Splunk Observability Cloud

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.

I work in a financial domain company where we manage customer applications and use Splunk Observability Cloud for event connectors, metrics, and traces.

We monitor our applications and track customer journeys as our metrics and traces for improving our applications' visibility and reliability of our platform.

When I compare Splunk Observability Cloud with other products available in the market, I feel it is very comprehensive.

It gives me the flexibility of parsing data, and at the same time, I can write SQL queries, search my data, have an alert mechanism, dashboards for visualizations, create reports, and monitor my infrastructure using that application.

Multiple benefits come from having Splunk.

This single product can handle my infrastructure observability end-to-end, not just for the application, but also for the underlying infrastructure.

Splunk Observability Cloud has helped to improve not just the operational performance but also the entire enterprise platform reliability, as well as the availability of the entire platform, which has greatly increased because of it.

If something is about to happen or some of my servers are having issues, Splunk triggers an alert before something actually happens in the production.

It gives me that flexibility, so I can monitor and check the health of my underlying infrastructure.

Before something major happens, it alerts me so that I can look into this, investigate, and fix it.

The out-of-the-box customizable dashboards provided by Splunk are really good.

Whatever my requirement is, whether tree leaves or chloropleth or any other kind of dashboard, they are really good.

The customization helps me to create a view that is specific to my requirement, so they are really helpful.

The tool is very good, but I feel it is very bulky.

For a huge organization, it is easy to get the license and manage costs, but for smaller organizations with just fifteen or sixteen people who have limited data ingestion, it will be a little expensive.

Managing Splunk requires professional people; you need a team to oversee the entire Splunk setup, not just one engineer.

If Splunk could provide at least a beta version that is not this enterprise-heavy, that would be a great thing.

The pros of Splunk Observability Cloud are that it is enterprise-specific and a very comprehensive and extensive tool that covers almost all wider areas where an observability platform is required.

The cons are that it is quite heavy; you require dedicated servers to handle your Splunk Enterprise instances, rather than just being able to install Splunk onto an application server or any other server.

You need a heavy infrastructure to maintain these Splunk Enterprise instances.

I have been using Splunk Observability Cloud for the last six or seven years.

Splunk Observability Cloud is very stable.

I have never seen any glitches or experienced any lag unless the underlying server is unhealthy or the underlying infrastructure is not good; Splunk remains very stable.

Splunk Observability Cloud scales very well with the growing needs of my organization.

Scaling up and scaling out is easy, but only if I know how it has been installed and how it has been set up.

I just create other instances following the same standards as the initial ones.

However, if the pre-work is not done well, then it becomes very complex.

The technical support team of Splunk is very prompt and helpful.

There are three mechanisms to reach out to them: via call, email, or chat support.

Email responses take twenty-four hours, call support is instant, and we have opened on-demand services with this plan.

I would rate their services as eight point five out of ten.

Whenever we raise cases with the Splunk team, they are very prompt, and we have that on-demand support, which is really good.

I have previously used Cribl, which we use very extensively along with Splunk for our observability, in addition to Grafana and an in-house tool called ELK.

The initial setup and deployment part of Splunk Observability Cloud is complex; it is not straightforward.

To set it up in a professional way, as an enterprise would, requires a lot of professional hands.

It is not as easy as just installing and being ready to use.

To make the best use of it, everything has to be standardized.

I find it cost-effective in larger organizations like financial companies such as Goldman Sachs, JPMorgan, or Chase. For huge enterprises, it is easy to get licenses and set up a team, which they actually need because these organizations require professional people to take care of observability.

In reference to smaller startups with just fifteen employees whose requirements are not much but want to set up observability, it may be different.

However, for my organization, it is very cost-effective.

I directly purchased Splunk from Splunk Enterprise, not through the AWS Marketplace.

I am using Splunk, Cribl, ELK, and Grafana.

These are the platforms that I am using.

The customizable dashboards in Splunk are definitely helpful when it comes to showcasing IT performance to business leaders.

The reason is that leadership does not have much time to go through events and summaries, so they visualize.

When we present anything in visualized form, it is catchy and easy for them to look into the KPIs and metrics rather than going through the entire data and summary.

I do not think there are any missing features in Splunk that I would like to see included in the future.

As far as I have explored Splunk, I feel it is a very comprehensive tool.

It does not miss out on anything.

I would give a really positive review about the No-Sample Tracing feature in Splunk.

It is a good feature that Splunk has.

Having this AI-powered analytics and guidance from Splunk helps me in issue resolutions.

Splunk has that AI capability, which aids in writing the queries, and if I want to create something, instead of navigating and doing it myself, I can just create a prompt, and the prompt helps me to create those kinds of features in my Splunk.

We are using the ability to enrich data with custom metrics in Splunk Observability Cloud.

We create Excel files, upload them, and that reads specific items, enriches the data, and removes duplicates and unnecessary events, providing the key-value pairs that are actually required for us. It is very helpful.

My advice for others looking into implementing Splunk Observability Cloud is straightforward.

If a person who is purchasing Splunk Enterprise has more than five hundred users, it is good to go for it.

Splunk Observability Cloud is very effective.

If I were to rate it out of ten, I would generally give it somewhere around eight or eight point five.

Splunk Observability Cloud has effectively helped me lower my downtime.

Since I have a platform that is constantly monitoring my infrastructure and keeping an eye on the health and metrics of my infrastructure, if any applications or servers are about to go down, there is a lot of noise on that specific infrastructure, and it triggers an alert so that I know what is about to happen.

I can investigate that before something actually breaks.

At the same time, if something goes down, I actually know the root cause of where it has broken.

It has helped me greatly.

Instead of investigating end-to-end, I know the root cause prior and where to look into, so it has helped us a lot.

I gave this review a rating of nine out of ten.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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