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.

My main use cases for Splunk Observability Cloud include retail analytics.

The feature I appreciate the most about Splunk Observability Cloud is Synthetic Monitoring. These features have benefited my organization by increasing availability and decreasing downtime, providing assurance that makes you feel good, and ultimately enhancing well-being.

The out-of-the-box customizable dashboards are very effective. At the same time, we also use Splunk Cloud to enhance them. The Splunk Cloud is a better dashboarding experience.

Our teams have utilized the ability to enrich data with custom metrics in Splunk Observability Cloud. We've been doing a lot of that with event management and linking that into IT as well. So we're using that to be able to tie systems together. The integration we have between Observability Cloud and ITSI for event management is where we're using that type of stuff.

Splunk Observability Cloud could be improved by having more integration with Splunk Cloud because at the moment they're two separate products. They're making great moves on what they call unified access; tighter integration is always a good thing.

I have been using Splunk Observability Cloud for three years.

I would assess the stability and reliability of Splunk Observability Cloud as generally good. We have experienced the odd bug; however, nothing too serious, and Splunk has been quite good in terms of resolving issues; it's just routine stuff and nothing bad.

Splunk Observability Cloud scales incredibly with the growing needs of my organization. It just means the more we use it, the more expensive it is, but there are no issues reported.

I would evaluate customer service and technical support as fantastic; nobody is better.

Positive

During the deployment, we only had some challenges when we switched on unified access. However, they were just teething problems.

I have seen a return on investment with Splunk Observability Cloud as we have averted some things that may otherwise have resulted in downtime. We have had it avert potential problems, and the first time it happens is a return on investment. The second time, nobody notices, making measuring business value a challenge.

I would advise other organizations considering this solution to give careful attention to the use cases they have and how they plan to proceed in terms of their roadmap over the next two to three years, as there are alternatives. Having an idea of where you want to go will help you make a better-informed decision.

Additionally, it's good advice to have a customer reference call to learn from someone's experience and avoid pitfalls.

On a scale of one to ten, I would rate Splunk Observability Cloud overall as a good eight; as soon as it's all integrated neatly together, then it's up in the high numbers.

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.

I use Splunk Observability Cloud for network logging analysis.

I prefer the dashboard building and search features of Splunk Observability Cloud the most. Splunk Observability Cloud has helped improve my operational performance. 

Previously, we used Elastic for similar purposes as Splunk Observability Cloud, and this has allowed us much more visibility into what we're working on with usable dashboards and metrics, which has been awesome.

The only strain point we've encountered with Splunk Observability Cloud is that the search times can be lengthy for some things. We have a large environment, so that's expected. That's the only complaint I've had so far.

I have been using Splunk Observability Cloud for approximately three months.

I experience slow searches occasionally with Splunk Observability Cloud, but there are no outages or anything in that regard, so it is pretty stable and reliable.

Splunk Observability Cloud scales with the growing needs of my organization effectively. As a large organization, we find it impressive that our volume has been handled with only occasional slow searches.

I haven't worked with customer service and technical support directly, however, another engineer on the team has shared positive feedback about their experiences.

Positive

Prior to adopting Splunk Observability Cloud, I was using Elastic. Support was a significant factor that led to switching to Splunk Observability Cloud. The previous solution wasn't fully supported by their team, and there weren't adequate integrations or visibility needed for some of our applications. Additionally, it was a legacy installation set up by former employees, so this was an opportunity to start fresh with people who are actively involved.

We haven't had Splunk Observability Cloud long enough for me to make substantial comments on its effectiveness in improving digital resilience within my organization.

I wasn't involved in the licensing. 

We haven't used the no-sample tracing feature in Splunk Observability Cloud specifically for eliminating blind spots in data collection. We haven't implemented the AI-powered analytics and guidance features provided by Splunk Observability Cloud either.

Our main security architect has done extensive work utilizing the ability to enrich data with custom metrics in Splunk Observability Cloud by setting up specialized dashboards and searches for our various integrated apps, including ISE and Palo firewall logging.

I haven't extensively used the out-of-the-box customizable dashboards provided by Splunk Observability Cloud as we're still using our custom ones. I wasn't involved in the pricing, setup, cost, and licensing. I enjoy using Splunk Observability Cloud, but I'm not familiar with the cost aspects.

