Splunk Enterprise. The limitations of these tools, coupled with the introduction of database monitoring and native Splunk OTEL capabilities in Splunk Observability Cloud, drove our transition.

There is a return on investment in terms of time saved. Traditional debugging required multiple interfaces and hours of effort, while now, with Splunk Observability Cloud, MTTD has decreased significantly, reducing investigation times from several hours to a maximum of thirty minutes.

My experience with Splunk Observability Cloud's pricing, setup cost, and licensing is positive.

We evaluated options including DataDog, Dynatrace, and Grafana plus Prometheus. DataDog is advantageous but expensive, making Splunk Observability Cloud the right fit for our budget.

My advice for those considering Splunk Observability Cloud is to ensure readiness in terms of data and integrations; simply using it for APM will not yield significant benefits. It is important to have a well-prepared OTEL strategy and to use RUM for its exceptional correlation capabilities.

Splunk Observability Cloud will help our organization scale over time, particularly with features related to database monitoring. SRE adoption and technical teams, such as DBAs, will benefit from more depth in database monitoring. Expanded adoption of Splunk Observability Cloud is due to its significant advantages.

It is very important for our organization that Splunk Observability Cloud offers end-to-end visibility into our cloud-native environments. Comprehensive visibility through improved adoption can significantly enhance operations.

We encountered significant challenges implementing Splunk AI assistant in the bank due to governance and security procedures. The absence of a BYOK model raises concerns about data exposure outside the environment.

We have not yet used Splunk AI capabilities in a production environment, so from our test environment perspective, we cannot assess accuracy until we implement AI capabilities in production. I would rate this review an 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.

We just started implementing Splunk Observability Cloud in my company as an ongoing project.

Our main use case for Splunk Observability Cloud is for our digital team for our website healthpartners.com, and that's the reason we decided to bring in a unified monitoring platform instead of using different monitoring tools. We are working with other teams as well at the same time, bringing their metrics and traces into Splunk Observability Cloud from other monitoring tools.

The other use case that we're working on regarding Splunk Observability Cloud is synthetic monitoring, and right now, we have some legacy solutions that have been used for the health checks, browser tests, and API tests, which have been moved over from a different tool to Splunk Observability Cloud. There is also one team that I'm currently talking to where we have set up the health checks in a legacy way using REST API inputs on the heavy forwarder, and we are planning to move that to Splunk Observability Cloud as well.

In my opinion, the best features Splunk Observability Cloud offers are the dashboards and some of the alerting features that I appreciate. I have also just found out that they are adding the incidents feature, which is really interesting, and I would to know more about that as well in the alert section to group all the alerts in a single incident, so that is going to be great.

For the alerting features of Splunk Observability Cloud, right now, we have been setting detectors for all the synthetic monitoring tests that were set up and also some of the host metrics, and we are testing it out, notifying those alerts and sending them to the teams, and that has been helpful even for our team. For the dashboards, so far, there is one dashboard that was built for us, the gateway monitoring dashboard where we send metrics via the gateway, and when something happens on the gateway side, if there's a delay or if the metric stops streaming into Splunk Observability Cloud, we can check the dashboard, so I appreciate that feature too.

Regarding the features of Splunk Observability Cloud, maybe the fleet management is what I'm looking forward to, as I know that feature has been added recently and we are able to see the gateway collectors showing up on there. I am more interested to see how the OTel collectors or anything else show up in there, and how that can be useful for our team.

I don't think I've seen any missing features of Splunk Observability Cloud so far, as I'm still exploring it and learning as we work on the project, so I think it is going to be great.

I can't think of anything at this time, but the new incident feature is great, and I heard for the DB monitoring, there is something that is going to be added in Splunk Observability Cloud where we can add custom attributes in there, which might be helpful in the future.

Splunk Observability Cloud is stable in my experience so far.

I cannot say with certainty at this time regarding Splunk Observability Cloud's scalability, as we're still in the middle of the project, so I don't know how scalable this is. Right now, there's one task that I'm working on to bring in the SQL servers for DB monitoring into Splunk Observability Cloud, and right now, we don't have licensing for it, so we are working on getting the licensing. I would love to see once we have those metrics into Splunk Observability Cloud, how that is going to work and how scalable it is.

I did interact with Splunk Observability Cloud's customer support, and it is working and is good so far.

