Splunk Observability Cloud Reviews

The main purpose of using Splunk APM is to optimize our application. We use Splunk APM primarily to understand how the application works, how it uses resources, and its response time in connection with different infra services. It is mainly used for application optimization and reviewing third-party application dependency response times.

Splunk APM helps us to identify long-running queries and long-running functions or methods, as well as third-party dependencies that are not responding on time. We are easily able to see the error or trace it. A developer can easily find out the issue without having to dig into the application.

We normally do not use the Tag Spotlight functionality, but our developers use this functionality when we are trying to dig into the logs. It helps to search the data that we want to see. It helps to troubleshoot the actual problem and visualize the data. We can see how the error is coming and how many reports are coming.

Splunk APM has helped us to optimize the application performance, find out when third-party services go down, and monitor our application within our SLA. It allows us to minimize our downtime. We can send timely notifications to our users. It mainly helps us to optimize application performance, and secondly, we are able to generate alerts based on the data that we receive from Splunk.

Splunk APM helps us to find errors immediately and resolve them. We are able to find some of the errors within five minutes. It minimizes the time to identify errors. There are about 30% to 40% time savings.

The best feature is the service map that they have. I have used multiple APM solutions such as Datadog and Elastic. They have a service map, but it does not work like Splunk APM. Splunk APM provides a holistic view of the application. Unlike other APMs, Splunk's service map is quite effective.

We suggested they provide an alert based on insert services. We told them that they have all the data, so why not have an alert on the insert service? They took feedback from us and added that feature. That feature helps us identify if any third-party dependent is down.

There is room for improvement in the alerting system, which is complicated and has less documentation available. We sometimes encountered issues in setting up alerts. The custom detector could be more simplified to assist system engineers in setting up alerts with ease.

We tested Splunk APM last year and officially started using it this year. It has been about a year.

Splunk APM is stable. I would rate its stability a nine out of ten, as it delivers on its promises.

We have not had to scale it. Our clients are medium enterprises.

The support is responsive, though it could use some improvement. In the past, we contacted their support about a feature. They did respond to us, but they did not explicitly inform us about the feature's absence. Instead, they directed us to try various resources or articles. They did not have a clear answer. I would rate them a five out of ten for customer service.

Neutral

Before using Splunk APM, I used Elastic APM and Datadog. Splunk APM is better than them. Splunk's service map and support for our existing libraries were significant reasons for the switch. The previous vendor required library updates that we could not accommodate, but Splunk supported our existing setups.

The initial setup of Splunk APM was easy and straightforward. It took around a week.

It appears to be expensive compared to competitors.

Splunk APM is suitable for enterprise solutions, particularly for those deeply involved in technical business. The service map and overall stability make it a robust choice for such needs.

I would rate Splunk APM a nine out of ten.

We use the solution to do a lot of email checking. We also use the tool to monitor different embassies, server IPs and some of the teams.

Splunk Infrastructure Monitoring has helped our organization tremendously. We have onboarded Splunk for the last four years, and we have 30 to 40 contractors who use Splunk daily. The solution has helped not just a small organization like ours but the whole DOS (Department of State).

The solution monitors attacks or unauthorized access to the information we want to protect. There is a dashboard called ISSO that monitors pretty much everything worldwide. We also monitor almost 300 embassies and consulates.

The solution's machine learning deployment is hard and should be made user-friendly. Even if a team doesn't have a data scientist, they should be able to use the machine learning toolkit for monitoring purposes. The solution should include more algorithms and SPL commands that people can use.

I have been using Splunk Infrastructure Monitoring for four months.

We haven’t faced any issues with the solution’s stability.

Splunk Infrastructure Monitoring is highly scalable. We were able to do monitoring and some of the advanced analytics.

I have not contacted Splunk's technical support. We have contacted our account manager for issues, and she's been awesome.

We have different vendors who do deployments, which is different for the government than regular businesses.

We have seen a return on investment with Splunk Infrastructure Monitoring regarding the kind of threats we can identify.

Splunk Infrastructure Monitoring is an expensive solution.

