Cisco Cloud Observability Operators

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.

I am Purnambica Kolavennu and I have been working for the past two to three years with the Agriculture Skills Council of India. I work at the intersection of technology and AI innovation in the agriculture skilling platform. I have been working in a similar portfolio for approximately five to seven years.

We have been using Splunk Observability Cloud for the past two years. At the intersection of agriculture and AI innovation, real-time visibility monitoring and analytics across the entire agriculture skilling ecosystem is extremely critical. We are helping various organizations, clients, training partners, assessment bodies, and government stakeholders improve their service delivery, audit compliance, and ultimate outcomes.

We are doing end-to-end monitoring of skilling platforms through our unified cloud-native SaaS platform, which is Splunk Observability Cloud. We are troubleshooting any kind of foreign incumbents or cyber threats. We are monitoring the health and performance of various systems including training systems, assessment portals, attendance portals, and learning management systems. All these components are monitored end-to-end. The entire monitoring of our training and assessment activities has become extremely easy, and we are able to make our clients happy with this solution. We are monitoring the real-time dashboard for PMKVY and government schemes.

Splunk Observability Cloud is helping us derive and fetch data-driven decision-making insights. These fresh insights help us take better decisions and improve scheme monitoring for PMKBY, PM Vishakarma, and other state programs. When monitoring assessment operations, our daily activities are critically handled here, and we have been able to reduce our assessment delays. We are able to make better compliance with guidelines and norms, and we can early detect bottlenecks or foreign threats or cyber threats coming into the system.

Through this platform, we have started doing automated identification of eligible and non-eligible candidates, which has improved the system greatly. Now there is more transparency and credibility and authenticity in the entire process. We have also reduced the manual verification effort, which has greatly helped our entire team. We are doing many activities in predictive analytics where we are trying to identify and gauge early intervention to protect scheme guidelines and enhance infrastructure performance monitoring of our various applications in a unified manner.

We have been able to have more traceability because the Log Observer Connect is a very useful functionality that has been given to our clients and various vendors. Through this, centralized log data for visibility is available to all partners, which allows seamless flow of information and data. We have been able to have more AI and automation integration, through which our manual effort has been reduced. We are doing specialization monitoring of the AI agents and our infrastructure stacks.

Splunk Observability Cloud is a very strong cloud-native SaaS platform designed for monitoring and troubleshooting cyber environments. The mean time to resolution functionality, which is an MTTR functionality, enables different kinds of intervention. Application performance monitoring is a very strong feature providing deep code-level visibility into distributed applications. It is also helping us manage each data flow into the system from one end to another end. Infrastructure monitoring is most important because improving operational efficiency is very critical for our organization. For that, we need real-time streaming analytics and automatic service discovery, which we are able to achieve through infrastructure monitoring. Real user monitoring of various applications is also a very strong feature, capturing the complete end-user experience from both web and mobile applications. The application proactively monitors service performance, APIs, and URLs globally before end users are impacted.

The strongest feature is unified application performance monitoring. We are working on approximately twenty-plus applications at a time, fetching data insights from them, and tracking their performance and identifying any bottlenecks or cyber threats. This would be extremely difficult if done manually, as many eyeballs looking for performance metrics would be necessary. This unified SaaS platform is helping us out and giving us full visibility and full business control, providing full business control in terms of mapping all performance metrics, deriving decision-making insights, and helping our people.

There is a very strong aspect of compliance monitoring and audit readiness for our organization because we are directly governed by the government of India. Fraud detection is helping us a lot because there are many times when confidential information and data flow are attacked by suspicious login patterns, unusual duplicate registrations, unauthorized system access, and anomalies that come into the system in the form of cyber threats. The system is doing twenty-four-seven monitoring of the various applications we are using, and because of that, there is enhanced compliance and improved audit readiness, which has actually reduced the risk of malpractices. The system is keeping us very clean in terms of compliance practices, and our service delivery has been excellent. Our stakeholder service management experience has increased greatly because clients are happy, there is faster issue resolution, and satisfaction is being built with our clients.

There is a very strong performance monitoring feature where we are able to track the entire performance metrics end-to-end because there is a single performance control center through which all dashboards and application visibility comes to us in just one click. There is real-time monitoring happening, which is helping us a lot. Our operational productivity and efficiency has improved. There is better audit and regulatory compliance now. Our clients are happy because they do not have queries raised on their processes. Twenty-four-seven issue resolution metrics are incorporated, which is helping us a lot. There is predictive insights, which is a kind of proactive insight, helping us in decision-making. There is strong unified governance. We are seeing a thirty percent to fifty percent reduction in incident resolution time, which is helping us a lot. There is higher compliance metrics, improved batch completion rates, and better visibility of all processes and systems. The strong data-driven decision-making is creating a very strong ecosystem and environment throughout.

There is a twenty-four-seven ticket resolution system and a very advanced chatbot system, which is a humanized form of chatbot system that works on a four-to-six-hour resolution time. For many of the applications, resolution time is twenty-four to forty-eight hours, but for them, it is approximately four to six hours. There is an embedded feature that if an issue is not addressed within thirty minutes or within one hour, then it is escalated to a higher level authority and quick resolution time is attained. Our manpower in terms of resolution and in terms of following up on tickets has been extremely reduced. Through the chatbot system, strong chatbot system support, and their email support, we have been able to reduce our burden greatly.