There are multiple different tools that have been used in my company; it's not just one. We have Foglight, Prometheus, Grafana, and we are moving towards having a unified monitoring tool or unified solution, which is the reason for the switch or migrating over.

I was not involved in any discussions about pricing, setup cost, or licensing for Splunk Observability Cloud, so I have very minimal experience with that.

I haven't seen any return on investment yet, but I do definitely expect to see ROI soon, once we bring in everything and reduce workload.

I was not involved in any discussions about pricing, setup cost, or licensing for Splunk Observability Cloud, so I have very minimal experience with that.

I am not sure if any other options or proofs of concept have been done before choosing Splunk Observability Cloud, as it was a higher-ups decision to go with it.

I have heard from one of the teams when they set up the synthetic monitoring, so they gave really positive feedback to us, which was good. The other teams, the problems they had with the other monitoring tools, I feel have been solved with Splunk Observability Cloud to some extent, so that is going well so far.

I think one of the issues that they had was with the alerting from their tool, and there is this one person who works with the developer teams and gathers all the information regarding the browser tests and API tests. He sets it up for the alerting; right now, we have the ServiceNow webhook integration that was set up to notify or generate the ServiceNow incident, and the way it was set up in Splunk Observability Cloud is that whenever the incident clears itself, it will notify the team, but it won't close the ticket out, so they can take a look and have human eyes on it before they close it out, and that was positive feedback that was given to me.

Regarding Splunk Observability Cloud's AI capabilities, we haven't had the AI capabilities enabled for our instance yet, so we have not had the AI assistant or any other features, but I'm looking forward to learning more or understanding the AI capabilities once we deep dive into the project.

For others looking into using Splunk Observability Cloud, I would first recommend taking a basic education course; if it is a user, maybe a free course or a basic course to understand how Splunk Observability Cloud works and what features are inside it. If it is a power user or admin, I think we need to provide the documentation to the users on how easily they can log in, go between the features, and understand where they can find the dashboards, alerting, metrics, and so on.

I'm looking forward to implementing, completing the project, and starting to use it, as well as our users starting to use Splunk Observability Cloud soon. I would rate this product an eight out of ten.

The primary use cases for Splunk Observability Cloud in our environment include log monitoring and detection controls, especially for the DI detection controls, and the application team uses it for all operational activities, to find issues, and to detect more issues before going live.

The resolution time is easy; it has led to a reduction of the MTTR for any incident, and it proactively identifies bugs before they go live for application teams, making issue identification simpler.

It helped us a lot in reducing the cost of unplanned digital downtime.

The strong points of Splunk Observability Cloud are an easy and consolidated view in one area, as we can query the log, customize it, and create some dashboards, which makes it easy for our operations team to detect the issues.

My impression of the No-Sample Tracing feature in Splunk Observability Cloud is that it has definitely helped us.

It has assisted in terms of eliminating blind spots in data collection.

The out-of-the-box customizable dashboards provided by Splunk Observability Cloud are definitely good; the customization is extensive, and it's really helping us based on my understanding.

Splunk Observability Cloud has indeed helped me improve the operational performance and resilience of my company.

Regarding drawbacks, something on the downside exists, but I have not elaborated on it yet.

Currently, we have not yet enabled the AI module in Splunk Observability Cloud, as we are just in the process of using the latest version. I can comment on that later, but I personally feel it really helps us, especially when we are running some SQLs, as it's guiding us a lot to optimize queries and provides many actions, particularly for those who work on SQL.

My team has not been able to enrich data with custom metrics provided by Splunk Observability Cloud.

I have been working with Splunk Observability Cloud for almost more than five years.

I would rate the technical support of Splunk Observability Cloud an eight; they are quite responsive.

Before adopting Splunk Observability Cloud, we did think of alternatives from different vendors, including some evaluations and reviews of several products, with Dynatrace being one of them.

Regarding deployment, it's quite simple; I don't see any difficulties.

As for time frames, it depends on the environment, but I would say if it's a very simple environment, it would be a few hours. For us, it's always days because it's a very large environment.

We do participate in the deployment process; we do have a dedicated team for that.

Globally, we have around 20 to 30 people in my team who are responsible for the implementation of solutions.

It's a combination of engineers, developers, and administrators; it's even more than that, so it's a very large environment.

