Splunk Observability Cloud Reviews

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 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.

My primary use cases for Splunk Observability Cloud include alerting the business and the integrations app team, which are the largest users of Splunk within our company. They take up most of our ingest and have many alerts set up, along with log analysis and events analysis. 

Those are our biggest team users, and alerting in general plays a crucial role for incident creation across multiple teams, regardless of who the shareholders are, cutting across multiple teams.

Splunk Observability Cloud has significantly improved my operational performance and my company's resilience. I would evaluate the effectiveness of Splunk Observability Cloud in improving digital resilience within my organization as very positive. It's easier to access for both Splunk experts and users who may not be familiar with Splunk, allowing even non-technical people to navigate it.

The features I appreciate the most about Splunk Observability Cloud are the ServiceNow integration feature, which is very seamless, and that's probably my favorite since ServiceNow is big in the observability industry. Being able to seamlessly create those incidents is the biggest plus. Without that, we probably wouldn't be using Splunk.

Just alerting with Splunk Observability Cloud provides significant benefits for my organization, particularly since this is a food and manufacturing company. Alerting the business side before the customer reports an issue helps us to be one step ahead, which is a big feature.

Dashboards for the IT executives are also beneficial since they might not be as technical yet can easily read a dashboard. The ability to create something easily interpretable, using color coding and heat maps, allows directors to see value in it.

The ability to enrich data with custom metrics in Splunk Observability Cloud has significantly impacted our integrations and app team, the biggest users of Splunk in our environment. The visibility provided and the remediation they gain from reading events have led to increased log ingestion, which is a good problem to have. They can confidently rely on Splunk for monitoring app creations, APIs, and more.

Splunk Observability Cloud could be improved in terms of integrations with more technical add-ons, such as Zoom. Although they have one with Zoom, it's not available in the cloud, so having that feature would be beneficial. Essentially, Splunk should continue expanding to create easier ways to ingest logs from different products.

The out-of-the-box customizable dashboards in Splunk Observability Cloud are very effective in showcasing IT performance to business leaders. However, there are aspects that could be improved, such as linking dashboards to one another. While IT leaders may not drill down, it's crucial to create levels of dashboards for technical users to find root causes, making it effective for stakeholders.

I have been using Splunk Observability Cloud for two years, as my first cloud was with ABC.

I have experienced slowness from Splunk Observability Cloud occasionally, yet we have not faced crashes or major performance issues. When slowdowns occur, we reach out to support for explanations and have received adequate responses.

Splunk Observability Cloud scales efficiently with the growing needs of my organization, even requiring occasional license increases. Maintenance is done by Splunk, and we receive alerts about maintenance windows to ensure we stay current. So far, we've experienced seamless operations without any breakdowns.

I find Splunk's customer service to be great compared to many other products. They do a good job of responding within 24 hours or less, even with P4 issues. They may reroute you a couple of times. Overall their support is commendable. I would rate technical support and customer service a solid eight.

Positive

Prior to adopting Splunk Observability Cloud, I was aware that our company used another solution. I don't recall what it was since I was brought in specifically for Splunk.

I wasn't a part of the deployment process. 

I have definitely seen a return on investment with Splunk Observability Cloud, particularly through how fast it has grown and how comfortable other teams are in relying on its outputs for monitoring and observability.

I don't directly deal with costs, so I can't comment on the return on investment regarding lowering unplanned digital downtime using Splunk Observability Cloud.

I haven't explored the no-sample tracing feature in Splunk to eliminate blind spots in data collection. 

AI-powered analytics and guidance provided by Splunk Observability Cloud will be very beneficial. We just initiated a response to get those AI functionalities into our cloud environments, so we haven't fully explored it yet.

My advice to other organizations considering Splunk Observability Cloud is to get it, especially for log monitoring and alerting. There aren't too many observability tools that match its ease of use, whether for IT-oriented users or not. Its graphical user interface is brilliant and very seamless, making it easy for anyone to navigate. I'm confident big companies considering Splunk should choose it, as it delivers in usability and integration with other tools.

