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.

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.

In my organization, we have 150 to 160 applications yearly with different frameworks including .NET, Java, and Python based applications. All of them are hosted on different types of servers such as Windows, Linux, ECS, and EKS. With respect to deployments, we integrated Splunk Observability Cloud. Previously, we used Prometheus and Grafana. My organization considered Splunk Observability Cloud to be a premium side of observability, so they switched from our previous solution.

We use the tracing feature in Splunk Observability Cloud.

I appreciate the service map and APM in Splunk Observability Cloud the most. This is the main feature I value. The interface is completely UI based, so I can see the complete service map, observe the latency present, and view complete metadata for a particular service or any database-related service. The service map enables a 3D view of the complete application architecture.

With respect to the effectiveness of Splunk Observability Cloud in improving digital resilience within the organization, it was quite similar to other third-party tools. The main distinction is that it has some improved security. We use SignalFlow queries, and with respect to those queries, we work with alerts and the dashboarding part. I can say it provides efficiency with improved security compared to other third-party tools, but in terms of usage, it is quite similar to Prometheus and Grafana.

I want to address a disadvantage regarding the service map showing misinformation with respect to latency, which relates to data reliability pulled from AWS cloud or on-premise servers. We saw issues with latency because Splunk APM app shows different data than Prometheus and Grafana. We tried to get premium support and on-call support with Splunk, and they were helpful in troubleshooting, but they ended up with no solution.

Performance with Splunk Observability Cloud is acceptable to me, but the modifications required by users are problematic. I had to build the complete alerting system and monitoring system, which had to be changed. The way they designed this is not optimal. If I compare with Prometheus, we can import and export dashboards, but here we face errors with dialogue boxes. We tried with technical support calls about this, but they were unable to solve it, so I do not understand why export and imports are not functioning.

The overall impression of the no-sample tracing feature in Splunk Observability Cloud, specifically in terms of eliminating blind spots in data collection, is that it needs improvement because the data is not adequate compared to other third parties. We get disturbance in the dashboards and charts while trying to correlate data. The mechanism functions differently manually than it does with a SignalFlow query, and both should be equal. We are unable to replicate from manual processes to the automation method, which is the issue.

The SignalFlow query feature in Splunk Observability Cloud needs improvement because it should function the same as manual processes. When we configure manual queries and then configure them via SignalFlow, they give different outputs. We tried with on-call support about this, but they were unable to address it, indicating there is a bug with the queries that needs improvement.

For enhancements, I would like to see improvements in the OTEL agents, OTEL collectors, and other features in Splunk Observability Cloud. The guidelines in the official documentation are not working at all. We have to deploy processes in our own way, and the documentation works only in 60 percent of the conditions, leaving the remaining 40 percent as problematic and needing improvement.

I have used Splunk Observability Cloud for nearly one to one and a half years.

I experienced a downtime with Splunk Observability Cloud one time. We were unable to access it for nearly one day, which took a lot of time to resolve. Normally, other tools do not take as much time, and I do not understand why Splunk took so long. From the vendor's end, they should address such issues in a much shorter timeframe. When downtime occurs, it raises concerns about how we measure and receive alerts, as everything needs to be in place.

In terms of lowering the cost of unplanned digital downtime using Splunk Observability Cloud, I found that many users report it is expensive, especially at a large scale, which can be a concern for organizations with tight budgets. At a large scale it is good, but for start-ups and some medium-range companies, it is expensive and they cannot afford it, especially as the cost increases with respect to data volume and retention needs.

Support wise, there are two kinds of support for Splunk Observability Cloud: bi-weekly support and on-call support, with one more being premium support. They need to decrease the price of premium on-call support because as an employee, we require credits to get premium support, and our organization does not have many credits. That is a point where it lagged, but with respect to the bi-weekly calls and on-call support, it was acceptable. Out of five, I can give three for normal support, and four for premium call support.

Neutral

Previously, we used Prometheus and Grafana.

In comparing Splunk Observability Cloud to other observability platforms I have worked with, I find no key differences in both pros and cons. The integration process is the same across the board, and I feel there is not a real differentiator, as everything is similar in terms of custom dashboards and APM features.

We miss the synthetic monitoring and AI-related features in Splunk Observability Cloud, which I think means front-end monitoring. We touch only the main AWS monitoring and service map, APM, and that is what we are using.

Regarding the ability to enrich data with custom metrics in Splunk Observability Cloud, we configured our breaches based on application performance only. Every application has different SLAs and SLOs, and according to each application, we have configured alerts using baselines that got triggered. We correlate this with multiple factors, such as Java-based memory leaks or garbage collections, and we generate custom metrics with alerts for notification purposes, employing the Webhook URL of Microsoft Teams and Outlook.

The out-of-the-box customizable dashboards provided by Splunk Observability Cloud are effective in showcasing IT performance to business leaders. It offers a nice point, as when we correlate different charts, I get so many x-axis and y-axis options, and we can correlate with other metrics. We have formulas there to find ratios and averages, which was a nice experience offering so many options. We are using the f(x) functions with respect to maximum, minimum, and averages, which are quite good.