You could see some improvement, and definitely there will be some return on investment, but not directly through my team; perhaps for the applications, which have multiple tools that can use this one consolidated platform for all log monitoring.

Price is always one of the factors; it's a little costlier when compared.

We purchase Splunk Observability Cloud products directly from the vendor, from Splunk itself. The overall review rating for this product is 8.

As part of the monitoring team called ESOC, Enterprise Security Ops, we use Splunk Observability Cloud to have a bird's eye view on all the applications and infrastructure, observing how they connect with each other and how workflows among them occur. When we detect incidents, we deep dive into Splunk Observability Cloud to see the flow of the signals and identify possible connection issues with other systems they're connected to. We have been using Splunk Observability Cloud extensively these days.

At Progressive Insurance, we have a huge claims presence where the applications related to claims are high priority. Recently, we encountered an issue with claims documentation, which resulted in disruptions that affected applications using documentation, especially claims documentation, causing them to struggle. During this time, Splunk Observability Cloud was incredibly helpful as we drilled down into root cause analysis to assist those application teams in identifying what could have gone wrong—whether it was API calls, network issues, or pods being down. It took some time, but Splunk Observability Cloud really facilitated our investigation.

Seeing the whole signal flow aspect was really beneficial. We could visualize how the signals moved from one module to another, even with inferred systems, and that visibility assisted us in our analysis significantly.

One great feature of Splunk Observability Cloud is how it transitions from monitoring to observability. While monitoring involves looking at potential issues and health checks, observability allows us to view the system more from a signal flow perspective, and the visuals are much better. I can see the system clearly represented in front of me, allowing for better visualization and flow assessment. I hope to benefit even more from this at the conference.

An example would be how we analyze the interconnections between applications and pods, monitoring pod health and determining whether a pod is in a crash loop or just down, or if an application has an excessive number of pods down, which helps us present data more effectively.

I am continuously exploring more about it. Although I have been working with it for around two years, I am eager to learn more, especially with the incorporation of AI, which excites me about using Splunk Observability Cloud further.

I believe increased adoption is essential. While usage is steady now, the more application teams embrace it and avoid working in silos, the easier it will be to create a more cohesive structure.

We are relatively new to this, and I aim to explore further, especially during classes and sessions at the conference. I am thrilled about how AI is being incorporated to enhance dashboard functionality and visibility. Once we become more seasoned, we will grasp more about the necessary improvements.

Regarding the AI capabilities of Splunk Observability Cloud, I think its governance and security need much exploration. I am uncertain about what governance teams are doing within my company, but there are growing conversations about backdoor channels and misuse, emphasizing the necessity for strict guardrails and governance as key factors for adoption.

I have been using Splunk Observability Cloud for two years.

Splunk Observability Cloud is stable.

So far, its scalability at the user level has been good, but I am uncertain about the configuration or administrative level.

Customer support has been really good.

On a scale of one to ten, I would rate customer support an eight.

Previously, we used AppDynamics and Splunk Enterprise. We have not fully switched yet, but we are growing toward observability.

Time saved is definitely a factor. While I do not decide on monetary matters within the company, I can confirm that there has definitely been a time-saving aspect.

I advise others looking into using Splunk Observability Cloud to practice more, explore extensively, and aim to customize as much as possible according to their needs. I would rate this review a seven out of ten.

My main use case for Splunk Observability Cloud is infrastructure monitoring in the cloud.

An example of how I use Splunk Observability Cloud for infrastructure monitoring is mainly infrastructure alerts, in particular virtual machines, when there is a degradation in the system or an alert or event. Splunk Observability Cloud actually surfaces those.

The best features Splunk Observability Cloud offers include the ability to see all the various hosts, not only private cloud but also cloud services as well.

Having visibility across both private cloud and cloud services with Splunk Observability Cloud has benefited my work significantly, especially when trying to tell the story around particular incidents or issues and when they happen. In particular, anytime there is an adverse event that impacts the company, whether it is an infrastructure issue or a cloud issue, being able to get to root cause as quickly as possible has been extremely helpful.

Splunk Observability Cloud has positively impacted my organization by allowing us to have visibility into the environment. Historically, that has not always been the case. Once we implemented Splunk Observability Cloud, it definitely allowed us to get to meantime to identify and meantime to detect as quickly as possible.

