Splunk Observability Cloud Reviews

My use case for Splunk Observability Cloud is primarily for monitoring and cloud management, and it serves us well.

The best features in Splunk Observability Cloud that I appreciate the most include its comprehensive monitoring capabilities and its user-friendly interface.

The solution has significantly helped improve my operational performance and my company's resilience by providing real-time insights. The enhancements to my operational performance and resilience are noticeable.

It has saved me a considerable amount of time and resources by streamlining our monitoring processes.

My impression of the AI-powered analytics and guidance provided by Splunk Observability Cloud is that they are very effective and enhance our decision-making.

I do use the no-sample tracing feature to eliminate blind spots in data collection, and it is quite helpful.

My team has effectively utilized the ability to enrich data with custom metrics to improve our analytical capabilities.

The out-of-the-box customizable dashboards are effective, and they help showcase IT performance to business leaders quite effectively.

In Splunk Observability Cloud, the areas that have room for improvement include usability enhancements to make it even better.

I have been using Splunk Observability Cloud for a considerable time, and I can share my experience with it.

Regarding stability, I would rate the stability of Splunk Observability Cloud as a 9, indicating it is very reliable. Splunk Observability Cloud performs exceptionally in terms of stability under varying conditions.

From 1 to 10, I would rate the technical support as an 8 since it is generally responsive and helpful.

The solution was purchased through a partner, and my experience with the partner has been generally positive. My experience with the partner has been satisfactory as they provided the needed support throughout the process.

My experience with lowering the cost of unplanned digital downtime has been positive as it has indeed reduced downtime.

Regarding the pricing of Splunk Observability Cloud, while I believe it can be improved, I would rate it around 7, leaning towards being expensive.

I would compare Splunk Observability Cloud with other solutions as more feature-rich and user-friendly based on my concerns.

For others looking into this product, I would recommend trying it out with a proof of concept to see its benefits firsthand.

Approximately 50 users in my company use Splunk Observability Cloud to leverage its capabilities effectively.

The solution does require some maintenance, but it is quite straightforward in managing it.

In terms of my company's relationship with Splunk, we are currently a customer making the most of their offerings.

I would rate Splunk Observability Cloud a solid 8 from 1 to 10 based on my experience and satisfaction with its performance.

I am using Splunk Observability Cloud as a log-based monitoring tool for my databases. We have ingested our database logs and OS system logs into Splunk Observability Cloud and are creating dashboards and alerting features over those alerts. One of my major use cases is that all kinds of databases I am currently working with have database logs that capture all information, warnings, and error messages. These database logs are moving to Splunk Observability Cloud. The first use case is that I no longer need to maintain a long list of flat files on my server for all those logs. Those can be directly ingested into Splunk Observability Cloud. The benefit I am seeing from here is that I can get pattern-based analysis of what kind of errors I am commonly getting and what the date patterns of those errors are. I can get dashboards over that and I can also create alerts. I can also incorporate those alerts with some back-end Git workflow for automatic remediation. This is one of the solutions.

Another use case for Splunk Observability Cloud that we are seeing is that there are multiple times when there is a requirement to publish some kind of data. So instead of publishing an alert if those data breaches occur or if some kind of dashboard needs to be created, instead of sending data directly to the users, if that data is not PII, we are also ingesting that into Splunk Observability Cloud in a JSON format and then again, dashboards and other alerting can be created. These two are the main major use cases for which I am using Splunk Observability Cloud.

With the help of the alerting and observability mechanism, resiliency, and automatic automation of issue remediation based on alerts and workflows, it actually reduces the cost and increases the uptime of my system and customer satisfaction. There are multiple indirect benefits I am getting when using Splunk Observability Cloud.

Currently, with the growth of the organization, I am seeing an increasing use of Splunk Observability Cloud in a more dynamic way. We are continuously creating new dashboards, ingesting logs in JSON, and trying to bring the best value out of it. I am seeing a dynamic and drastic increase in the use of Splunk logs and the Splunk data we are ingesting.

There are two aspects to expanding the usage. Organic growth of the environment actually puts new systems into Splunk Observability Cloud, and exploring new opportunities for what all can also be ingested into Splunk Observability Cloud. Previously, I can see that memory dumps are there. We are also looking at whether we can ingest memory dumps so that if the system is about to crash, those memory dumps can be captured into Splunk Observability Cloud so that it can create alerts over that and I can also perform analysis. I can also see if any other system is facing the same kind of memory dump issues. So that maybe it is one alert for one system for me, but for the complete farm, there may be different servers with different teams or business units facing the same issues. When I have Splunk Observability Cloud on all systems, I can actually create a consolidated report and see that this is the pattern which particular farms are having this kind of issues, and maybe something is broken. This is the way the plan is to increase the availability or the usage of Splunk Observability Cloud.