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

I have been using Splunk Observability Cloud for the past one year in my career. Splunk Observability Cloud has been introduced to our project for end-to-end monitoring for applications, providing complete visibility of applications, services, tech stacks, and CIs, which constitutes the whole monitoring solution for an entire application.

Previously, we were using different monitoring systems such as Dynatrace, the competitors of Splunk, and even Splunk Cloud Platform or Enterprise platforms for logging alone. Now we have the entire solution under one name and one platform, which is Splunk Observability Cloud, and that is why we mainly introduced Splunk Observability Cloud to our project.

The UI is quite understandable, making it not as complex when compared to the other previous platforms I have worked on. Another thing I could specifically point out is that we can have entire visibility for the entire application performance when we look into Splunk Observability Cloud, and it is much easier to navigate across various aspects such as real user monitoring, application performance monitoring, or synthetic tests, making it stand above the other previous applications I have worked on previously.

One thing I should point out is that there are some auto-detectors which are defaultly present in Splunk. For example, if you are configuring a detector for AWS RDS service, you have an auto-detector which detects what the technology is, and you will have a readily available detector, needing only to configure your specific metrics on that, which is one advantage. The dashboards especially stand out, being different compared to the other platforms. Even previously, Splunk Enterprise also had dashboards, but this is different as we can have live metrics through the dashboards, which is quite impressive with how Splunk Observability Cloud has been introduced and it is performing better than the previous Splunk versions.

When we have too many detectors in place for one particular app, such as when I have created 50+ detectors through my account, the entire page becomes a bit loaded when creating the 51st detector, feeling heavy and taking time to load. Additionally, it throws random errors; for example, when we try to save one detector, it might throw some random error which is not even related, with something else being wrong, not that particular error, but the underlying root cause might be different. Sometimes the error is just "some problem occurred," and we are not able to point out what the real cause is.

This mainly happens when we have too many detectors or too many alerts in place rather than a standard number. One more thing is in the alert rules; if we have a main general alert, and instead of creating a new detector, we are adding a new rule under one detector, when the number of rules also increases, such as when we have 10 or 15 rules under one generic detector, that again creates the same kind of problem, taking some time to save that particular newly added rule, and it might not save at times, just keeps on spinning. Those are the two drawbacks which I spotted recently; other than that, everything looks perfect.

There was an outage which occurred about three or four months ago; that was the only outage I faced entirely in one year, and I believe that was a global outage from Splunk's side, which prevented us from logging in for a couple of hours. The Splunk team was working on it, and they resolved it within five or six hours, which was the only outage I faced in one year. Other than that, everything was smooth.

Splunk Observability Cloud is quite scalable compared to the other platforms I have worked on, and I do not find any difficulty in scaling up or even scaling down.

For particular kinds of issues which we were not able to resolve, we have raised Splunk tickets a couple of times before contacting the technical support or customer support. The support is an eight out of ten. The speed is actually quite good; they would respond within 48 hours, and the solutions they are giving are quite good, as we were able to solve most of the issues with their solutions.

Positive

I have used Dynatrace and DataDog as alternatives to Splunk Observability Cloud.

The initial deployment of Splunk Observability Cloud is actually easy. With the clear documentation we have in place, it is quite straightforward. We even have examples of code snippets in the documentation, making it quite straightforward.

One or two people can manage the deployment; you do not need a team of five. I have myself worked on an entire project, and with one of my colleagues, I have worked on a much bigger project. I believe one or two people can easily manage the deployment process.

Splunk Observability Cloud has helped me reduce my mean time to detect. We have worked on around 80 applications last year for one particular client, and since the MTTR has improved drastically, they have given us 245 applications, which is around 150 applications added to the previous number of applications. This is definitely a performance improvement.

The pricing area I am not particularly aware of because that is centrally managed by the company which I work for. However, I feel that we are not spending too much on the licensing cost; it is manageable for how much we are working with currently for the number of applications we have at present, which is what I believe is not too much.