Splunk Observability Cloud has helped improve my operational performance and my company's resilience. The biggest improvement is really improving uptime and reliability of the systems, getting to root cause as quickly as possible, minimizing impact as it relates to outages, and then being able to recover from those as quickly as possible.

I think the biggest improvement in Splunk Observability Cloud that Splunk is currently working on is just unification across all their products, not just Splunk Observability Cloud, but a single pane of glass to allow the engineers and operators to stay in one location.

I have been using Splunk Observability Cloud for approximately four years.

Splunk Observability Cloud is stable.

Splunk Observability Cloud has been able to meet all my use cases, so from a scale standpoint, I have not encountered any problems or issues.

Customer support has been great with Splunk Observability Cloud. The Splunk system team has always been there whenever there is an issue or problem. They have actually been very helpful in resolving those issues.

I would rate the customer support for Splunk Observability Cloud a 10.

I did previously use a different solution, which I believe was a custom-made solution built in-house. The reason for switching was mainly due to cost, and not only cost, but additional resources we had to add in order to customize that solution.

I cannot say whether I had a return on investment. The return on investment is probably mostly tied to operational efficiencies that I discussed before. I cannot say I have seen a reduction in employees needed. I do have to have added additional resources to actually help support observability and Splunk Observability Cloud, but most of my efficiencies have come in being able to identify and detect issues when they happen.

I do not get involved with pricing; my procurement organization deals with that. As it relates to setup, procurement actually gets involved and negotiates that. So I am not as intricately involved with that as my procurement colleagues.

I cannot say whether I had a return on investment. The return on investment is probably mostly tied to operational efficiencies that I discussed before. I cannot say I have seen a reduction in employees needed. I do have to have added additional resources to actually help support observability and Splunk Observability Cloud, but most of my efficiencies have come in being able to identify and detect issues when they happen.

I did evaluate other options along with Splunk Observability Cloud, with ScienceLogic being one of those. ScienceLogic and Splunk Observability Cloud were the two that were evaluated.

My advice to others looking into using Splunk Observability Cloud is to clearly define your use cases and your intent, and then gradually work towards those. I would recommend not trying to take on too much at one time. Instead, take on small use cases at a time and then build on those. Then probably the last thing is to continue to go back and check to make sure Splunk Observability Cloud is actually meeting its intent and its need and actually capturing the right signals for your organization. I would rate this product a 10.

I started using Splunk Observability Cloud approximately one to two months ago. My main use case for Splunk Observability Cloud is for cloud-native workloads and OpenTelemetry. I am using Splunk Observability Cloud for Kubernetes workloads, and with OpenTelemetry, I want to determine if I can have the open source enabled.

The best features Splunk Observability Cloud offers are agent observability and the AI SRE agent, which appears promising. With agent observability, I can have the agent application observe with a full-stack view, and with the SRE agent, I can perform automatic root cause analysis and deep dives. I am about to start with Splunk Observability Cloud through a proof of concept, and it looks good.

The faster I adapt and implement all the changes in Splunk Observability Cloud would be helpful. I believe it is still in the early days of using Splunk Observability Cloud, so I need to see how the product works. I need to test the accuracy and reliability of output from Splunk Observability Cloud after the proof of concept. Overall, Splunk Observability Cloud needs to cover all native and legacy monitoring, and all the gaps I have seen with existing platforms need to be embedded along with business observability to help my organization scale.

I started using Splunk Observability Cloud approximately one to two months ago.

Splunk Observability Cloud is stable.

I will automate Splunk Observability Cloud through the CI/CD pipeline, so it should be fine in terms of scalability.

The customer support for Splunk Observability Cloud is excellent. I would rate the customer support of Splunk Observability Cloud a nine on a scale of one to ten.

I had been using AppDynamics before choosing Splunk Observability Cloud.

Splunk Observability Cloud is still being deployed in my organization in a public cloud.

I did not purchase Splunk Observability Cloud through the AWS Marketplace; I bought it through the vendor.

At this moment, Splunk Observability Cloud has not helped improve my operational performance or my company's resilience. I have not yet seen a return on investment from Splunk Observability Cloud.

The pricing for Splunk Observability Cloud looks fine, and I believe the overall proposal that was provided looks great.

I did not evaluate other options before choosing Splunk Observability Cloud.