The performance and speed are valuable. Previously when Splunk offered the enterprise solution, I needed to install Splunk and maintain my local server. There was a limitation that only a certain number of servers could be supported in one instance and I would need to have multiple instances if I was in an enterprise system setup. When I am in the cloud, a single instance can support N number of systems. It is pretty fast, no matter how much data is there. Dashboards are pretty good with multiple functions available. The alignment or integration that can trigger automatic solutions with the workflow for automatic remediation of the alerts is the best thing. These three or four things are the best Splunk Observability Cloud features that I am seeing.

The point in time alerting, the point in time data capture, and automatic remediation with the integration of good workflows or Ansible workflows is definitely the key to any resiliency and increasing the uptime of any system.

After moving to Splunk Observability Cloud, it is almost zero downtime. We never face downtime because when I was in the enterprise setup, I needed to maintain my servers and maintain hygiene of vulnerabilities, patches, and all. Now when I am in the cloud, everything is automatic. Almost zero downtime plus the perfect alerting feature and log-based analysis are available. Metrics alerting is also there in Splunk Observability Cloud through queries. This is one of the features that keeps me updated with the current health of my system and helps me to keep my system up and running fine and available for my customers.

Splunk Observability Cloud incorporated a new AI agent feature that is really good. Sometimes I need to create queries and Splunk queries for filtering the data and some pattern-based analysis. This agent is really good in helping me and suggesting the queries. This means I do not need to have a Splunk expert or Splunk query expert. I can just ask that agent that I need pattern-based analysis or I need to create this kind of filters for this kind of data and it can suggest to me. Once it suggests a sample query to me, I can do the tweaking and I can have my data ready. It actually reduces my time to perform my analysis and to reach the conclusion about what exactly is causing issues in my system and what are the repetitive issues in my system. This AI feature really helps for newcomers to Splunk Observability Cloud to perform deep diving analysis with the data captured by it.

Custom metrics are valuable. In Splunk Observability Cloud, some infra-level metrics are not available, but through custom metrics, I can achieve it. This is an add-on feature that Splunk Observability Cloud is providing and without any additional monitoring tool. If that feature was not there, then I would need to plan some other monitoring tool for metrics-based alerting, but this custom one helps me to achieve it in the same monitoring tool. The consolidation and integration of metrics-based alerting and log-based alerting in a single tool is actually the lovable feature. I do not need to worry about or look for multiple tools. I can have my own data and own health available in a single tool, in a single view.

The dashboards are good, but the only limitation I see currently is that they need particular formats only to create a dashboard. They need to have a particular JSON format or time series format. This sometimes creates additional work for me so that when I am ingesting logs in Splunk Observability Cloud, it should be in a specific format. Either Splunk Observability Cloud should have multiple formats available or multiple dashboards available for different kinds of formats. At least Splunk Observability Cloud has everything available at a Splunk level. They can do some kind of analysis and see what are the major top ten or top twenty types of logs they are getting and they can have dashboards according to those logs. Instead of forcing customers to design their logs in the way of Splunk Observability Cloud, Splunk Observability Cloud can create dashboards based on the customer requirement. This will actually ease things up for the end users.

The current dashboards are good. The feedback is that Splunk Observability Cloud is forcing me to modify my logs that I am ingesting in Splunk Observability Cloud in a specific format. If Splunk Observability Cloud can leverage it and make it open for any format, that would be great. If that is not feasible, at least the top ten or top twenty logs that Splunk Observability Cloud is getting should be readable by Splunk Observability Cloud without any changes. That actually is one of the major feedback items I can provide which can actually ease the life of the end users or any layman. As a newcomer to Splunk Observability Cloud, I may not know JSON. I now need to hire someone or I need to look for someone who knows JSON and who can convert my logs into JSON format and then I will ingest them into the logs if I want to create a dashboard. If I do not want to create a dashboard, that is okay. On the other hand, Splunk Observability Cloud is giving me a usability and easy to go interface, but for a dashboard, I need to have an understanding of JSON so that I can ingest the log in JSON format. That is a dilemma that they have and they should work on.

Currently, Splunk Observability Cloud is not the only solution which any organization is using. There is also Grafana and PagerDuty. If Splunk Observability Cloud can plan some kind of integration with PagerDuty and Grafana, then those things can be controlled from a single position and if something else is happening at one location, it can update things at all levels. That can also bring great value to the users. Currently, I have to maintain three systems separately, but if some kind of integrations can be developed with these three vendors, then that can be a great thing because all these three have now become the industry pillars or industry standards for observability and resiliency.

I have been working with it for the last two years. Before that, it was an enterprise solution. Now it is cloud-based.

I cannot relate any stability issues to my experience with Splunk Observability Cloud.

Scalability is pretty smooth. I just need to deploy the Splunk forwarder and the config file that specifies which servers it should connect to and it will get connected. My data will start populating. It is pretty straightforward. I do not see any challenges there, even when it was in enterprise and now when it is in the cloud. The deployment and onboarding of new servers and ingesting the logs is pretty straightforward. Anybody can learn it within a day without having any prior knowledge.

We have raised multiple questions when we face any issues. Our support is prompt and usually within a day, I will get my answers.