I would prefer Splunk Observability Cloud any day when comparing these solutions to Dynatrace and DataDog because the first thing is that the documentation Splunk has is perfect, and anybody who is new, even new to the platform, can gain knowledge reading through the documents, which are perfectly explained for configuring various kinds of technologies and integrating various kinds of technologies with Splunk Observability Cloud. The second thing is the UI, which is much more user-friendly compared to Dynatrace and DataDog.

The No-Sample Tracing helps me eliminate any blind spots in my data collection because we have particularly many services, and for example, using Mule, it is an added advantage to use no-sampling traces provided by Splunk Observability Cloud, giving us the exact points where the service is emitted and the exact spans between the two endpoints. It helps us break down where the actual issue is rather than just getting sample trace data and looking into each point entirely, which takes much time, providing a particular breakdown of that span and how it navigates across endpoints and pointing out the particular error which occurs when we access a service or when a service travels across two endpoints.

As of now, we are not using any AI tools in Splunk Observability Cloud, but we are planning to onboard them, considering that the number of applications we have is increasing day by day. We are planning to automate a few applications to generate the detectors and synthetics automatically as soon as we have the metrics in place. For that, we are developing code that can integrate with Splunk Observability Cloud platform and generate the results, which will be a time saver for us.

The recent UI changes have been more streamlined. Initially, the UI was a bit different, but later, I believe a couple of months ago, maybe one month before, they re-modified the menu options, and that has also resulted well for us in navigating across the panes.

I have utilized the ability to enrich data with custom metrics in Splunk Observability Cloud; I did it for custom metrics for AWS services, and for a couple of MQ, IBM-based MQs, we have worked on custom metrics, integrating easily in both cases. My company may have partnerships with Splunk, but I am not sure of that. I would rate this review as a nine out of ten.

My use case is the APM model, as we have the APM and since I mentioned I have all the flavors of Splunk, from Splunk Cloud, Splunk ITSI for specific event management, Splunk Observability Cloud is specific to the APM model. When I say APM model, it is infrastructure monitoring, synthetic transaction monitoring, and real user monitoring.

With the help of Splunk Observability Cloud, I can monitor all the real-time transactions happening in the entire application. With the help of OpenTelemetry, I installed everything and got the logs, traces, and metrics. I created the dashboard since I have the data within the Splunk Observability Cloud. As I have the observability data within Splunk Observability Cloud, this observability cloud will not go with event management.

If I want to go with event management, we will be sending the data to Splunk ITSI. Combining these, Splunk Cloud, Splunk ITSI, and Splunk Observability Cloud, we achieve the Splunk full-stack observability. This means I can monitor the overall application availability, including the REM, synthetics, all the APM, and the network observability coming from the arm. We can achieve full-stack observability with the help of all these Splunk stacks.

Splunk Observability Cloud has enhanced our operational performance and our company's resilience, ultimately contributing to improved customer satisfaction. Regardless of the business, customer satisfaction shouldn't compromise or degrade performance; it should remain optimal. Since I have these features available and inbuilt within Splunk Observability Cloud, building all these functionalities only takes seconds. I might encounter a little distraction; I can't say it is 100% perfect, however, it is about 95 to 96% resilient to whatever business we have.

The best features of Splunk Observability Cloud include the Splunk Assistant and the Splunk AI Assistant, which help in triaging issues and identifying them immediately, allowing me to create dashboards in a matter of some minutes or seconds. We can monitor all the Kubernetes, Docker, and whatever platform we have, including AWS, Azure, and GCP.

We can monitor all the AWS CloudWatch logs, GCP stacks, and storage. Everything can be monitored because we have the out-of-the-box dashboard add-ons. You don't need to do anything, but you might have to work a little bit; however, everything might be inbuilt, allowing me to fetch everything and monitor from all platforms, whether private cloud, public cloud, or data centers like Equinix.

