Splunk Observability Cloud Reviews

My main use case for Splunk Observability Cloud is for infrastructure management and application logs.

For infrastructure management, I use it for EC2 spikes and memory leaks, as well as high memory usage. For applications, I monitor any errors or alerts we receive for application activity.

We receive emails and alerts for applications whenever migrations occur, allowing us to see the mobility of the application in operation and monitor how application usage progresses through Splunk logs.

The best feature Splunk Observability Cloud offers is monitoring activity.

Real-time monitoring is the most helpful aspect for me.

Splunk Observability Cloud has positively impacted my organization by providing real-time data whenever we migrate applications from on-premises to the cloud and by tracking customer interaction, customer activity, and orders coming into the application.

Since using Splunk Observability Cloud, I have seen improvements in customer experience and the ability to detect issues before they lead to an outage.

Currently, I believe Splunk Observability Cloud could be improved by adding more alerting options for the instances we add.

Regarding Splunk Observability Cloud's AI capabilities, I think it should have more safeguards on information data and sensitive data.

I have been using Splunk Observability Cloud for one year.

Splunk Observability Cloud is stable.

Splunk Observability Cloud's scalability is good.

The customer support for Splunk Observability Cloud is the best, and I would rate it a ten.

Splunk Observability Cloud's AI capabilities have ninety percent accuracy, as I did not use them extensively.

Splunk Observability Cloud has helped improve our operational performance, specifically in tracking and alerts, although I cannot include specific data points.

I assess Splunk Observability Cloud overall as a nine because it provides significant information data for monitoring purposes and an effective alerting system.

Splunk Observability Cloud has helped reduce our mean time to detect by ninety percent.

Splunk Observability Cloud is helping my organization focus on its business-critical initiatives by improving the alerting system and providing more realistic data when migrations occur, with all residency data appearing in the dashboard and displaying easily.

My advice to others considering Splunk Observability Cloud is to use it at the best scale.

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.

My main use case for Splunk Observability Cloud is for customer support.

Using Splunk Observability Cloud for customer support helps expedite my customer's needs and ensure that I have access to all the right data to answer questions quickly and effectively.

I have seen that Splunk Observability Cloud made a difference when it helped me with a critical customer workload when a part of their database was impacted by an event. I was able to quickly determine what was impacted and how to quickly isolate and do damage control. Thanks to Splunk Observability Cloud, I would not have been able to gather the information I needed so quickly.

The best features that Splunk Observability Cloud offers are all of the metrics and dashboards that are most important to myself and the management team.

What I appreciate about the metrics and dashboards in Splunk Observability Cloud is that they're easy to configure and edit and give me access to all of the data that I need.

Splunk Observability Cloud has impacted my organization positively by allowing management access to the logs, dashboards, and metrics they need to operate a high functioning team and product.

The specific outcomes I've seen with Splunk Observability Cloud include faster response times and lower MTTR. Some of the specific improvements I've seen are in faster mean time to resolution and easier damage control during critical outages and failures.

Regarding how Splunk Observability Cloud can be improved, there are few opportunities for improvement, but perhaps one area of improvement is greater integration with partners and clearer ability to quickly gather the information I need to supercharge my AI workloads using Splunk Observability Cloud.

The customer support for Splunk Observability Cloud is thorough and gave me everything I needed to make sure I was well supported.

On a scale of one to ten, I would rate the customer support for Splunk Observability Cloud a nine.

My experience with lowering the cost of unplanned digital downtime using Splunk Observability Cloud has been positive, as we've been able to lower our mean time to resolution and make sure that we are resilient and quickly recover from disasters.

My experience with pricing, setup cost, and licensing for Splunk Observability Cloud was very straightforward, and I felt supported the whole way through. There wasn't a moment in which I felt we could not get the information we needed on pricing and setup.

The advice I would give to others looking into using Splunk Observability Cloud is to take your time getting to know the product and make sure to get involved with the support team. Make sure that you feel comfortable adjusting what you need to adjust and have fun. I would rate this product a nine overall.

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.

Splunk Observability Cloud is used for day-to-day operations and monitoring of our Azure Kubernetes environment.

Splunk Observability Cloud monitors our Azure Kubernetes infrastructure, including our pods, nodes, and clusters in our Azure environment.

For monitoring of Kubernetes environments, we track pod health and node health using Splunk Observability Cloud.

Recently, Splunk Observability Cloud was used for monitoring our Kubernetes cluster. We have around 35 to 40 Kubernetes clusters. One of the pods was down and we received an alert. We investigated why the pod was failing, looked at the logs in Splunk Cloud and were able to troubleshoot the issue and resolve it soon.

Splunk Observability Cloud is used for day-to-day operations and monitoring of our Azure Kubernetes environment.

Splunk Observability Cloud monitors our Azure Kubernetes infrastructure, including our pods, nodes, and clusters in our Azure environment.

For monitoring of Kubernetes environments, we track pod health and node health using Splunk Observability Cloud.

Recently, Splunk Observability Cloud was used for monitoring our Kubernetes cluster. We have around 35 to 40 Kubernetes clusters. One of the pods was down and we received an alert. We investigated why the pod was failing, looked at the logs in Splunk Cloud and were able to troubleshoot the issue and resolve it soon.

The options I refer to include getting visualization of the data. For instance, when you configure your Kubernetes clusters, those will automatically pop up, which is a pretty good feature.

When there is an incident, we look at the dashboards. The beneficial aspect is that Splunk Observability Cloud has its own scenarios.

Unified visibility is not there.

Almost two years.

Whenever I go to Splunk Observability Cloud platform, the first thing I do is go to infrastructure. I look for Kubernetes, I look for pod health, and node health.

The first thing I do is look out for the Azure Kubernetes cluster. I have 35 to 40. I look at one to see how the pods, nodes, and everything is happening.

Whenever there is a health problem with the pod or node, I am able to find out that it is having some problem and I am able to view it.

Splunk Observability Cloud is deployed in a hybrid environment in my organization.

We use Azure as the cloud provider in our hybrid environment.

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

My overall review rating for this product is 6 out of 10.

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.