Our organization monitors multiple cloud environments using Splunk Infrastructure Monitoring, which works well. This is the only tool we use, and we aren't considering moving or having additional tools.

It is important for our organization that Splunk Infrastructure Monitoring has end-to-end visibility into our cloud-native environments. Our job is critical and very sensitive, so having end-to-end visibility is really helpful.

Splunk Infrastructure Monitoring has helped reduce our mean time to resolve. Looking at the solution's dashboards has helped tremendously because we don't have to look at the individual index or events.

Our business is different from that of a private organization, and Splunk Infrastructure Monitoring has helped improve our organization's business resilience. The machine learning toolkit allows us to do clustering, and we have a couple of deployments on the clusters. That has helped cluster different events based on their critical or security threats.

We have seen time to value using Splunk Infrastructure Monitoring.

Splunk's unified platform has helped consolidate networking, security, and IT observability tools. We don't have to integrate Splunk with a different tool and worry whether those two will integrate. Having everything in one platform helps us create dashboards, alerts, and monitoring tools in one place.

Overall, I rate the solution an eight or nine out of ten.

We utilize Splunk APM for security purposes, monitoring all transactions within the organization to prevent potential attacks. Additionally, we leverage Splunk APM to analyze application logs, gaining insights into application behaviour and facilitating a reduction in Mean Time To Resolution should any issues arise in the production environment.

OpenTelemetry provides more accurate information about an application by combining views from the customer perspective, infrastructure metrics, and application-specific data. This holistic view enables full telemetry observability, allowing us to analyze and strategize effectively for our company or clients.

Once configured correctly, the analysis reporting the Splunk APM provides is better than that of the other APM tools. Once the correct fields are defined, we can create different report dashboards.

Splunk isn't an ideal tool for application performance management due to the extensive setup required. It necessitates various configurations to gather diverse information from applications, networks, or other sources. Creating the right tables and defining the appropriate fields to extract comprehensive data involves a significant amount of setup within the tool. Managing this process can be quite challenging. However, once configured, the collected information is invaluable, although not easily manageable.

Splunk falls short compared to other APM tools such as AppDynamics or Datadog. It does not collect online information in real time and relies heavily on log files. Unlike Datadog, which collects real-time application behaviour data like CPU, memory, load, and response time, Splunk requires additional configuration to obtain similar information. This makes using Splunk for APM purposes significantly more difficult compared to the automatic data collection capabilities of AppDynamics or Datadog.

I have been using Splunk APM for more than a decade.

Splunk APM lacks scalability, requiring the administrator to constantly monitor or create specific alerts to ensure sufficient disk space, CPU, and memory for data collection and transaction processing. This results in a tool that is challenging to manage and costly to maintain.

Splunk support is responsive and provides quick resolutions when tickets are opened. Their service has left a positive impression on me.

Positive

The initial deployment is complex, requiring the definition of the switch, storage, correct host, and working with certification. This necessitates at least one expensive specialist, costing approximately $5,000 per month to hire and work with our team.

Splunk APM is expensive. Even before we begin, we need substantial infrastructure investment to collect comprehensive logs. For example, to gather log data, we must create specific tables in Splunk, starting at 50 gigabytes. In a cloud environment, this storage requirement becomes very costly.

I would rate Splunk APM six out of ten.

Cisco recently acquired Splunk, and its roadmap for the coming year includes incorporating aspects of Splunk into AppDynamics. Cisco's intention behind combining these two tools is to showcase its commitment to open telemetry and comprehensive observability to the market and its customers.

We use Splunk APM to understand and know the inner workings of our cloud-based and on-premises applications. We use the solution mainly for troubleshooting purposes and to understand where the bottlenecks and limits are. It's not used for monitoring purposes or sending an alert when the number of calls goes above or below some threshold.

The solution is used more for understanding and knowing where your bottlenecks are. So, it's used more for observability rather than for pure monitoring.

The solution's service map feature allows us to have a holistic overview and to see quickly where the issues are. It also allows us to look at every session without considering the sampling policy and see if a transaction contains any errors. It's also been used when we instrument real use amounts from the front end and then follow the sessions back into the back-end systems.