Previously I was on Splunk Enterprise. I have been using Splunk for seven to eight years before we moved to the cloud in the last eighteen months.

The initial setup is pretty smooth. I just need to deploy the Splunk forwarder and the config file that specifies which servers it should connect to and it will get connected. My data will start populating. It is pretty straightforward. I do not see any challenges there, even when it was in enterprise and now when it is in the cloud. The deployment and onboarding of new servers and ingesting the logs is pretty straightforward. Anybody can learn it within a day without having any prior knowledge.

Our main use cases for Splunk Observability Cloud are to observe our application, our websites, and our infrastructure metrics.

What I appreciate the most about Splunk Observability Cloud is the APM part and the log analytics part. These features can help us with troubleshooting our problems between multiple systems. 

Distributed tracing is very useful to us, and the infrastructure part can help us identify problems with the infrastructure. Splunk Observability Cloud has helped improve our operational performance and our company's resilience on the path of adopting it, and I expect more improvements in the future.

The RUM part of Splunk Observability Cloud can be improved significantly. We are currently struggling to use it since our application is mixed mobile and non-mobile. Some AI features in the search functionality could be beneficial in the next release of Splunk Observability Cloud.

In GCP, Cloud Run is not natively supported by Splunk, and we are challenged with bringing data from Cloud Run to Splunk. Native support of it in the future would be great for us.

We started using Splunk Observability Cloud one year ago.

I would assess Splunk Observability Cloud as quite reliable. The only problem is the graphical interface, which sometimes is buggy. It crashes, doesn't display data, and requires reloading the browser. I have experienced downtime with Splunk Observability Cloud only once, which lasted one hour due to issues that prevented us from logging into the platform.

Splunk Observability Cloud scales with the growing needs of our organization quite efficiently. I have expanded the usage of Splunk Observability Cloud, and the process of expanding usage was smooth apart from one part.

Customer service and technical support respond very quickly. That said, sometimes the solutions take too long to implement.

Neutral

Before adopting Splunk Observability Cloud, we used DataDog, and before that, we had no solution. The factors that led me to consider the change were mainly because my company has different IT offices. My IT office used DataDog, another IT office used New Relic, and others used different tools. We needed to adopt Splunk across the group to have something standard in my company.

My experience with deploying Splunk Observability Cloud was quite good, mainly since we almost have everything on cloud and that makes deployment quite easy.

My advice to other organizations considering Splunk Observability Cloud is to adopt it if you don't have anything else as it's a very good tool, and having something for observability is very good. Not only for the observability part but for all the Splunk platform, that's great. 

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

My main use case for Splunk Observability Cloud at my current company is monitoring apps. We have a lot of microservices because we sell different SaaS solutions to businesses, so monitoring, tracing, and everything related to that is essential given our microservices architecture.

We have traces and a service map set up where currently we only have two of the apps onboarded. The goal is to get the other four or five apps onboarded so we can have a single pane of glass view of how all of our apps work, how they're all connected, and how the traffic flows through them.

The best features Splunk Observability Cloud offers include in-depth capabilities. From going over it at the boot camp these last two days, there is definitely a learning curve, which could be a positive or negative. It's a positive thing because there are so many features, and it definitely takes a little time to get ramped up, but it is feature-rich with any feature one could think of, whether it's monitoring backends, frontends, or RUM metrics.

Regarding the RUM metrics feature, I'm not entirely sure yet, but we definitely have users of our SaaS solutions, so I can see it being useful to see how our apps load for users, how they are liking them, and what their sessions are like so we can research what it's from our end users' point of view.

I'm still relatively new, but I consider pricing one of the positives of how Splunk Observability Cloud has impacted my organization. From what I hear, they were using DataDog, and the pricing was very obscure, so they made a pivot to Splunk for pricing reasons. That combined with giving us clearer insights into the apps has been beneficial.

Clear insights are important because we host in AWS using ECS Fargate. It's difficult to rely on CloudWatch alone to trace everything that's going on with the apps running in the containers.

There is a pretty steep learning curve with Splunk Observability Cloud. There is a lot going on, which has pros and cons. The pros include the abundance of offerings in Splunk Observability Cloud, but the cons are that there is so much to learn that you need full-day boot camps just to really understand it.

I feel there is a lot going on, and I think some things could be simplified for certain, particularly the menus and how to accomplish things. Additionally, my understanding is that it is expensive to get infrastructure data into it, though I'm not sure if that's AWS's fault or Splunk's. My opinions may change since I just started using the product.

I've just started using Splunk Observability Cloud as I'm new at this company. I previously worked at Choice Hotels for practically my entire tech career. I started at this new company about a month and a half ago, and they use Splunk. I'm at the annual Splunk conference doing a three-day boot camp to learn Splunk Observability Cloud because we use it at this new company. I've been using it for approximately a month at this point.

Splunk Observability Cloud is stable.

