Splunk Observability Cloud

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.

Our main use case for Splunk Observability Cloud  revolves around the agriculture scaling ecosystem, which is heavily dependent on advanced automation in terms of predicting climate resilience, agriculture analytics, and predicting IoT activities, where we check all activities through drones. The drones are controlled through various other application platforms, and we are revolutionizing the Indian agriculture ecosystem by introducing Kissan drone operators, also known as Srishi drone operators, where these drone applications are heavily monitored and evaluated. Various application platforms need unified control and monitoring, which is accomplished through Splunk Observability Cloud . Many activities or loopholes go unnoticed and can cause serious issues later, so bridging those gaps and bugs necessitates the introduction of Splunk Observability  Cloud across all ecosystems.

Splunk Observability  Cloud's monitoring has significantly changed our day-to-day operations and decision-making, especially in drone operation and its monitoring, which is heavily dependent on real-time data insights and technological interventions. Many decisions must be made quickly and in a scalable manner, achievable only through a unified platform that delivers all these aspects. We are currently using several metrics to measure outcomes in terms of increased production efficiency and improved operating efficiency, along with insights into activities undertaken by farmers or data monitoring teams, all of which provide us with a transparent system within the ecosystem. Troubleshooting these issues helps our farmers and engineers prevent performance problems and downtimes.

The core capabilities provided by Splunk Observability Cloud application platform have been well-documented, and it is crucial to note that there are no lapses or sampling errors in our organization's performance monitoring. We achieve 100% accuracy when monitoring application performance and providing data dashboards to our senior management. We have never missed a trace while analyzing transactions across different services offered to farmers. Infrastructure monitoring is equally vital for us, given our multiple servers and multi-cloud environments where various agricultural applications operate simultaneously and data flows seamlessly. All of this has only been possible because of Splunk Observability Cloud. Our digital enhancement experience has improved multifold, especially with the introduction of AI-powered insights over the past two to three years, allowing for pinpoint guidance in detecting anomalies, identifying root causes, and significantly reducing alert fatigue while enhancing overall efficiency.

The best features of Splunk Observability Cloud include full-stack application monitoring, which is very easy to navigate. The platform consistently demonstrates no performance downtime, even on weekends, which bolsters our client's trust and confidence. Predictive analytics powered by AI insights provide us with real-time data matrices and insights, significantly improving our customer experiences and accelerating innovation. Identifying potential threats and issues, such as root causes, has become very straightforward, leading to our immense satisfaction and gratitude towards their responsive business team. Their multidimensional features provide unified security and substantially enhance visibility, which perfectly aligns with the concept of observability.

One standout feature is full fidelity monitoring and proactive troubleshooting, especially with approximately twenty to twenty-five applications concurrently used across multi-cloud environments, managing data transfers and inputs efficiently. I recommend that more flexibility be included in launching applications and features. The database standard integration is incredibly beneficial, as is checking each data layer in a full-stack environment, something which Splunk Observability Cloud handles excellently.

Splunk Observability Cloud positively impacts our organization by significantly increasing overall visibility and observability experiences for the entire team through numerous newly introduced features. Previously, we lacked visibility into query logs, but now we can track and trace these logs effectively for problem identification and troubleshooting. As a result, the reoccurrence of similar issues has dramatically decreased. We now have structured logs and tracking that are amazing, and the user experience, especially for our clients—primarily farmers using less developed Android phones—is vastly improved. The application performance monitoring criteria make navigating the platform easy and clear, allowing us to perform hygiene practices for coding. Our on-premises deployment has proven advantageous in monitoring the health of our cloud environment, and we are recommending this to others. The scalability as we have grown from three to twenty-five platforms has been seamless; our system hasn't crashed, indicating stability.

The metrics from utilizing Splunk Observability Cloud clearly show improvement, especially in downtime reduction. Previously, we faced a systematic performance lag of around twenty to thirty percent, which has now reduced to just two to five percent—an improvement we can credibly showcase to our clientele. We now collect and track traces, query logs, and session data effectively, providing us with credible, quantifiable metrics for assessing business enhancements and current operational stages. Real-time visibility and data fetching for dashboards is an extraordinary addition that distinguishes our experience.

There are several performance enhancement areas for Splunk Observability Cloud. For instance, Splunk Observability Cloud's IT service intelligence core part needs improvements as clients request more IT services performance matrices than the current system supports. Certain matrices are still unnoticed, creating false alarms that require enhancement. We previously used Datadog  and other AWS  observability solutions that were quite affordable. Currently, smaller businesses struggle to reap the benefits. UI navigation is easy but could use polishing for a better experience. Integration issues arise with some services taking longer than expected to connect properly, which is an area for improvement.