Splunk APM should include a better correlation between resources and infrastructure monitoring. The solution should define better service level indicators and service level objectives. The solution should also define workloads where you can say an environment is divided up by this area of back end and this area of integration. The solution should define workloads more to be able to see what is the service impact of a problem.

I've been using Splunk APM in my current organization for the last 2 years, and I've used it for 4-5 years in total.

Splunk APM is a remarkably stable solution. We have only once encountered an outage of the ingestion, which was very nicely explained and taken care of by the Splunk team.

I rate the solution a 9 out of 10 for stability.

Around 50 to 80 users use the solution in our organization. The solution's scalability fits what we are paying for. On the level of what we pay for, we have discovered both the soft limit and the hard limit of our environment. I would say we are abusing the system in terms of how scalable it is. Considering what we are paying for, we are able to use the landscape very well.

We have plans to increase the usage of Splunk APM.

Splunk support itself leaves room for improvement. We have excellent support from the sales team, the sales engineers, the sales contact person, and our customer success manager. They are our contact when we need to escalate any support tickets. Since Splunk support is bound not to touch the consumer's environment, they cannot fix issues for us. It's pretty straightforward to place a support ticket.

Positive

We have previously used AppDynamics, Dynatrace, and New Relic. We see more and more that Splunk APM is the platform for collaboration. New Relic is more isolated, and each account or team has its own part of New Relic. It's very easy to correlate and find the data within an account. Collaborating across teams, their data, and their different accounts is very troublesome.

With Splunk APM, there is no sensitivity in the data. We can share the data and find a way to agree on how to collaborate. If two environments are named differently, we can still work together without infecting each other's operations.

If you're using the more common languages, the initial deployment of Splunk APM is pretty straightforward.

The solution's deployment time depends on the environment. If the team uses the cloud-native techniques of TerraForm and Ansible, it's pretty straightforward. The normal engagement is within a couple of weeks. When you assess the tool they need and look at the architecture and so on, the deployment time is very, very minimal. Most of the time spent internally is caused by our own overhead.

We have a very good conversation with our vendor for Splunk APM. We have full transparency regarding the different license and cost models. We have found a way to handle both the normal average load and the high peak that some of our tests can cause. Splunk APM is a very cost-efficient solution. We have also changed the license model from a host-based license model to a more granular way to measure it, such as the number of metric time series or the traces analyzed per minute.

We have quite a firm statement that for every cost caused within Splunk, you need to be able to correlate it to an IT project or a team to see who the biggest cost driver is. As per our current model, we are buying a capacity, and we eventually want to have a pay-as-you-go model. We cannot use that currently because we have renewed our license for only one year.

We are using Splunk Observability Cloud as a SaaS solution, but we have implemented Splunk APM on-premises, hybrid, and in the cloud. We are using it for Azure, AWS, and Google. Initially, the solution's implementation took a couple of months. Now, we are engaging more and more internal consumers on a weekly basis.

We implement the code and services and send the data into the Splunk Observability Cloud. This helps us understand who is talking to whom, where you have any latencies, and where you have the most error types of transactions between the services.

Most of the time, we do verification tests in production to see if we can scale up the number of transactions to a system and handle the number of transactions a business wants us to handle at a certain service level. It's both for verification and to understand where the slowness occurs and how it is replicated throughout the different services.

We can have full fidelity and totality of the information in the tool, and we don't need to think about the big variations of values. We can assess and see all the data. Without the solution's trace search and analytics feature, you will be completely blind. It's critical as it is about visibility and understanding your service.

Splunk APM offers end-to-end visibility across our environment because we use it to coexist with both synthetic monitoring and real user monitoring. What we miss today is the correlation to logs. We can connect to Splunk Cloud, but we are missing the role-based access control to the logs so that each user can see their related logs.

Visualizing and troubleshooting our cloud-native environment with Splunk APM is easy. A lot of out-of-the-box knowledge is available that is preset for looking at certain standard data sets. That's not only for APM but also for the available pre-built dashboards.