Splunk Observability Cloud's scalability appears to be infinite from my understanding since it's a SaaS solution, so Splunk will ingest as many logs as we can send. I have heard that there are some limits on things, but I imagine if we pay for it, we can scale as large as we want.

I have not used customer support, but the people I've interacted with here today at the conference have been great, so I can imagine it's more of the same.

At this company they used DataDog, but at my previous company, we used OpenSearch.

I wasn't here when the setup, pricing, and licensing happened. However, I have heard that it was cheaper than DataDog, which is why we switched.

I have seen a return on investment in terms of time saved. I don't have hard numbers, but using the MCP server has saved me a ton of time. I'm able to multitask and do some work on the side while querying Claude to pull data and traces, which comes back to me rather than me having to manually do it and have that take my full attention.

Splunk Observability Cloud has helped improve my operational performance. I'm quite new, but I would definitely say it has improved operationally. I already got Claude hooked up to Splunk Observability Cloud and the regular Splunk Cloud MCP servers, which has helped me tremendously to be able to track down issues and traces, which could have taken me much longer to do manually.

It's really important for my organization that Splunk Observability Cloud has end-to-end visibility into our cloud-native environment. Otherwise, we're flying blind, especially because some of these new products we're about to release in an early release program require us to know what we're doing and to hit it out of the park to make it a success.

To help our organization scale, we only have two apps piped into it so far. We have acquired a couple of other companies, so Splunk Observability Cloud has allowed us to consider piping in that data as well to get the single pane of glass situation as we continue to build and grow the company.

I haven't noticed any specific outcomes or improvements in lowering the cost of unplanned digital downtime using Splunk Observability Cloud yet. I haven't done a traditional on-call role yet because I'm new to this company. We are beginning our on-call rotation, and I will definitely be leaning on Splunk Observability Cloud more heavily as I am on call and handling outages and after-hours calls. The solution's out-of-the-box dashboards and detectors are great. I'm doing the boot camp at the conference, and creating dashboards and filtering was probably my favorite part. Learning the roll-ups and the functions and how all of that works was excellent. The detectors seem very easy to create, and I appreciate how it creates a border on a chart when that chart is in the alert state.

I haven't really looked into the AI capabilities too much. I have used the AI tool that can help me write SPL, but that's about it. I typically use the MCP server for it and plug it into Claude, which has been my main way to interact with it from an AI perspective.

Regarding the accuracy and reliability of output from Splunk Observability Cloud's AI capabilities, I haven't used it too much beyond creating SPL, and so far there are no issues to report.

My main use case is end-to-end monitoring for the application.

We utilize the APM and auto-detectors, as the core metrics and core alerts are available for us, which are the features of Splunk Observability Cloud that I appreciate the most.

We lead the SRE, so our job is to ensure reliability, stability, and uptime, and without good observability monitoring, there is no way we can accomplish that. This is the main tool that we would use.

I would evaluate the effectiveness of Splunk Observability Cloud in improving digital resilience by saying that the idea is to minimize incidents. If any incident happens, the first thing I would do is go back to see why Splunk Observability Cloud did not detect that. I will take it back, do the reverse engineering to find out where it was missed out, and then work with the team to ensure these things are identified.

I have yet to experience the No-Sample Tracing feature in Splunk Observability Cloud, however, I am only in conversation with the teams where distributed tracing is required, and we want to provide the traces. My teams utilize the ability to enrich data with custom metrics in Splunk Observability Cloud, and I appreciate the feature supported within the Observability Cloud. Custom metrics could also be introduced from within the microservices, so I am yet to explore the OTEL library. I gave this feedback to the Splunk team that they should have their dedicated .NET library that customers can embed and start using; I do not think that is there today.

We are the first project within the company for a fully cloud-native application, so we will set the ground for the rest of the teams to get motivated. Therefore, I expect that I will have the best experience to become an example for others.

The integrations need to be improved for Splunk Observability Cloud. Currently, they do not have great support for Azure. We are on Azure, and I know they invested a lot of time in AWS yet not in Azure.

I had given feedback to the teams here, as the integration from Azure Cloud, how we supply the logs and the metrics, is not clearly documented yet, which was acknowledged by the team. For example, the OTEL collector has a thousand parameters, and we need a very specific use case with 10 parameters required for our integration. We can't go through the thousand parameters; we can, however, that is basically why I think some integrations need to get better for Azure.

There's a lot of talk about AI-powered analytics and guidance in Splunk Observability Cloud. I didn't get a great sense of how much of it is actually working; there are a lot of AI hallucinations. I think it probably needs much more improvement to contextualize it so that it is very clear and precise about what it randomly thinks, but it needs to match the context better.

Customer service and technical support need some improvement. We had issues with technical support, and the professional services were struggling as well.

I've been using Splunk Observability Cloud for six months.

I would assess the stability and reliability of Splunk Observability Cloud by saying no crashes or performance issues have been experienced.

On a scale of one to ten, I would rate customer service as eight.

Positive

My experience with deployment has been good. It's just the routing, the matrices, and the integration is where we were struggling a little bit. That said, having the cloud as observed to provision was never a problem.