The benefits I can see from using Splunk Observability Cloud include faster and quick resolution, allowing me to identify the root cause analysis, or RCA, for whatever issue is currently going on. This is the first analysis and first RCA. That is one of the most significant advantages or features we can achieve with the help of observability, offering simple and fast resolution.

Splunk Observability Cloud has indeed helped reduce downtime, as it has inbuilt operations that send triggers in real time, even for false alerts or positives, within seconds. It can identify anomalies and future forecasting. If an anomaly continuously occurs, it indicates something has gone wrong. It may not be a real issue, but something is about to happen. In such cases, we raise an incident or utilize the ITSM tool integration to immediately inform the next dealer group, ensuring quick action is taken.

Regarding blind spots, I haven't seen significant issues in this aspect with Splunk Observability Cloud. However, if one wants to eliminate blind spots, it may require writing some technical mix, which I haven't explored in depth.

In terms of AI-powered analytics and guidance provided by Splunk, we have two AI features: the Splunk AI Assistant and the Splunk Commander AI. The Commander AI embodies a generative AI functionality, connecting with LLMs. According to LLM standards, various algorithms are written to perform specific functions. As soon as the request comes from Splunk, it connects with the respective LLM, which will select the algorithms you need. This is an inbuilt AI module within Splunk, and the only requirement is a specific add-on called the Splunk MLTK, which stands for Machine Learning Toolkit. Having this MLTK add-on, tightly coupled with AIOps capabilities and ML, yields good results in AIOps operations.

Regarding the effectiveness of the out-of-the-box customizable dashboards provided by Splunk Observability Cloud, they showcase IT performance very well. It includes multiple add-ons and intelligent integrations, but it is crucial to ensure that data comes to Splunk Observability Cloud via the OTel Collector and universal forwarder. Once data is received, I can create dashboards for GCP utilization or AWS CloudWatch details. Although there may be numerous metrics to monitor from AWS and GCP, not all are critical, so the dashboards have been constructed to focus on the critical parameters we want to monitor.

In my organization, we have built observability for many applications. Although I cannot disclose specific account information, I use it for development purposes with a minimum number of users. User numbers can extend based on infrastructure, environment size, and data ingestion rates. The maintenance of Splunk Observability Cloud is very easy; it's manageable.

For potential areas of improvement, I find that while Synthetics, APM, and infrastructure management models are fine, an enhancement could be seamless integration with some third-party tools. It should better support interactions within Splunk tools.

If a customer utilizes third-party tools and wants to forward data from Splunk Observability Cloud, seamless integration would be beneficial. This is crucial for passing data to tools such as Dynatrace or Grafana, as integrating some third-party add-ons can be challenging, involving many implementation and configuration steps.

I have been using Splunk Observability Cloud for around two years.

Stability within Splunk Observability Cloud is reliable; I rate it a seven out of ten. I haven't experienced any significant stability issues, though it does depend on the environment's size, and multiple factors come into play when assessing stability.

From a scale of one to ten, I rate the technical support an eight, based on my experiences raising tickets for Splunk Cloud and Splunk ITSI, where I receive proper support. I haven't raised a ticket specifically for Splunk Observability Cloud, but I hope that the support levels are consistent across the board.

Positive

The deployment is easy.

The costs of Splunk Observability Cloud are a bit higher compared to AppDynamics and Dynatrace. I'm not saying it's prohibitively expensive for Splunk Observability Cloud, however, it is on the pricier side.

It's noteworthy, however, that both AppDynamics and Splunk are Cisco products, and there is discussion about possibly consolidating these licenses into a single offering soon, though I'm not certain on that.

When comparing Splunk Observability Cloud with other products or solutions on the market, it tends to stand out. It has improved significantly over time. Initially, when it was acquired from SignalFx, it lacked many functionalities and was not very useful, even if the features existed. Now, after a couple of years, it has become much faster and improved in functionality.

Splunk Observability Cloud is a solid choice if you're considering APM solutions, especially as it is likely to combine with AppDynamics soon due to their shared Cisco ownership, which should enhance its market position.