We are able to use distributed tracing with Splunk APM, and it is for the totality of our landscape. A lot of different teams can coexist and work with the same type of data and easily correlate with other systems' data. So, it's a platform for us to collaborate and explore together.

We use Splunk APM Trace Analyzer to better understand where the errors originate and the root cause of the errors. We use it to understand whether we are looking at the symptom or the real root cause. We identify which services have the problem and understand what is caused by code errors.

The Splunk Observability Cloud as a platform has improved over time. It allows us to use profiling together with Splunk Distribution of OpenTelemetry Collector, which provides a lot of insights into our applications and metadata. The tool is now a part of our natural workbench of different tools, and it's being used within the organization as part of the process. It is the tool that we use to troubleshoot and understand.

Our organization's telemetry data is interesting, not only from an IT operational perspective but also to understand how the tools are being used and how they have been providing value for the business. It is a multifaceted view of the data we have, and it is being generated and collected by the solution.

Splunk APM has helped reduce our mean time to resolve. Something that used to take 2-3 weeks to troubleshoot is now done within hours. Splunk APM has freed up some resources if we are going to troubleshoot. If you spend a lot of time troubleshooting something and can't find a problem, we cannot close the ticket saying there's no resolution. With Splunk APM, we can now know for sure where we have the problem rather than just ignoring it.

Splunk APM has saved our organization around 25% to 30% time. It's a little bit about moving away from firefighting to be preventive and estimate more for the future. That's why we are using it for performance. The solution allows us to help and support the organization during peak hours and be preventative with the bottlenecks rather than identify them afterward.

Around 5-10 people were involved in the solution's initial deployment. Integrating the solution with our existing DevOps tools is not part of the developer's IDE environment, and it's not tightly connected. We have both subdomains and teams structured. Normally, they also compartmentalize the environment, and we use the solution in different environments.

Splunk APM requires some life cycle management, which is natural. In general, once you have set it up, you don't need to put much effort into it. I would recommend Splunk APM to other users. That is mainly due to how you collaborate with the data and do not isolate it. There is a huge advantage with Splunk. We are currently using Splunk, Sentry, and New Relic, and part of our tool strategy is to move to Splunk.

As a consumer, you need to consider whether you are going to rely on OpenTelemetry as part of your standard observability framework. If that is the case, you should go for Splunk because Splunk is built on OpenTelemetry principles.

Compared to other tools using proprietary agents and proprietary techniques, you may have more insights into some implementations. However, you will have a tighter vendor lock-in, and you won't have the portability of the back end. If you rely on OpenTelemetry, then Splunk is the tool for you.

Overall, I rate the solution a 9 out of 10.

We use Splunk APM to monitor the performance of our applications.

Splunk APM offers end-to-end visibility across our entire environment. We need to control how many types of metrics are ingested by Splunk APM from all incoming requests. While we allow some metrics to be collected, Splunk APM provides the ability to track each request from its starting point to its endpoint at every stage.

Splunk APM trace analyzer allows us to analyze a request by providing its trace ID. This trace ID gives us a detailed breakdown of how the request entered the system, how many services it interacted with along the way, and its overall path within the system. We can also identify any errors that occurred during the request's processing and track any slowness or latency issues. This information is very helpful for troubleshooting performance problems in our application.

Splunk APM telemetry data has been incredibly valuable. While we faced challenges with Splunk Enterprise, such as the lack of a trace analyzer, Splunk APM's user interface is modern and highly flexible. The wide range of data it provides has significantly improved our incident response times, allowing us to quickly create alerts and adhere to the infrastructure as code principle. Splunk APM also proves beneficial during load testing, contributing to a positive impact on our overall infrastructure performance analysis.

Splunk APM helps us reduce our mean time to resolution. With its fast and accurate alerting system, we can quickly identify the exact location of issues. This pinpoint accuracy streamlines the investigation process, leading to faster root-cause analysis.

Splunk APM has helped us save significant time. We're now spending less time resolving production incidents and analyzing performance data. This focus on Splunk APM allows us to dedicate more time to other areas.