Access to Splunk Observability Cloud has been reliable for all users. On a scale of one to ten, I rate Splunk Observability Cloud an eight. 

I recommend spending time working on your own dashboards and searches to fit your business needs, as that's where you'll get the most value out of Splunk Observability Cloud.

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.

For the retail sector, we are building a solution for customer stores in order to know how the products are sold.

The feature of Splunk Observability Cloud that I prefer most is the easy deployment on the cloud. The benefit of that feature for my organization is to optimize the deploys and implementation and the response to our customers, to quickly make a demo. Splunk Observability Cloud has helped improve our operational performance, especially for our customers.

My experience with the out-of-the-box customizable dashboards provided by Splunk Observability Cloud is that they are effective in showcasing IT performance to business leaders. For the initial point of contact, it helps and works nicely as a star point. Then, you have the basics and use that as a framework to deploy others, so they are very helpful.

Splunk Observability Cloud can be improved. In terms of additional features I would want to see in future releases, since Cisco acquired Splunk, more Cisco integration could be beneficial.

I have been using Splunk Observability Cloud for the last two years.

I have not experienced any downtime, crashes, or performance issues.

Splunk Observability Cloud scales very well with the growing needs of my organization, as we just need to add a license or data ingestion.

I would evaluate customer service and technical support for Splunk Observability Cloud as good. They respond effectively and in time.

Positive

Prior to adopting Splunk Observability Cloud, we used other solutions to address similar needs, such as Dynatrace and ElasticSearch.

It is easy to deploy on the cloud.

I have not seen a return on investment with Splunk Observability Cloud yet, as we are relatively new to it.

My experience with pricing, setup cost, and licensing of Splunk Observability Cloud is that it is somewhat expensive, considering I am from Mexico and the market in Mexico is very different from the market in the USA. It is expensive, especially when there are other vendors that offer something similar for much cheaper.

The factors that led me to consider the change to Splunk Observability Cloud include performance and cost, and it depends on the customer. If the customer is a network user or partner with all Cisco solutions, Splunk Observability Cloud fits perfectly.

However, if we have a new customer that doesn't have any Cisco products, it might be better for them to use another solution that is easier to deploy and not as complete as Splunk Observability Cloud, especially if they only need one or two features.

My advice to other organizations considering using Splunk Observability Cloud is that if you want a comprehensive, consistent tool or solution, it is one of the leaders in the market because it integrates with the network side of their organization, including Cisco solutions. Regarding customers who don't come from the Cisco world, it is a good choice, depending on their use. However, for small customers or those that are not large companies, Splunk Observability Cloud may not be the best fit, as it is a comprehensive tool. In Mexico, we observe that customers claim they only need APM or infrastructure monitoring, a very basic requirement, and don't require the entire Splunk portfolio.

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

My main use cases for Splunk Observability Cloud are indexing, dashboards, alerts, and reports.

The dashboards are the features of Splunk Observability Cloud that I appreciate the most, providing visual representation of all data and text. These features have benefited my organization by speeding up people's jobs, allowing a place to monitor all logs, as there are usually thousands of entries coming in which can become very disorderly. Users can monitor everything and write queries to organize the data and build dashboards to visualize it. This creates one-stop shops to get answers on how products and applications are performing, as opposed to having to jump onto servers and look through numerous logs.

The main improvement I would suggest for Splunk Observability Cloud would be offering the ability to implement custom apps, specifically allowing Python scripts that Splunk Cloud could host. Currently, we cannot create custom apps through Splunk Cloud. Additionally, continuous performance improvements for faster searching and indexing would be beneficial.

I have been using Splunk Observability Cloud for over the last year.

I would assess the stability and reliability of Splunk Observability Cloud as good. There have been some performance issues, though not necessarily crashes, occurring approximately 20% of the time or less.

Splunk Observability Cloud scales smoothly with the growing needs of my organization. There have been some cases of performance loss due to rapid onboarding. We are handling multiple terabytes of data daily, so we expect some hiccups, but otherwise, it has scaled effectively for our fast-paced migration.

My experience with customer service and technical support has been very present and super responsive. When we submit a case on Splunk support, they usually reach out within the same day or next day. They have consistently helped us resolve any issues we've encountered.