I hope to see a return on investment with Splunk Observability Cloud. I have not applied this for production. That said, we already use Splunk Cloud for production, and we are good with that, so I see the value.

The cost is fine, and we are good with what is given. It's a centralized tool for my organization, so at the org level, a lot of things were decided, but we are actually happy with the cost we received because I know I have to approve my budget, and it's within our range, so we are okay with it.

My advice to Splunk is to mix Splunk Cloud and Splunk Observability Cloud into one. Don't make oObservability only needed in Splunk Cloud, too. You don't want to have two products competing with each other; you want to compete with someone outside your organization. Combine this, as there's a lot of confusion. Even in different classes and training sessions meant only for Splunk Cloud, they were not for Splunk Observability Cloud, and they are different today. The acquisition of SignalFx, which is not its own, adds to the confusion. So, to the customer, provide one interface, and combine them.

On a scale of one to ten, I rate Splunk Observability Cloud an eight overall.

My main use case for Splunk Observability Cloud is tracking details for AI agent spending that has been occurring in recent days. My goal is to break down these spends and understand where tokens go, who consumes them, and how to maximize this usage.

Splunk Observability Cloud helps me track and break down AI agent spending through a framework I have built where I map all LLM calls to OTel collectors and then send them to Splunk Observability Cloud, adding team names, team tags, environment, and related information. This helps us track how much work has been completed, how it is being used, and if there is any possibility of an agent going out of control, we can detect it and stop it.

I am looking forward to more releases for Splunk Observability Cloud for AI agents through this conference.

The best features Splunk Observability Cloud offers are the dashboards where I can view all metrics and telemetry. What I value most about the dashboards is both the visualization and customization capabilities along with the depth of data I can access from what we have gathered. I can drill down into exact details and view graphs and charts.

Although I have not used Splunk Observability Cloud in my own organization, I use it to help my customers leverage Splunk Observability Cloud. It has been helpful for them as they transition from different platforms to Splunk, and I am able to assist them in porting to Splunk Observability Cloud and support them in making these decisions.

My customers have benefited from moving to Splunk Observability Cloud through specific integrations with AI agents and MCP gateways, allowing them to correlate any incident directly from Splunk Observability Cloud to their DevOps agent and take action on it. A problem that previously took four to five hours is now completed automatically through Splunk Observability Cloud and DevOps SRE agent integration in just a couple of minutes.

The metrics and outcomes from this improvement include significant reductions in incident time and mean time to resolution has decreased, freeing up engineering hours that were previously required, allowing teams to focus on other priorities.

I would like to see Splunk Observability Cloud move more into the AI agent space and help shape the future of AI spending and how tokens are being consumed. A breakdown between input, output, and cache tokens would be helpful.

I would also like to see integration with some existing open source platforms, which would be valuable for integrating with not just large platforms but also smaller ones that might become open-source in the future. I think Splunk already does this effectively with the OTel collector.

For the past one year.

Splunk Observability Cloud is stable.

Splunk Observability Cloud's scalability is very good.

My customers previously used DataDog before switching to Splunk Observability Cloud due to a company mandate that led to their transition.

My experience with pricing, setup cost, and licensing through AWS Marketplace is that it is straightforward.

I am both a partner and reseller with this vendor.

My customers did not purchase Splunk Observability Cloud through AWS Marketplace.

Splunk Observability Cloud has helped reduce mean time to detect (MTTD) and mean time to resolution (MTTR) has decreased from hours to minutes because of the integration available with other AI agents as an MCP.

It is very important for my customers that Splunk Observability Cloud provides end-to-end visibility into their cloud native environments. Having this visibility helps them avoid relying on multiple third-party products and maintain a comprehensive product portfolio.

I assess Splunk Observability Cloud as an effective solution for helping organizations scale and it provides better support through various levels.

My advice for others considering Splunk Observability Cloud is to look at the full suite of products, not just Splunk Observability Cloud itself. Splunk offers many other solutions including security, SIEM, and other capabilities, so it is worth exploring the complete portfolio. I would rate this solution an eight out of ten.

My main use case for Splunk Observability Cloud is logging. For logging, I monitor applications across a Kubernetes cluster and Docker containers.

The best features Splunk Observability Cloud offers include the API access. API access has helped me in my day-to-day work by allowing easy integration. Splunk Observability Cloud has positively impacted my organization because we have become huge clients.

Splunk Observability Cloud has helped improve my operational performance and my company's resilience. For example, it has helped with security and video. Splunk Observability Cloud helped with security by providing visibility into tiling.

I think Splunk Observability Cloud could be improved by being cheaper.

I have been using Splunk Observability Cloud for less than a year.

Splunk Observability Cloud is stable.

Splunk Observability Cloud's scalability is great.

The customer support is good. On a scale of one to ten, I would rate the customer support as a ten.

As far as I know, the organization has always used Splunk Observability Cloud, so I cannot say if we previously used a different solution.