I would recommend Splunk Observability Cloud to others because it allows for faster resolution and root cause analysis. In any business, flaws are common, but Splunk Observability Cloud is a tool that provides quick results. For industries such as retail, where every second counts with high traffic, Splunk is particularly beneficial, enabling effective tracing and metrics collection.

I'd rate the solution seven out of ten.

I work with Splunk Observability Cloud.

Splunk Observability Cloud is effective for detecting anomalies and preventing system outages.

There are pre-built dashboards where I can check service centers and monitor spikes in errors and traces. I can also check error logs, and everything is consolidated while providing anomaly alerts in case there is any deviation from the baseline.

The personalized dashboard helps my team. Splunk Observability Cloud has its own query language that can be used to build easy dashboards. Multiple teams can build their own, replicate them, and also have role-based access control, which is beneficial.

The application management feature helps with end-user experiences because front-end monitoring helps track user issues and any back-end issues that may be causing them. It shows how the user experience is overall and identifies any outages. Front-end monitoring is very useful.

As an integrator, I think the biggest advantage of Splunk Observability Cloud is because it is part of the Splunk ecosystem, it is good to correlate logs with application data through traces and metrics. Overall, it is an evolving product, not top class, but it is getting there.

I see many good things about the product and many advantages. Regarding the negative side, I think the licensing can be much better because it is based upon host units and there is additional licensing for the number of traces that I can bring in. A simplified licensing model would be much better, similar to what other tools offer. Pricing could be either based upon ingestion or directly based upon host units, rather than multiple different trackers. There are licenses for custom metrics, licenses for the number of traces that I can ingest, and host unit licensing. A better licensing plan would be beneficial.

I have been using Splunk Observability Cloud for more than two years.

I have not seen any issues with stability. The solution is very stable.

Regarding scalability, I do not think there is an issue with scaling. I have never encountered any issues with that.

Support is good.

I have worked on Coralogix, which is also an observability tool. I worked in the product company itself. I have also worked on Dynatrace, and now I am working on Cribl.

The installation and deployment process is somewhat challenging, but there are multiple ways of deployment that give me a lot of options. I would say it is acceptable and not that complicated. I can deploy agents with Splunk deployment server, which is beneficial. However, there is some dependency on the deployment server.

As an integrator, I deployed it and made it workable with OpenTelemetry.

I am able to observe significant ROI with Splunk Observability Cloud. When I worked with a previous solution, it was one-third of the cost of Dynatrace, so there was definitely an exceptional return on investment. It helped reduce costs by almost 50%.

Splunk Observability Cloud is affordable. I have visited the PeerSpot website and downloaded reports on Azure, Grafana, and Splunk Observability Cloud.

When I compare Splunk Observability Cloud to other vendors, the good part is the branding because the support is good. There is a large community where I can look for known issues. However, experience-wise, DataDog is far more superior and easier to use. DataDog has its own agent for tracing, so I just deploy one trace. With Splunk Observability Cloud, they are dependent upon OpenTelemetry, and there is a learning curve because it is open source. The onboarding is not as smooth as DataDog or Dynatrace.

I deploy both on-cloud and on-premise options for clients. I have deployed Splunk Observability Cloud on Splunk Cloud. I have not used threat detection because there is a separate tool for it. I have not deployed a solution on AWS Cloud or purchased it from AWS Marketplace in my career. I would rate this review 7.5 out of 10.

Our primary use case for Splunk Observability Cloud is to monitor our infrastructure and applications, and it helps us troubleshoot issues related to any failures.

The feature we appreciate most about Splunk Observability Cloud is their distributed tracing. We also value the ability to create real-time dashboards and their alerting system is exceptional. The main best feature of that observability is their distributed tracing.

We are very satisfied with the out-of-the-box dashboards and detectors in Splunk Observability Cloud. In distributed tracing, we have banks as our clients, so if anything goes wrong with transactions, we directly go to the trace and troubleshoot those issues faster.