Positive

I used Splunk Enterprise before adopting Splunk Observability Cloud. While other parts of the company were leveraging different logging tools, we primarily revolved around Splunk. When Splunk Cloud became available as the next option, we were ready to migrate.

I haven't had personal experience with pricing, setup cost, and licensing as it's managed by our managerial side.

I have seen a return on investment with Splunk Observability Cloud through faster debugging and troubleshooting capabilities with enhanced observability. A significant return on investment comes from not having to host Splunk Enterprise ourselves. Having servers on Splunk's end allows us to focus more on development, monitoring, and our products, rather than maintaining our own local version of Splunk.

I would rate Splunk Observability Cloud overall as a solution 9 out of 10.

The solution involves observability in general, such as Application Performance Monitoring, and generally addresses digital applications, web applications, sites, and mobile applications. I worked with it in two companies: one in the energy sector and one in the hotel sector.

The Splunk teams helped us with data collection, instrumentation, and many other options.

The testing and monitoring of infrastructure is useful. We also use it for many metrics and can use it effectively for troubleshooting and for detection. It's very helpful. 

With Splunk Observability Cloud, I appreciate working with open telemetry. The standards of open telemetry are especially useful for collecting data such as traces, matrices, and logs. Splunk respects the standards of open telemetry. This is beneficial. Many clients work with AWS and the cloud in general with multiple solutions such as Datadog, Dynatrace, and Splunk. Working with the standard open telemetry is very advantageous. Splunk Observability Cloud is very simple for users in general, including developers, DevOps, and data teams. It's more straightforward compared to Dynatrace.

There are many out-of-the-box solutions proposed by Splunk, such as dashboards for AWS instances, EC2, Fargate, and Lambda. It's very helpful for beginning, especially for monitoring, and the detectors for alerting help understand how the platforms work.

The no-sample feature is great. It eliminates blind spots.

After completing the instrumentations, we have many dashboards and tests for monitoring infrastructure, particularly CPU and memory. We also use applicative metrics such as JVM, Java Runtime, and many other applicative metrics and testing. For troubleshooting, we can detect problems in seconds, which is particularly helpful for digital teams.

AI analytics have the potential for a lot of functionality. The detectors for alerting may prove useful.

When we deploy the instrumentation in the application, we can start using the dashboards immediately. The dashboard building is very helpful for starting work.

It's beneficial for monitoring performance and infrastructure, especially when deploying applications with multiple versions with Git. It's important to detect performance issues, such as CPU consumption or memory consumption, particularly over time in Java and Python.

For other teams, they need help and guidance to use custom metrics. For observability engineers and specialists, it's straightforward, but for others, it can be challenging.

The solution overall is very valuable for me.

The time to value was immediate. Once we deployed, we started to use the dashboard directly and began detecting issues. 

Saving time with automation can save us weeks. It's improving our resilience. It helps us detect issues and increase performance. 

The solution has been very useful for helping us focus on business-critical initiatives.

Regarding dashboard customization, while Splunk has many dashboard building options, customers sometimes need to create specific dashboards, particularly for applicative metrics such as Java and process terms. These categories of dashboards would be very helpful for customers.

I started working with Splunk Observability Cloud in 2023.

The system is relatively stable. We rarely have problems accessing the dashboard or the page. We encounter problems in the Splunk platform very rarely.

It's very scalable. We haven't experienced any problems with the instrumentation or scalability. On a scale of one to ten, I'd rate it a ten.

We've used the solution across more than 250 people, including engineers.

I would rate Splunk technical support at six out of ten.

When we have a problem and need to create a case, the response isn't quick. They often require multiple questions, with five or six emails to get a response. Problem resolution typically takes between two and five days, which isn't very helpful. However, sometimes we do receive quicker solutions.

Neutral

We used legacy solutions such as Grafana and Prometheus. There are several differences between Splunk Observability Cloud and these solutions. We used Grafana as a monitoring solution, however, it's not truly observability. We used OpenSearch for logs, Prometheus for metrics, and Grafana to work with Prometheus. That said, it's not equivalent. Observability is different.

We're also familiar with Datadog and Dynatrace.

The implementation took between two and three weeks.