On a scale of one to ten where ten is the best, I would rate Splunk Observability Cloud differently. For the UI part, I would rate it an eight, but for the configuration part, I would rate it three to four, as the configuration and integration aspects are not good at all. Overall, I would rate Splunk Observability Cloud a three 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.

The use cases for Splunk Real User Monitoring (RUM) are almost the same as Dynatrace, for people who want to do infrastructure monitoring, application monitoring, or check their front-end monitoring, such as monitoring for the real user. They are actually using Splunk Real User Monitoring (RUM).

The best advantages and features in the product are notable. Splunk Real User Monitoring (RUM) seemed to have advantages in 2015, but when Cisco acquired AppDynamics, they stopped upgrading for a few years. Their advantage now is that they are the only solution that can monitor above SAP. Apart from that, Datadog and Dynatrace have superior features. Splunk Real User Monitoring (RUM) has one great advantage in that if they have SAP users, they can monitor their SAP applications.

In terms of features, UI, or the ability to do monitoring of RUM, the picture by UI or the ability to actually do the monitoring is pretty good, but not as good as Dynatrace, Catchpoint, Datadog, and New Relic.

My thoughts on room for improvement in general relate to Cisco trying to mount the Cisco cloud solution when they have AppDynamics, but after acquiring Splunk, they changed their roadmap. It's inevitable that when a big company has resources but changes the roadmap, it takes time to establish well-structured manuals and guidelines. I believe they will get better, but right now, they need some improvement.

I have been selling Splunk Real User Monitoring (RUM) for almost the same duration as NetScout; it's been 3 years.

In terms of deployment, Dynatrace is the easiest by far. For Datadog and Splunk Real User Monitoring (RUM), you have to work with scripts, meaning you need an engineer for deployment; Dynatrace is straightforward and really easy.

Regarding stability of RUM, stability is also a problem because during the POC, we ask customers not to deploy RUM or EUM scripts in the actual stage. We encourage them to test in staging because even if those solutions are certified and tested, customers have many different environments. Unlike NetScout or regular agents for APM, RUM has many problems during the POC phase because customer environments vary widely.

In terms of scalability, the solution is scalable enough, but sometimes implementation is hard. However, it doesn't take more than a month. The issue is mainly about pricing because if they want to monitor more, it costs money. Those who have a great pricing plan or volume table will gain an advantage.

Support from Splunk is not very helpful because Splunk doesn't have a dedicated APM; they only have one APM engineer in Korea.

If I were to rate technical support for Splunk Real User Monitoring (RUM) from 1 to 10 points, I would give it a score of 5.5. I appreciate the engineer, but since there's only one person doing everything, it's not easy. Cisco doesn't hire enough personnel, and I heard they don't have enough staff in APAC. Datadog and Dynatrace, especially Datadog, cover a huge market share in Asia, and that's why other competitors are not investing enough at this time.

Neutral

My clients, such as Hyundai and POSCO, are using RUM; most of them use Dynatrace. For those using Splunk Real User Monitoring (RUM), it took time to implement because our engineer tried really hard.

Regarding whether the solution provides ROI or savings, I understand that even Dynatrace, Datadog, or Splunk has a low price per transaction, but it concerns the number of data and sessions. People struggle to comprehend the total budget, which might be massive. Customers need to understand this, and I'm not sure if it's possible; maybe some companies just decided to take the entire market and cut down prices, but anyone working in front-end management should recognize the market price to see the true value of end-user monitoring.

I am the general manager of this company, and a team leader focusing on Dynatrace, Cisco AppDynamics, and NetScout, while we used to do some other APM solutions but not extensively. We only focus on those three solutions.

We try to sell Splunk User Behavior Analytics, but we haven't been able to sell it so far because Splunk used to be a great company in Korea. After Cisco acquired it, it's complicated because when two companies merge together, they have issues with who will be the product seller, the sales personnel or the engineer. Their organization is not fully merged yet, which leads to fewer marketing and sales activities.

The integration in real user monitoring from Splunk is actually another problem because of some vacancies when they merged the companies and changed the organizations; there are issues with the manual not being complete. When you work with integration, Datadog and Dynatrace have issues but have to work with the manual; AppDynamics has many people and support. The satisfaction level is around six compared to Dynatrace, which might be a seven or eight. The innovation level is not complete, but they are working on it.

When using real user monitoring to analyze performance bottlenecks, the most critical metrics include data from Catchpoint, which has extensive data to gather, and Dynatrace also gathers substantial data. However, sometimes it's unnecessary because customers in charge of front-end monitoring should see the problems immediately and act with reporting to their superior. A simple UI is crucial; that's perhaps why Datadog has the advantage with their comprehensive UI.

Regarding pricing for RUM, it becomes another problem because Splunk and Dynatrace and Datadog don't ask customers to use RUM for every single session. Many large customers want to monitor every session, resulting in a big gap. For example, Korean Air, which is among the top five air travel companies in the world, uses Datadog but changed to Dynatrace because, even with a limited budget, Dynatrace managed to persuade them to monitor just 10% of user sessions to reduce the budget, and they are satisfied with the results.

This review rates Splunk Real User Monitoring (RUM) 5 out of 10.

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.