Detectors are a powerful feature. They create signal flow code in a format similar to Splunk APM language. For example, if we select five conditions, the detector can automatically generate the code for that signal flow. This code can then be directly integrated into our Terraform modules, streamlining the creation of detectors using Terraform. This is particularly helpful because our infrastructure adheres to a well-defined practice, and detectors help automate this process.

APM dashboards are another valuable tool. They provide more comprehensive information than traditional spotlights. One particularly useful feature is the breakdown of a trace ID. This breakdown allows us to see the entire journey of a request, including where it originated, any slowdowns it encountered, and any issues it faced. This level of detail enables us to track down the root cause of performance problems for every request.

We currently lack log analysis capabilities in Splunk APM. Implementing this functionality would be very beneficial. With log analysis, we could eliminate our dependence on Splunk Enterprise and rely solely on APM. The user interface design of APM seems intuitive, which would likely simplify setting up log-level alerts. Currently, all log-level alerting is done through Splunk Enterprise, while infrastructure-level alerting has already transitioned to Splunk APM.

The Splunk APM documentation on the official Splunk website could benefit from additional resources. Specifically, including more examples of adapter creation and management using real-world use cases would be helpful. During our setup process, we found the documentation lacked specific implementation details. While some general information was available on public platforms like Google and YouTube, it wasn't comprehensive. This suggests that others using Splunk APM in the future might face similar challenges due to the limited information available on social media. It's important to remember that many users rely on social media for setup guidance these days.

I have been using Splunk APM for 1.5 years.

While Splunk APM occasionally experiences slowdowns, it recovers on its own. Fortunately, these haven't resulted in major incidents because most maintenance is scheduled for weekends, with ample notice provided in advance. We have never experienced any data loss that occurred during previous slowdowns.

Splunk APM customer support is helpful. They promptly acknowledge requests and provide regular updates. They've been able to fulfill all our information requests so far. However, Splunk APM is a constantly evolving product. This means there are some limitations due to ongoing industry advancements. They are actively working on incorporating customer feedback, such as the CV request. Overall, the customer support is excellent, but the desired features may not all be available yet.

Positive

Previously, we used Grafana, but we faced challenges that led us to switch to Splunk APM. Since then, Splunk has become our primary tool for data analysis. In our experience, Splunk offers several advantages over Grafana. Setting up and using Splunk is significantly easier than Grafana. Splunk provides a user-friendly interface that allows anyone to start working immediately, while Grafana's setup can be more complex. Splunk also boasts superior reliability. Its architecture utilizes a master-slave node structure, with the ability to cluster for redundancy. This ensures that if a node goes down, another available node automatically takes over, minimizing downtime. Ultimately, our decision to switch to Splunk was driven by several factors: user-friendliness, a wider range of features, cost-effectiveness, and its established reputation. Splunk is a globally recognized and widely used tool, which suggests a higher level of trust and support from the industry.

We use Splunk Enterprise and Splunk APM. Splunk APM offers a comprehensive view of various application elements. We primarily migrated to APM to gain application-level metrics. This includes latency issues, which are delays in processing user requests. Splunk APM generates a unique trace ID for each user request. This allows us to track the request from the user to our servers and identify any delays or errors that occur along the way. 
Additionally, Splunk APM utilizes detectors to create alerts based on specific metrics. We've implemented alerts for CPU and memory usage, common issues in our Kubernetes infrastructure. We can also track container restarts within the cluster and pinpoint the causes. Another crucial area for us is subscription latency. Splunk APM allows us to monitor this metric and identify any performance bottlenecks. This capability was absent in Splunk Enterprise, necessitating the switch to APM. Furthermore, Splunk APM enables us to track application status codes, such as 404 errors.

Splunk APM facilitates the creation of informative dashboards using collected metrics. Additionally, the Metrics Explorer tool allows us to investigate specific metrics of interest and generate alerts or customized spotlights. 
Spotlights are tailored visualizations that track metrics for critical application areas. They can trigger alerts based on unexpected changes, such as a sudden increase in error codes over a set timeframe. This provides a more proactive approach to identifying potential issues compared to traditional detector-based alerts.