An area needing improvement is the AI-driven anomaly and issue detection system, which occasionally generates many false alarms that consume our time. We also face challenges with metrics not communicating across different measurement platforms, which requires addressing regarding log-specific queries. Additionally, I suggest extending the trial period beyond thirty days to forty-five or sixty days, allowing more time for our team to understand the software's functionalities and business use cases.

The accuracy and reliability of Splunk Observability Cloud's outputs have been consistently impressive. No one can question accuracy due to its proven record, as many large organizations depend on it for application performance monitoring. Splunk Observability Cloud excels in troubleshooting cloud applications, and whenever customization is needed, it is smoothly introduced. Overall data insights gathered during critical platform phases are near 100% accurate, with no identified lapses in the data monitoring processes we have employed.

Splunk Observability Cloud significantly enhances our operational performance and company resilience. With automation in place, we enjoy improved customer experiences based on impactful business insights that help our clients make sharper decisions. This solution allows us to project future performance accurately and identify data anomalies while managing incoming threats effectively. The integration of this solution is straightforward and open-source, enabling users with basic knowledge to adapt without difficulties. We have also eliminated other monitoring solutions, consolidating everything onto one platform for greater efficiency.

I have been using Splunk Observability Cloud specifically for approximately two to three years, utilizing the platform for monitoring and automating the agricultural activities in our various ecosystems.

Splunk Observability Cloud is stable and scalable, effectively monitoring and supporting real-time operational needs. The system's steady performance includes customization capabilities tailored to different organizations, enhancing transparency across all systems while remaining highly reliable.

I would rate customer support for Splunk Observability Cloud a perfect ten out of ten. Their team is available twenty-four-seven, with ideal resolutions usually achieved in one to two days, and even complex issues resolved within a week. The accuracy in addressing tickets is commendable, ensuring efficiency in problem-solving.

Previously, we used other solutions, including Grafana  and Signos for around two to three years before deploying Splunk Observability Cloud.

Pricing and setup costs were reasonable, initially about a hundred to a hundred twenty dollars for a variety of additional features along with the regular offerings. We increased from fifteen to eighteen dollars per month per user, which remains affordable and manageable. Licensing is renewed annually without significant issues over the past three years.

Before deciding on Splunk Observability Cloud, we evaluated other options, particularly Absolute Fleet , which is regarded as stable and scalable. During deployment, we also tested Signos and Datadog , well-known for observability and security platforms offering comprehensive log management.

We have seen a return on investment with Splunk Observability Cloud. The solution's affordability and high metric index create substantial benefits for our observability needs, allowing easy configuration and automatic adjustments without requiring excessive API usage. We have saved considerable amounts of money, reducing our expenditures from around three to four crores to approximately one to one point two crores. Our application performance monitoring has shone brightly, leading us to exceed targets and numbers that were previously unattainable.

For our organization, it is crucial that Splunk Observability Cloud provides end-to-end visibility into our cloud-native environments, especially given the government's audit parameters in India. Splunk Observability Cloud's global ratings above 4.3 reflect its excellent service in providing insights across various infrastructure layers. Customization becomes attainable when our team utilizes personalized navigators for better visibility, particularly regarding sensitive user data. Their monitoring does not retain information and maintains confidentiality, adhering to data protection policies, which has contributed positively to our experience.

When assessing Splunk Observability Cloud regarding our organization's growth, the initial client base of four to five has expanded to twenty to twenty-two, forecasting further growth shortly. Our turnover time has decreased significantly, allowing us to perform better practices and enhance confidence in our scaling efforts. Our cloud monitoring team feels empowered due to the accuracy in reporting, satisfying clients and promoting a win-win scenario for everyone involved.

Splunk Observability Cloud enables us to transition our team from repetitive tasks to focusing on critical business initiatives. Instead of spending time on trivial activities fetching dashboard reports, our team can now concentrate on creating strategies for the upcoming months and quarters, maximizing the utility of our human resources. This shift has also fostered a research-oriented approach, allowing us to explore advanced cloud infrastructure options beneficial for our ecosystem.

The out-of-the-box dashboards and detectors in Splunk Observability Cloud are exceptionally advanced and enable us to integrate various platforms without experiencing downtime. This maturity facilitates a unified integration approach tailored to our needs and reflects the team's understanding of user cases. The system effectively mitigates challenges faced by other software during integration.

Since introducing Splunk Observability Cloud, the mean time to detect issues has certainly improved, allowing us to identify potential threats and cyber security issues proactively. Before the implementation, identifying issues took a month; now, we can recognize red flags twenty-four to forty-eight hours in advance, facilitating timely strategic adjustments across teams.

My advice for anyone considering Splunk Observability Cloud is to deeply explore the product page before obtaining a license. Understand  the features available, and engage with the customization team to address any inquiries. Familiarize yourself with on-their-top guided workflows that clarify processes and enable informed decisions. I would rate my overall experience with this product an eight out of ten.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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