The AI-powered analytics and guidance in Splunk Observability Cloud is very useful. You can observe your LLM models and monitor the usage of your APIs in that cloud.

Splunk helps improve our operational performance and resilience significantly. Before we used Splunk Observability Cloud, if any failures occurred, we had to go to servers and check all the log files to find the failure. Now in Splunk, we go to that single dashboard and filter with the timestamp of failure to directly find the log, allowing us to troubleshoot issues faster. In terms of optimization, before using Splunk, we could not measure why our API was taking 100 ms, but now through distributed tracing, we can see where the bottleneck of that API is. If that bottleneck is the database, we optimize our database queries, and our application is now optimized.

Splunk Observability Cloud has reduced our mean time to detect by approximately 25 to 30 percent because it offers real-time monitoring and intelligent alerting, allowing us to troubleshoot issues faster and enhancing detection by approximately 30 to 40 percent.

In terms of pricing, I have one issue with Splunk Observability Cloud. In a large-scale organization, it does not have features such as cost optimization or budgeting for observability spend. I think they need to improve that so that I can optimize our observability. For instance, if our thousands of server applications are running, I should be able to set a budget, such as only spending $100 per month for a specific environment. They need to introduce that feature because it is very important for budgeting.

In terms of areas for improvement in Splunk Observability Cloud, the first is cost budgeting. The second is that they have many integrations, but if you are new to Splunk or new to observability, you must dive deep into more concepts. They can improve user-friendly features so that new users can set up their observability in their environment more smoothly. I think they need to improve in that integration part so that end users can onboard their infrastructure or applications very effectively.

I would appreciate more simplicity in the platform.

I have been using Splunk Observability Cloud for the past eight or nine months.

I rate the stability of Splunk Observability Cloud as ten out of ten because it is very stable, especially since we are using their cloud environment, and Splunk Observability Cloud is built for cloud-native systems.

We have not explored enriching data with custom metrics in Splunk Observability Cloud because their ready-to-use dashboards are well designed, and every organization can benefit from them. However, if you have a very large organization with over ten thousand servers running applications, you may need to build a team to create custom metrics for your specific use case.

I would rate their technical support in Splunk Observability Cloud a nine.

I have used other vendors such as Elastic Stack and Grafana Stack, but in Splunk Observability Cloud, there are so many integrations and useful features that no other vendor can offer. In Grafana, the logs and tracing features are almost nonexistent. You can use Grafana only for monitoring your infrastructure, but Splunk provides end-to-end visibility with infrastructure monitoring, tracing, and overall observability of our application.

Deploying Splunk Observability Cloud is an intermediate task for new users, but if you have been in this space for one or two years or longer, then it is easy to deploy their products.

It can take up to one week to deploy Splunk Observability Cloud.

We are not using the NoSample tracing feature in Splunk Observability Cloud.

In our organization, we have approximately 25 to 30 users using the solution daily.

We do not require any maintenance for Splunk Observability Cloud since we are using their cloud solution, which means that all patching and updates are done by them.

I recommend Splunk Observability Cloud to other organizations because we are currently saving our engineers time by 20 to 30 percent, and for infrastructure alerting, we can use it to ensure that servers will not go down. Every organization should use this because it will reduce your engineering team's effort and the downtime of your application, and in terms of any failure or APIs, you can troubleshoot your issues faster.

End-to-end visibility into our cloud-native environment is very important. If an organization is building a SaaS or B2B software, then end-to-end visibility is crucial in terms of security, failures, and compliance. The end-to-end visibility of our infrastructure and applications is extremely important.

I recommend Splunk Observability Cloud to every user because they offer trials. If you do not just read the reviews, you should try it out. Understanding the biggest features and why others are using it can be beneficial, and I always recommend Splunk Observability Cloud for end-to-end visibility in your application.

I gave this review an overall rating of ten out of ten.

My primary use cases for Splunk Observability Cloud include creating dashboards for metrics, detecting incidents, and ensuring overall observability of applications, service connections, and integrations, along with reporting and Slack integrations.