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

I do not have visibility into what changed after we started using Splunk Observability Cloud. I did not purchase Splunk Observability Cloud through the AWS Marketplace.

I have seen a return on investment, but that is outside my scope.

I am not the right person to share my experience with pricing, setup cost, and licensing.

I am unaware if my organization evaluated other options before choosing Splunk Observability Cloud.

Regarding Splunk Observability Cloud's AI capabilities, I think its governance and security are excellent. Regarding Splunk Observability Cloud's AI capabilities, the accuracy and reliability of its output depend on how long it takes; the longer it takes, the better it is.

It is not important for my organization that Splunk Observability Cloud has end-to-end visibility into our cloud-native environments. Overall, I assess Splunk Observability Cloud as great; it is overall very good for helping my organization scale. I cannot answer if I have seen time to value with Splunk Observability Cloud. I think Splunk Observability Cloud could be improved by being cheaper.

I would rate this review as a five overall.

Splunk Observability Cloud is used to monitor the health and performance of cloud infrastructure and microservices, providing infrastructure applications and their health insights for security insurance as well as correlating performance anomalies.

Regarding the feature sets of Splunk Observability Cloud, the primary benefit is real-time infrastructure monitoring. Additionally, APM (application performance monitoring) is provided, which helps to obtain intelligent alerting with the aid of log and metric correlation available in the portal. There is also an AI-driven analytics add-on.

Regarding the detector functionality of Splunk Observability Cloud, the intelligent alerting mechanisms in the observability platform have been observed and implemented. These help to obtain continuous metrics with predefined conditions that are automatically generated whenever the performance threshold has increased or when high volumes of traffic or anomalous behavior have been identified by the detectors. This helps to achieve benefits such as proactive monitoring, real-time alerting, and faster instant responses.

This has helped regarding the monitoring of blind spots by improving the inline visibility of infrastructure with Splunk Observability Cloud, which helps to correlate metrics, traces, and logs from a single platform, enabling the identification of issues that might go unnoticed within a single system. It also helps to reduce troubleshooting time.

In Splunk Observability Cloud, the AI analytic engine detects anomalies and prioritizes critical issues, which helps with prioritization as the main concern is to get alerts resolved within a timeframe to improve MTTR. It also accelerates root cause analysis by correlating metrics and traces within the dependencies.

For example, in a case where an application response gradually increased over a certain period, increasing CPU and memory consumption, which was normal, the AI analytics capabilities detected the anomaly based on historical behavior and generated an alert before users monitored these dashboards. This had a noticeable impact, enabling proactive investigation to reduce troubleshooting time.

The problem that was to be fixed with the help of the RCA detections is that Splunk Observability Cloud streamlines the entire engine lifecycle, helping to identify the root cause. The AI-powered detection and anomaly detection in Splunk Observability Cloud identify performance issues in real-time, generating alerts before they significantly impact customers, and proactively detecting errors. Engineers can correlate metrics, distributed traces, service maps, and logs to follow the request path and identify issues.

A specific instance where Splunk Observability Cloud identified the root cause across a hybrid environment, which would have typically required manual integration to solve, occurred when users reported an ecommerce application that was loading slowly. A detection was received from the detector indicating that the API response time had increased from the normal baseline threshold. Using RCA analytics tracing, the engineer followed the user request through the application, revealing delays occurring in the database query while correlating metrics, indicating increased CPU and query division. That aspect required troubleshooting. Based on these insights, the team optimized the query response by improving the port sizes, resolving the issue within the time limit.

With regard to visibility into the entire AI stack including LLM performance, GPU utilization, and vector stores, this has changed the ability to manage the nondeterministic nature of AI applications while maintaining cost and quality standards. Traditional applications give the same input and the same output, while AI applications evaluate quality and relevance based on dynamic perspectives and the availability of data, focusing on response qualities, latencies, and token usage. Changes in latency monitoring have been implemented that help generate responses.

The integration of business context in the telemetry has helped to reduce MTTR by fifty percent, which has been the key metric.

The key benefits of Splunk Observability Cloud are related to performance monitoring and service monitoring that provide end-to-end visibility. It offers unified infrastructure and operations, such as Kubernetes and cloud services, all within a single platform, which helps to maintain end-to-end visibility. Furthermore, it provides real-time monitoring that helps to continuously assess the health of applications.

Splunk Observability Cloud has helped improve operational performance and company resilience, as members have access to all in one place, which significantly reduces MTTR. Additionally, it provides instant responses with the intelligent detectors mentioned, such as the AI-driven anomaly detection, which aids in detecting alerts or performance issues that degrade over time.

MTTD has been reduced by thirty percent as of the current date.

Regarding time to value with Splunk Observability Cloud, the biggest value regarding performance was that the monitoring system did not have to be built from scratch. This is key because within a short period after deployment, actionable dashboards, intelligent alerts, and end-to-end visibility were available.

Enhancements can be made to Splunk Observability Cloud dashboard, particularly the widgets that can help display ROI metrics and streamline setup for detection tuning. These two points are key concerns that should be highlighted.