For cloud deployment, it's straightforward. We can use GitLab and DevOps CI/CD. For on-premise deployment, such as Linux and deployment with satellite, it's easy yet requires some work to configure the configuration files.

Updates are generally needed, especially for the open telemetry version or SDK. However, regarding the platform itself, we don't need to do anything.

I worked with my company when they used the solution, so I'm not certain about the history of how long it took to detect problems. However, for mean time to detect, and mean time to respond, I'm sure it's very helpful, and we can estimate a minimum improvement of 20%.

We're a customer and end-user.

Currently, in France, we cannot use the artificial intelligence option. While this option is enabled for the United States and many countries, it's not yet available in France. However, the solution with detectors, especially for alerting, is important for us.

I recommend it, especially for teams using legacy monitoring.

I would rate Splunk Observability Cloud nine to ten out of ten.

I mostly work with the performance metrics of the CPU, or host metrics, as well as application metrics and traces. Overall, I use these mostly for real-time monitoring based on the application to track application performance.

For the monitoring of infrastructure, it is quite insightful because in-depth, I can see what is going on in the infrastructure. If something goes down or some crons fail inside the infrastructure, the alerts are quite helpful for more visibility on the cloud-native side.

This is quite helpful for improving the application observability and the infrastructure side as well. I would rate observability above an eight.

I am not that much involved in the business side because I work as a DevOps engineer, so I do not know how much it helps on that front. However, it helps in tracking traces and metrics quite generously well and helps us improve the application side for more reliability on the business side.

It is very helpful and really enhances the AI-powered analytics, which helps us for troubleshooting the application and to get more insightful information while troubleshooting application error rates.

AI-powered guidance is really helpful because it provides more actionable insights and highlights anomalies automatically. I do not need to go through it manually, and it also helps us with smart alerting and recommendations.

It helped operationally because due to the insights of the applications, I get more insight for our application to enhance it further. It detects anomalies and correlates data while guiding us to the root causes, so we can enhance our application accordingly.

I have seen that mean time to resolution was reduced around 30 to 50 percent. The main reason for this combination is because of real-time monitoring and AI-powered anomaly detection and distributed tracing. Instead of manually checking the logs and metrics across multiple tools, the platform quickly highlights the issues, correlates data, and points us towards the root cause.

After implementing Splunk Observability Cloud, there was a deep learning curve for the new tool. It took one or two months to get proper insights from it. After configuring, I have seen that it is very useful for tracking traces and metrics of our application, servers, and clusters. Adoption time is usually after two months, or after a few weeks of getting Splunk Observability Cloud.

Splunk Observability Cloud is highly effective in improving digital resilience. Real-time visibility and proactive alerting and fast root cause analysis, distributed tracing, and AI-driven insights enable anomaly detection, which allows us to quickly understand failures and recover faster. This is critical for maintaining system availability and helps us handle failures in complex distributed environments since we can see how services interact and where breakdowns occur.

Regarding features, it helps us for better understanding of how the application works and in-depth tracking of application monitoring.

It can be more enhanced using additional AI power. I can get more reliability using AI because AI-driven guidance is more useful nowadays. It can really improve more on the AI side because it will help us to reduce manual intervention with the system and root cause analysis will be much better with AI over human analysis.

I would say that it is quite helpful, but for different kinds of applications, it could be improved because sometimes it might provide a cloud judgment of the root cause analysis. I need to do manual intervention using a dedicated human for root cause analysis for better understanding of the root cause. This is how the agentic side can be improved.

I have been working with Splunk Observability Cloud for around a year.

It is quite scalable. Right now, it is providing much better insights and can be more enhanced over several aspects. I would rate scalability an eight to eight point five.

I have tried other solutions, but they were not that great in terms of functionalities and overall performance. Splunk Observability Cloud is much better than the others because it provides AI alongside the solution. This is very helpful due to the AI-driven solutions and guidance for root cause analysis. Splunk Observability Cloud goes through the details of application traces and metrics in depth, so I get better observability over the application. This is why I have preferred Splunk Observability Cloud over other monitoring tools.

I have tried SignalFx, but it was not quite insightful. I have tried Splunk Observability Cloud over SignalFx.

Splunk Observability Cloud is quite insightful and helpful for improving the observability side. I provide this solution an overall rating of eight.