By visualizing the integration of the service, I can understand the flow of the data, which is one of the features I appreciate most about Splunk Observability Cloud.

With the metrics collection, I can proactively find incidents and work on the major issues when they happen and predict these issues.

With alerting and the detectors, we can inform the engineers that are on call to take over the service responsibility.

With the metrics and the dashboards, we can have a clear view of how the system is performing. Splunk Observability Cloud has helped improve my operational performance by detecting, analyzing tracings, and detecting alerts.

50% of our metrics on Splunk Observability Cloud are custom metrics, so we heavily rely on that. The out-of-the-box customizable dashboards provided by Splunk Observability Cloud are excellent, especially with the Amazon ones, AWS, memory cache, and Kubernetes dashboards, which are complete for the Kubernetes needs.

The UI of Splunk Observability Cloud is one of the major issues; it's old and has been there for more than 10 years, acquired by other applications from other companies. It's time to reinvent how the UI is going to work with the AI modules and integrations, making it softer and cleaner.

Splunk Observability Cloud is comprehensive in terms of functionality and features, so educating users has to be more functional. Users need to know how to be educated about certain views or pages they're working on.

I have been using Splunk Observability Cloud for five years.

I assess the stability and reliability of Splunk Observability Cloud as built on top of reliability because of the Cisco networking and infrastructure. That's not a concern for me; I totally rely on it. I've experienced downtime, crashes, and performance issues with Splunk Observability Cloud, as with any other solution. Comparing it with other monitoring solutions, Splunk has been excellent with availability. When I experienced issues, they were communicated through maintenance windows, resulting in 100% satisfaction with how they conduct this.

Splunk Observability Cloud scales very well with the growing needs of my organization. We didn't have scaling issues as the application evolved. I expanded usage of Splunk Observability Cloud when the company opened new coverage areas in different countries. Adding those metrics or new indexes to Splunk wasn't much of an issue in scaling.

I evaluate customer service and technical support for Splunk Observability Cloud as having only great experiences working with people at Splunk.

Positive

Prior to adopting Splunk Observability Cloud, I was using Datadog, which would accomplish 70% of what Splunk does currently.

There have been so many challenges that I can't name one right now. There is always a challenge in deploying open source material, like the open telemetry modules, that don't have the reliance on Splunk. It's just an integration challenge that we have the most. Deploying Splunk itself wasn't that much of a big deal.

I see ROI with Splunk Observability Cloud. My company is heavily dedicated to analytics, so the Splunk deal is significant. I cannot imagine how the business would run without it currently.

I had low pricing and setup costs for Splunk Observability Cloud, and overall, my company has received a good deal on all the features that we have. We just have to understand how to explore it further.

Not directly because of Splunk, but the visualization that I have with the main aspects of scaling made us create custom dashboards that proactively detect the changes in scale, and then we can get ready for those changes. We don't have to spend time testing the new capacity when it's already being defined and envisioned by Splunk.

My advice to other organizations considering Splunk Observability Cloud is to watch out for your budget. If I could assess the impact of not having Splunk Observability Cloud, there would be a monetary impact with other solutions. For the business, we would lose resiliency of the system. To imagine the impact, it would be catastrophic.

Splunk has to think about how to redesign Observability Cloud. It came from SignalFx and AppDynamics to Splunk Cloud. It's a merge of different platforms into one, and this merge is being done at a pace where I expected more velocity.

On a scale of one to ten, I rate Splunk Observability Cloud overall as a seven.

Splunk Observability Cloud is primarily used for application latency measurement, CPU usage monitoring, and memory usage tracking. It essentially functions as a system monitoring solution.

Splunk Observability Cloud is being used to monitor and optimize AI applications. Every AI model is on one server using Ollama, and Splunk Observability Cloud is deployed to track which AI model is being used more, which is not, at what time, and which prompt has been heated. This integration allows for detailed monitoring and optimization of AI workload distribution.