Log Observer Connect is embedded here, but we are facing some delays in centralized log collection and analysis, which can be further fastened. We are collecting all the data metrics and decision-making insights, but all these data-driven decisions coming from different applications are not connected somewhere. A consolidated form or correlation of these insights is not happening between each other due to which we feel we are missing something significant.

Some generalized feedback includes that predictive alerts or alarms which can be integrated with AI-driven alarms and alerting features should be established so that there is AI-driven intelligence and anomaly detection happening with a complete systematic process in service delivery. Application dependencies are huge, and business and operational dashboards should be improved. Right now there are very interactive custom dashboards, and every now and then, the personalization of enhancements keeps happening. KPI monitoring, executive reporting, and analytics have definitely been introduced to a great extent. There are few things in cloud-native monitoring, such as integration with AWS and Azure, where we sometimes do face lags. Those things can definitely be improved upon.

I have used Datadog and Dynatrace before using Splunk Observability Cloud. Datadog was definitely recommended by most of our peers because of its very strong comprehensive observability and very strong and unique dashboard systems. Dynatrace was also very good because they have offered a lot of AI-driven analysis methods and processes, which was helping our organization a lot. Since our organization has a very strong IT ecosystem for agriculture, very different kinds of customized things are required.

Splunk Observability Cloud is very, very stable. We are using approximately twenty-plus applications, and the system has the capacity to increase applications to up to ninety. There was a time when we were having sixty to seventy applications to be monitored in one go, but there was never any outages or downtime. We have never faced any kind of downtime or performance issue. It is highly scalable because it can handle approximately up to one hundred applications at a time without any lapse or lag.

Scalability is huge for our needs. We are able to use it in our native cloud environment, but we also have external cloud environments of our client servers with very different configurations. The API integration is so smooth with those external client servers that there is never a scalability issue or compatibility issue that we have seen. We have never seen any kind of downtime or crashes, as it has been absolutely very easy to scale. If I am working on twenty different applications today and tomorrow I want to scale it up to fifty different applications, everything can be done easily without any downtime or outages.

Customer support is great. The turnaround time for solution is extremely good. They are available twenty-four-seven with an advanced AI-driven chatbox system, and they are resolving issues within four to eight hours, which is commendable. The customer support system is the foundational pillar of any successful business, and the team has greatly excelled at this.

I evaluated Datadog and Dynatrace. Datadog was very highly recommended by most of my peers because of its strong comprehensive observability and unique dashboard systems. Dynatrace was also recommended because of the strong AI root cause analysis. We also checked for new solutions but could not find the best deal with them, so we ultimately switched to Splunk Observability Cloud.

Many features keep adding up every now and then as per different requirements and as per the changing business environment. We request their business team and tech team to do capacity planning or capacity development sessions every now and then so that there is uniform training happening across the ecosystem. Our new incumbents, new learners, and new tech executives are learning those new systems every day, and there is no mishap in understanding. This would definitely enhance user experiences and provide better orientation and better understanding of the systems, processes, and how the application actually functions and what the various utilities are.

We have reduced our employees from approximately ten to twelve people working in this vertical to five. We have reduced our operational expense by forty percent, and we have reduced our operational burden by nearly ten percent in the form of multitask management, which was done by human intervention or manual intervention. We have been able to save a great deal of money, and our profits have increased by twenty percent. Initially, even after one year of deployment, we were in profits.

The pricing and initial setup cost were a bit pricey for us. However, we have done a lot of negotiations with the business team, and now we have gotten a reduction of approximately ten to fifteen percent. Their licensing has annual renewal, so we are doing every year SLA agreements with them and renewing it.

I would definitely give Splunk Observability Cloud a nine out of ten rating. The unique strengths include strong application monitoring infrastructure, a very comprehensive observability environment, and a very powerful native cloud environment. There are strong dashboards for real-time visibility twenty-four-seven, and it is suitable for large enterprises. I would say it is best for large enterprises because their personalization and customization is extremely good and suited to the requirements and needs. Unlike other observability cloud applications, this is very advanced, and AI root cause analysis keeps happening throughout, due to which even complex IT ecosystems or complex integrations are handled very easily. There is full stack monitoring happening, and there is excellent log analytics, which is actually helping us a lot to make faster and better data-driven decision-making. Splunk Observability Cloud is extremely reliable and an extremely trusted source, and it has definitely gained public faith and public trust. It is a highly recommended application.

With the small enhancements or improvements regarding integration and doing a lot of training and orientation time and again to make the system more compatible and understandable for all, it could definitely be a ten out of ten.

There is very strong governance and security. The policy processes are very strongly governed. Government cloud ecosystems are very susceptible to any kind of threat attacks, and there are a lot of system bridges built there, with a lot of stake involved. The system is giving a lot of advanced use cases, such as Google Cloud-based applications which are very secure. They are hosting the entire program on another platform and also creating a duplicate of it, due to which there are various strong audit processes that have been inbuilt. There has been real-time observability all the time so that there is no such any problem. All of this is very cost-effective, so it is definitely very strongly compliant with built processes.

Accuracy and reliability are excellent. We are dealing with approximately millions of data every week, and the system runs throughout the day continuously running on those data and bringing data and insights to us. In the past two years, I have never seen any data mismatch or inaccuracy. There is strong trust built in where there is no data leak, no data misinformation, and nothing leaking or any kind of information going out of the system. Accuracy and reliability are very strong features. My overall review rating for Splunk Observability Cloud is nine out of ten.