My advice to others looking into using Splunk Observability Cloud is that the tool appears promising, so I recommend pursuing it. Splunk Observability Cloud is still in its early days, and I need to see how it works over time. I am positioning Splunk Observability Cloud to be one of the bigger drivers for full-stack observability, and it is very important for my organization that it has end-to-end visibility into my cloud-native environments. My impression of Splunk Observability Cloud's out-of-the-box dashboards and detectors is that the visualization looks excellent, and the implementation is straightforward. I would give this product a rating of nine out of ten.

I work in data analytics with experience in monitoring systems and working with large-scale data. I have used Splunk Observability Cloud in the context of real-time monitoring and performance tracking.

Splunk Observability Cloud works well alongside Splunk Enterprise for logs and integrates with cloud platforms and monitoring tools. It is often used together with other observability solutions. The tracking metrics such as latency, error, and throughput are easily visible. I can also build dashboards for real-time visibility.

We use Splunk Observability Cloud to track latency metrics and identify where slowdowns are happening. We have visualized response time trends and quickly detected performance degradation. We have also used it for infrastructure monitoring. Over the past six months, we have been monitoring metrics such as CPU usage and memory. If there is unusual usage, we identify it quickly using this tool and take action before it impacts our performance.

Splunk Observability Cloud has optimized our solutions and helped us understand the metrics. The AI-powered guidance in Splunk Observability Cloud helps us identify patterns and anomalies in system performance data. Instead of manually going through a large volume of metrics, it highlights unusual behavior and potential issues automatically. This makes it easier to detect problems early and understand where to focus, especially in complex systems.

There is definitely log analysis and dashboards. Log monitoring and dashboards have been better using Splunk. Splunk Observability Cloud is the best tool for log monitoring and dashboards. Splunk Observability Cloud feels more focused on real-time metrics and performance tracking compared to some other traditional log-based tools.

The learning curve for understanding all features should be improved, and the cost can increase. Splunk Observability Cloud is very costly. Cost is one of the drawbacks.

Sometimes too many alerts, if not configured properly, is a major drawback that could be improved.

The prices are quite high. As I have mentioned earlier, we are Splunk partners, so this has been handled by my other team. However, for other companies and small startups, the prices are very high for them to use Splunk Observability Cloud. Price is a concern.

I have been working with Splunk Observability Cloud for the past six to eight months.

We have expanded our team and usage. We are scaling up right now from ten people to twenty-five or thirty. Over time, I expanded my usage by going through basic monitoring and exploring things like setting up custom dashboards. We have gradually expanded our usage from setting up dashboards and alerts.

For customer service, I would rate them eight out of ten because whenever we raise a support case, they are always available for us.

For Splunk real user monitoring, implementation took time because our engineers tried very hard. In case of support, there should be more engineers specifically for this case.

We have used different products like Palo Alto and Cribl before moving to Splunk Observability Cloud. As we got a partnership, we have shifted to Splunk Observability Cloud.

The information is confidential and I cannot share specific details. However, I can tell you in percentage that fifty to sixty percent of our work has been easy to identify in terms of performance metrics and performance using Splunk Observability Cloud.

It has saved us thirty to forty percent in cost because we used some other tools before that were more costly. As we are Splunk partners, we obtained Splunk Observability Cloud, and our costs have been reduced by thirty to forty percent using this solution.

My overall impression of using Splunk Observability Cloud is that it is a strong tool for real-time monitoring. It does take some time to get fully comfortable with all the features. We have not explored everything right now, but in the future, we are looking forward to using more features.

A part of the implementation has been handled by my other team. I have explored using custom metrics to enrich observability data, mainly by adding application layer or business-related metrics alongside system metrics. I have used custom metrics in a limited way to add more context to monitoring, such as tracking application-specific metrics alongside system data.

Dashboard customization in Splunk Observability Cloud is quite flexible. We care about metrics in different types of visualization, and it helps us organize them in a way that makes sense for monitoring. It allows us to build dashboards tailored to specific use cases. This makes it easier to monitor system performance and quickly identify issues without going through unnecessary data.

The integration in real user monitoring from Splunk Observability Cloud is actually better than from some other tools. If you are looking for the best SIM tool, then Splunk Observability Cloud is for you. If you have funds and capability for the cost, then Splunk Observability Cloud is definitely the best tool you can use.

I have given this review an overall rating of nine out of ten.