Splunk Observability Cloud has been used for seven to eight months.

Splunk Observability Cloud is stable when it comes to visibility aspects.

Splunk Observability Cloud would be assessed as a nine out of ten for helping the organization scale.

The tech support of Splunk would be rated as a nine on a scale of one to ten.

Prior to using Splunk Observability Cloud, no different monitoring product was used.

The experience with lowering the cost of unplanned digital downtime using Splunk Observability Cloud shows that while managing critical incidents in the past few weeks, it has helped reduce downtime by providing timely detections using the AI detection-driven mechanism.

The deployment was done in-house, and support was available during the setup process.

A specific instance where Splunk Observability Cloud identified the root cause across a hybrid environment, which would have typically required manual integration to solve, occurred when users reported an ecommerce application that was loading slowly. A detection was received from the detector indicating that the API response time had increased from the normal baseline threshold. Using RCA analytics tracing, the engineer followed the user request through the application, revealing delays occurring in the database query while correlating metrics, indicating increased CPU and query division. That aspect required troubleshooting. Based on these insights, the team optimized the query response by improving the port sizes, resolving the issue within the time limit.

It took approximately two months to build Splunk Observability Cloud from scratch.

No other options or solutions available in the market have been evaluated, as Splunk or Cisco tool stack is already being used. Therefore, the decision was made to go with Splunk Observability Cloud, as the native tools are inbuilt and work well with the systems.

The ability to enrich data with custom metrics in Splunk Observability Cloud is managed by the administration team, and there is no direct involvement in that aspect.

My impressions of Splunk for helping the organization focus on business-critical initiatives show that it is on the right track, with several features including AI-driven analytics and detection, which are quite helpful in reducing downtime, making them key features.

Organizations looking for an end-to-end observability platform with faster root cause analysis, improved uptime, real-time dashboards, alerts, and scalability monitoring should consider the Splunk Observability Cloud solution. This review is rated as a nine out of ten.

My main use case for Splunk Observability Cloud is real-time monitoring of infrastructure and applications, and I mainly use it to monitor system performance and identify issues quickly to troubleshoot performance or availability problems.

For example, we had a performance issue where an application was responding slowly, and I used Splunk Observability Cloud to check the application metrics and system performance, identified the affected component, and helped troubleshoot the issue, making root cause analysis much faster.

Splunk Observability Cloud also helps us monitor trends and detect potential issues early, and the centralized dashboard and alerts make daily monitoring and troubleshooting much easier.

The features that stand out most in Splunk Observability Cloud are real-time monitoring, customizable dashboards, powerful alerting, and infrastructure and application performance visibility, which make troubleshooting faster and help us identify issues proactively.

Splunk Observability Cloud has improved our monitoring and troubleshooting by giving us better visibility into system and application performance, allowing us to detect issues faster and reducing the time needed to identify and resolve problems. Issues can be identified and resolved faster because of real-time monitoring and alerts, and while I do not have an exact percentage, it has improved troubleshooting efficiency and overall system visibility.

Splunk Observability Cloud could be improved with more advanced troubleshooting tools, better dashboard customization, and more intelligent alerting, as these improvements would make monitoring and issue resolution even faster.

Better integration with third-party tools, more detailed reporting, and similar configuration would make the day-to-day experience smoother.

I have been working in my current field for one and a half years.

Splunk Observability Cloud is stable.

Its scalability is high, as it can easily support growing infrastructure and monitoring requirements.

The customer support for Splunk Observability Cloud has been good, with a responsive support team and helpful documentation and support resources when we face issues.

The pricing, setup cost, and licensing for Splunk Observability Cloud were reasonable for the features provided, and the initial setup was straightforward while the licensing model was manageable for our organization.

We have seen a return on investment through time-saving results, as faster issue detection and troubleshooting reduce the time our team spends investigating incidents.

Splunk Observability Cloud has helped reduce the cost of unplanned digital downtime by detecting issues early through real-time monitoring and alerts, allowing our team to respond and resolve incidents faster.

The pricing, setup cost, and licensing for Splunk Observability Cloud were reasonable for the features provided.

We evaluated a few other monitoring solutions, including Datadog and New Relic, before choosing Splunk Observability Cloud due to its strong monitoring capabilities, integration, and overall visibility.

I would rate Splunk Observability Cloud an 8 out of 10 overall.

I chose it because it provides reliable real-time monitoring, good visibility, and effective alerting that helps us identify and troubleshoot issues quickly, and I think better customization and advanced troubleshooting features could make it a 9 out of 10.

Strong governance and security are important for AI capability, and proper access control, role-based permission, and audit logs help ensure that the AI is used securely and responsibly.

The AI output of Splunk Observability Cloud is generally accurate and useful for identifying issues and analyzing performance data, although I would still validate important recommendations before taking action in a production environment.

Our organization uses a hybrid cloud deployment for Splunk Observability Cloud, allowing us to monitor both on-premises infrastructure and cloud-based applications from a single platform.