Splunk APM empowers us to effectively monitor various metrics during load testing. This includes analyzing memory usage across ten to eleven metrics, tracking container restarts during flow testing, and verifying the functionality of auto scaling mechanisms. The comprehensive visualization capabilities of Splunk APM surpass those of Splunk Enterprise, making it ideal for analyzing large sets of metrics and graphs.

We're currently exploring the integration of an OpenTelemetry agent with Splunk APM. This will enable us to collect and transmit a wider range of data, including application metrics, latency metrics, and basic infrastructure metrics such as CPU, memory, etc.

During the initial Splunk deployment, I found that most information available on social media platforms catered to enterprise deployments. Fortunately, many of our new hires had prior Splunk experience, which eased the initial learning curve. Splunk's widespread adoption across industries also meant there was a general familiarity with the tool among the team. Additionally, the comprehensive documentation proved helpful. Overall, the initial rollout went smoothly, though there were some challenges that we were able to resolve.

The Splunk deployment was done on multiple environments. We started with development and then deployed to a staging environment, which sits between development and production. As expected, the development deployment took the longest. The total time for the entire deployment, including my cloud setup, was 2 to 3 weeks. It's important to note that this timeframe isn't solely dependent on Splunk implementation. Other factors can influence the timeline, such as network requests, firewall changes, and coordination with IT teams for license purchases. While the development deployment took longer, promoting Splunk to the staging and production environments was significantly faster. It only took 1 week for each environment.

Our cloud deployment didn't require a consultant, but we used one for our on-premise enterprise deployment, which was a bit more complex.

I would rate Splunk APM 9 out of 10.

The maintenance required is minimal because the cluster deployment helps ensure there is always 1 node working.

We use Splunk Infrastructure Monitoring to monitor our hybrid infrastructure.

We implemented Splunk Infrastructure Monitoring to help us monitor our infrastructure as we scale.

Splunk Infrastructure Monitoring is easy to use. It helps us quickly analyze how our infrastructure is performing across various services.

It helps with proper log management, allowing us to monitor our systems and analyze log data regularly. It also provides security operations capabilities for monitoring system health and ensuring uptime. We noticed these benefits immediately.

Our operational efficiency has been increased. It has improved our system health by monitoring the performance of data on servers, virtual machines, and containers, along with overall background processes.

Splunk Infrastructure Monitoring provides end-to-end visibility into our cloud-native environment. This is crucial because any data corruption can impact all the information we've deployed. It also aids in log management, offering parameters that extend its functionality as a comprehensive monitoring tool for CPU, memory usage, and network traffic.

It has helped reduce our mean time to detect by four hours.Our mean time to resolution has been reduced by two hours. By providing access to all our network parameters, it simplifies log ingestion through streamlined calculations.

Splunk Infrastructure Monitoring provides us with faster and more comprehensive insights into our infrastructure, allowing us to focus on critical business initiatives.

We saw the time to value immediately after deploying Splunk Infrastructure Monitoring.

The data collection from our VMs, containers, databases, and backend components is valuable.

Splunk Infrastructure Monitoring's data analytics can be improved by including suggestions for various types of continuous monitoring.

I have been using Splunk Infrastructure Monitoring for three years.

The network uptime and monitoring are great.

The scalability of Splunk Infrastructure Monitoring is excellent.

The technical support is good.

Positive

We previously used Datadog but it doesn't offer network monitoring features like CPU utilization or overall server performance, which Splunk Infrastructure Monitoring does, so we switched.

Splunk Infrastructure Monitoring offers more functionality and visibility, making it a better choice for handling cloud architecture compared to Datadog.

The initial setup was straightforward. One person was required for the deployment.

I would rate Splunk Infrastructure Monitoring 9 out of 10.

Splunk Infrastructure Monitoring offers automated, continuous monitoring and diagnostics, delivering real-time reports for all your data with enhanced functionality compared to other solutions.

We have 200 users of Splunk Infrastructure Monitoring.