Previously, issues such as identifying which instance was using more CPU or which was using more GPU were solved manually. With Splunk Observability Cloud, AI assists in these tasks automatically, allowing the team to focus on bigger issues.

The engineering team primarily deals with alerts that come in. The team tries to solve these issues using AI and other tools provided by the platform.

What I like about Splunk Observability Cloud are mainly the real-time dashboards. I am getting real-time usage of my CPUs and everything. It also provides end-to-end visibility with the system. I am getting to know what application is using which CPU, everything. Metrics have been set in our system, so I can get very end-to-end visibility into it.

Out of the box, the solution's dashboards and detectors were very helpful. During our last sale on Good Friday, one of my CPUs was being used with very high latency. We got very high alerts from it because we had set that up. Because of it, we are very grateful. We solved that issue in a very short amount of time, and the sale went live. It is basically used to reduce manual work. Earlier, we were not using this platform, so we had to find which CPU usage had increased and which had not. It was a very manual and messy process earlier.

My impression of the No-Sample Tracing feature in Splunk Observability Cloud is that we are collecting from Cribl and we are getting data from it. We have set every metric on the cloud and in Splunk, and it helps to showcase everything in real time.

The AI-powered analytics and guidance provided by Splunk is very good. The AI part is what I was expecting earlier because it was very messy. There is not a lot of information about Splunk in the market, so we required an AI for this. AI-powered analytics help identify any anomalous activities or any spark on any platform and solve issues automatically, resulting in good management of high latency and resource storage.

End-to-end visibility into my cloud-native environments is very useful. Earlier, I had to solve these things manually. I had to check every instance, I had to check every monitoring system, which has been blasted and which has been corrupted. With this solution, I am able to manage it effectively.

I would like to see a very detailed tutorial about it and how any newcomer can be able to use it. If there is any tutorial or other resources available, it could be better to use it.

I do not have any missing features that I would like to see included or enhanced in it. We have not faced any technical issues right now.

I have been working with Splunk Observability Cloud for more than a year, but I have been using it for the last six to eight weeks.

Splunk Observability Cloud is very stable. We have been using it for the last six months and it is very stable.

I do not face any downtime because we do not experience that type of issue.

I would evaluate customer service and technical support as a nine. It is very good.

It is a nine out of ten.

The experience with the deployment is easy.

I purchased Splunk directly through Splunk, not any third party.

I have done it through myself.

I find the experience with the pricing aspect, setup cost, and licensing part to be very less. However, the pricing is not coming under my purview, so I cannot be sure about it.

We are developing AI applications, so every AI model of ours is on one server using Ollama. We have integrated everything into it and we are using it as an API. We have deployed Splunk Observability Cloud and everything. This allows us to know which AI model is being used more, which is not, at what time, and which prompt has been heated. It is very good with the AI models.

The main benefits that Splunk Observability Cloud brings to the table are mainly to reduce the time and to reduce any manual work.

Splunk Observability Cloud has helped to improve my operational performance because it is useful for my company. It helps to solve issues, reduce the manpower, and reduce the man-time to solve it. It is very useful.

Mean time to detect has worked very well with our small application. We have our small application in the cloud, in an AWS instance, and we have connected everything with this. It works very well with that as well. It is working very well even in a very small application.

Previously, we had to solve problems manually, figuring out which instance was using more CPU or which was using more GPU. But with the help of this AI, we have come to know that we do not have to worry about the small things. The AI is solving those things on its own. We just have to focus on the bigger issues and the bigger picture. It is very good.

Overall, I assess Splunk Observability Cloud for helping my organization scale as very helpful because it is very useful and very impactful in my organization.

My impressions of Splunk Observability Cloud for helping my organization focus on its business-critical initiatives are positive. It is very useful because we can have more focus on what the issue is rather than finding what the issue is.

If you are using servers or any cloud, I highly recommend Splunk Observability Cloud. Because of it, we are able to find any issues in the server directly. I highly recommend Splunk Observability Cloud to any organization if they are using servers.

I assess this product with an overall rating of nine out of ten.