It is very important for our organization that Splunk Observability Cloud has end-to-end visibility into our cloud-native environment because it helps us monitor the entire environment from infrastructure to application, making it easier to identify performance issues and understand dependencies for quick troubleshooting.

I assess Splunk Observability Cloud very positively for helping our organization scale since it provides centralized monitoring and visibility as our infrastructure and applications grow, making it easier to manage performance and troubleshoot issues across a larger environment.

The out-of-the-box dashboards and detectors are very useful, as they provide a quick view of system and application health without requiring much initial configuration.

I recommend Splunk Observability Cloud for organizations that need real-time and end-to-end monitoring, as it provides good visibility, alerting, and troubleshooting capabilities in hybrid cloud environments.

Overall, I am satisfied with Splunk Observability Cloud, as it provides strong real-time monitoring, good visibility, and faster troubleshooting, being useful for improving our day-to-day operational efficiency. My overall rating for this solution is 8 out of 10.

My primary use cases for Splunk Observability Cloud include alerting the business and the integrations app team, which are the largest users of Splunk within our company. They take up most of our ingest and have many alerts set up, along with log analysis and events analysis. 

Those are our biggest team users, and alerting in general plays a crucial role for incident creation across multiple teams, regardless of who the shareholders are, cutting across multiple teams.

Splunk Observability Cloud has significantly improved my operational performance and my company's resilience. I would evaluate the effectiveness of Splunk Observability Cloud in improving digital resilience within my organization as very positive. It's easier to access for both Splunk experts and users who may not be familiar with Splunk, allowing even non-technical people to navigate it.

The features I appreciate the most about Splunk Observability Cloud are the ServiceNow integration feature, which is very seamless, and that's probably my favorite since ServiceNow is big in the observability industry. Being able to seamlessly create those incidents is the biggest plus. Without that, we probably wouldn't be using Splunk.

Just alerting with Splunk Observability Cloud provides significant benefits for my organization, particularly since this is a food and manufacturing company. Alerting the business side before the customer reports an issue helps us to be one step ahead, which is a big feature.

Dashboards for the IT executives are also beneficial since they might not be as technical yet can easily read a dashboard. The ability to create something easily interpretable, using color coding and heat maps, allows directors to see value in it.

The ability to enrich data with custom metrics in Splunk Observability Cloud has significantly impacted our integrations and app team, the biggest users of Splunk in our environment. The visibility provided and the remediation they gain from reading events have led to increased log ingestion, which is a good problem to have. They can confidently rely on Splunk for monitoring app creations, APIs, and more.

Splunk Observability Cloud could be improved in terms of integrations with more technical add-ons, such as Zoom. Although they have one with Zoom, it's not available in the cloud, so having that feature would be beneficial. Essentially, Splunk should continue expanding to create easier ways to ingest logs from different products.

The out-of-the-box customizable dashboards in Splunk Observability Cloud are very effective in showcasing IT performance to business leaders. However, there are aspects that could be improved, such as linking dashboards to one another. While IT leaders may not drill down, it's crucial to create levels of dashboards for technical users to find root causes, making it effective for stakeholders.

I have been using Splunk Observability Cloud for two years, as my first cloud was with ABC.

I have experienced slowness from Splunk Observability Cloud occasionally, yet we have not faced crashes or major performance issues. When slowdowns occur, we reach out to support for explanations and have received adequate responses.

Splunk Observability Cloud scales efficiently with the growing needs of my organization, even requiring occasional license increases. Maintenance is done by Splunk, and we receive alerts about maintenance windows to ensure we stay current. So far, we've experienced seamless operations without any breakdowns.

I find Splunk's customer service to be great compared to many other products. They do a good job of responding within 24 hours or less, even with P4 issues. They may reroute you a couple of times. Overall their support is commendable. I would rate technical support and customer service a solid eight.

Positive

Prior to adopting Splunk Observability Cloud, I was aware that our company used another solution. I don't recall what it was since I was brought in specifically for Splunk.

I wasn't a part of the deployment process. 

I have definitely seen a return on investment with Splunk Observability Cloud, particularly through how fast it has grown and how comfortable other teams are in relying on its outputs for monitoring and observability.

I don't directly deal with costs, so I can't comment on the return on investment regarding lowering unplanned digital downtime using Splunk Observability Cloud.

I haven't explored the no-sample tracing feature in Splunk to eliminate blind spots in data collection. 

AI-powered analytics and guidance provided by Splunk Observability Cloud will be very beneficial. We just initiated a response to get those AI functionalities into our cloud environments, so we haven't fully explored it yet.

My advice to other organizations considering Splunk Observability Cloud is to get it, especially for log monitoring and alerting. There aren't too many observability tools that match its ease of use, whether for IT-oriented users or not. Its graphical user interface is brilliant and very seamless, making it easy for anyone to navigate. I'm confident big companies considering Splunk should choose it, as it delivers in usability and integration with other tools.

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