Splunk Infrastructure Monitoring is the best solution for monitoring networks, parameters, CPU, memory usage, and network traffic cases. 

I use it for monitoring and troubleshooting the performance of cloud-native applications.

Providing comprehensive visibility throughout the environment, it monitors my system, enhances career performance, and offers insights into the user experience.

Troubleshooting and visualizing a cloud-native environment is made easy with Splunk APM. It provides complete visibility into software tools, swiftly monitoring business performance and applications.

It possesses the capability to conduct distributed tracing within our environment. This includes monitoring the speed of tracked access, extending from end users to the Internet, system, and network services, and supporting my software application. Consequently, it offers an end-to-end overview of potential bottlenecks.

Splunk APM has significantly enhanced our organizational efficiency. Initially, my responsibilities included tracking website application performance, managing applications, and handling license releases. Now, it provides real-time user monitoring, transforming the way I handle these tasks.

It significantly impacts our organization's telemetry data, improving operational performance and user experience. The platform provides insights into application performance and effective log management. Ensuring accurate tracking of all performance-related logs contributes to building up the application performance percentage with comprehensive data.

It contributed to a daily reduction of six hours in our mean time to resolve.

The most valuable features are troubleshooting and optimizing application performance. 

Another value lies in the resilience and quick recovery capabilities offered by the SIEM. It enables thorough monitoring across our landscape, providing insights into the number of running software applications. The tool furnishes comprehensive information across microservices, significantly enhancing our proficiency.

Enhancing system availability and optimizing service performance are crucial. It is essential for the monitoring tool to deliver quick response times when generating analytical reports, instead of prolonged delays.

I have been using it for two years.

It provides good stability capabilities.

It has the capacity to scale. There are approximately two hundred users and one administrator that use it.

I would rate its customer service and support eight out of ten.

Positive

The initial setup was straightforward.

The deployment process took six hours. During this time, a clear understanding was established regarding which technical applications—whether cloud-based, native, or others—needed monitoring and improved performance. These categories were identified in-house, with two individuals overseeing the process.

It allowed our IT staff to focus on other projects by freeing up their time. In total, it saved around four hours.

We evaluated Grafana.

It can serve as an analytical application for enhancing performance, ensuring all dependencies are effectively addressed. Overall, I would rate it eight out of ten.

We have a lot of applications that we monitor. We have a lot of hardware that runs on VMware. We monitor all of that as well.

Dashboards have been helpful because people can go and look for themselves how their systems are running. The requests for us to go look at something have gone down because people can go and do it themselves.

It is important for us that Splunk Infrastructure Monitoring has end-to-end visibility. Developers and those types of teams can look at and troubleshoot any kind of issues quickly.

Splunk Infrastructure Monitoring has helped reduce our mean time to resolve, but I do not know how much. We just help as needed, but for the most part, it is just the teams going in there and looking at things themselves.

Splunk Infrastructure Monitoring has helped improve our organization’s business resilience.

Different teams can see a lot of different aspects of what is going on. They can see network traffic. They can see applications, and they can see hardware peaks and performances. They can see everything they need.

We could see the value of Splunk Infrastructure Monitoring within a couple of weeks of implementing it.

Dashboards help the application support teams to have a quick look at how their systems are running. It helps other teams as well.

They can get more integration with a few more products.

They can also update some of the dashboards that are in there now.

It is pretty good in terms of the ability to predict, identify, and solve problems in real-time, but there is always room for improvement.

I am in a new role. I have been there for two months. That is as long as I have been using it.

It is very stable. It is good.

Its scalability is great.

It is very good. I would rate them a nine out of ten. They are usually pretty helpful and knowledgeable.

Positive

We have it on-prem, and we also have a cloud instance. Our cloud provider is AWS. We do not monitor multiple cloud environments.

Deploying it was pretty straightforward. We just had to make sure that we were getting the logs right and setting the apps right. That was pretty much it.

We have seen an ROI in terms of manhours and less work for everyone.

I have always used Splunk.

I would rate Splunk Infrastructure Monitoring a ten out of ten. It is great. It is much better than a lot of other products, so it is definitely up there.