Splunk Observability Cloud Reviews

Currently, we are in the process of migrating from on-premises to Splunk Cloud as well as Observability. For metric-based monitoring, we can monitor via Observability and are migrating it there. We are setting up private locations to monitor synthetic tests, such as ping checks, port checks, and URL monitoring. The rest is metric-based monitoring, which is being done by Splunk using Splunk OTeL, which is an OpenTelemetry agent for Observability. This agent brings metrics from end devices to Observability. Based on these metrics, we set detectors and rules to trigger alerts.

Our observability is not yet live in production with Splunk Observability Cloud. It is currently being built, and we are adding new components, but it is not yet fully ready.

Comparing to Cloud, Splunk Cloud, or any other solution, the most valuable feature of Splunk Observability Cloud is that it is entirely based on metrics. The agent is also very lightweight compared to Splunk UF and does not consume much compute resources on the end server or host from which we are pulling data. However, it can only monitor metrics and cannot monitor logs.

Regarding how Splunk Observability Cloud has benefited our organization, we are yet to go live, but most of the configuration that requires conditions and triggers on Splunk Cloud involves writing queries. With Splunk Observability Cloud, the process is quite simple. We can directly get metrics flowing, set thresholds, and everything is UI-based. This requires less time to set up and use. I do not have that much visibility with Splunk Observability Cloud at this time as I am working as an administrator. It has helped us create dashboards for visualization purposes.

There is one thing that could be improved in Splunk Observability Cloud. We have the capability in Splunk to connect to Splunk agents such as Splunk forwarders from a deployment server and update the end agents and forwarders using server classes. We can push and update configurations from our own hosted servers without needing to access the end device. In Splunk Observability, the OTeL agent cannot be updated from our end. Every time we need to update, we have to reach out to users or gain access to the host to update the configurations. There should be a solution to update OTeL agents from Splunk Observability Cloud itself.

I have been working with Splunk Observability Cloud for approximately five to six months.

Splunk Observability Cloud is reliable based on my experience with stability and reliability so far.

We were facing some challenges with the stability of Splunk Observability Cloud regarding the login page. It was not working several times and was not accepting SSO authentication. The observability team found a solution for this issue, though I am not fully aware of the details. There were several times when opening the page did not directly log in and showed some errors.

I have not encountered any scenarios regarding the scalability of Splunk Observability Cloud. It should be good because it is cloud-based. I am not aware of the licensing model and how it scales or what the rules are for scaling.

I was not directly involved with technical support for Splunk Observability Cloud, but I am aware that my teammates reached out to support. They were finding issues regarding configuration, installation, and deployment of Observability for specific components. Since Observability is cloud-based and hosted by Splunk, the components we own on-premises are the OTeL gateways, agents, and private locations. They reached out to the vendor regarding these components, and the support was quite smooth. They have raised some bugs as well for the vendor to fix. I would rate the technical support from Splunk an eight out of ten.

Since it is cloud-based, Splunk Observability Cloud was ready to use upon deployment. The OTeL gateways were built by our team and required configuration. I was not part of that process but am aware that we needed to configure the OTeL gateways to route data to them as an endpoint and from there it would be ingested to Observability or forwarded to Observability. There were no significant issues with this process and it was quite smooth. However, configuring private locations on a few gateways was quite difficult to set up and maintain because Docker was going down at times. There were some issues that were discussed with Splunk vendor, and they provided guidance on how to fix them.

Our main use cases include synthetic monitoring, APM, RUM, alerting, detectors, dashboards, and all related functionality.

My favorite feature of Splunk Observability Cloud is the RUM because I like the RUM data. Splunk Observability Cloud has helped improve our operational performance and company's resilience, which was their initial offering. Without it, we wouldn't have any exposure and would be looking at raw data.

It can be improved through the integration of AI, which is either coming or already available.

I've been using Splunk Observability Cloud for two and a half years.

I've only experienced downtime, crashes, or performance issues when it isn't configured correctly.

Splunk Observability Cloud scales effectively with the growing needs of our organization; we simply need to pay more and ingest more data.

Prior to this, I wasn't using another solution to address similar needs.

I've seen ROI with Splunk Observability Cloud, though I cannot specify the exact amount. We would be unable to track user access issues without it, which would result in significant losses.

I use the cloud almost exclusively and am still learning some features. I handle synthetic monitoring, but don't manage all integrations or usage aspects. I need to explore the AI-powered analytics and guidance, as we haven't implemented it yet. The out-of-the-box customizable dashboards are effective because they contain all the necessary base components.

On a scale of one to 10, I would rate Splunk Observability Cloud as an eight. I appreciate the cloud because it provides more visibility into the user's path. It's quite good, though the observability aspect is somewhat complicated, primarily due to my limited experience with it.

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.

We use Splunk in APM to monitor our applications. So, we integrated it into our systems to enhance our monitoring and observing capabilities, especially for our microservices. 

So, we have used Splunk APM for this.

APM integrates well with Splunk’s other observability solutions. These logs with application performance monitoring can significantly impact our business in several positive ways, like troubleshooting and root cause analysis. 

Using these logs with APM, we can collaborate performance metrics with log data. It allows us to pinpoint the exact cause of issues, such as identifying specific errors in the logs. Because of this, we have access to faster resolution and detailed logs alongside performance metrics, enabling quicker diagnosis and resolution of problems. It also helps us minimize downtime and improve system reliability. 

Additionally, it improves our performance optimization with detailed insights and analyzing historical log data along with APM metrics. This allows us to understand long-term trends and make informed decisions about performance improvements and better user experience, like error reduction and proactive monitoring.

Splunk has reduced our mean time to resolution by 30%. 

If there is any issue in Splunk; we’ll identify the issue first and look for the error messages, like alerting with the Splunk user interface or in the logs that might indicate what the issue is and then determine which part of Splunk is affected. 

Then, we’ll refer to the Splunk official documentation and check the system's health. We’ll review the logs. By following these steps, I can resolve the issues with Splunk, ensuring that our monitoring and analytics capabilities remain effective.

Mainly, I like Splunk APM because it will show the errors compared to other tools. We use the dashboards to monitor our applications. It will tell us the errors, and we can solve them quickly.

I have used APM but haven’t used Trace Analyzer, though I have some knowledge of it. We are able to implement it. We have some Trace Log Points in Splunk APM to catch the errors. We have a special graph for it where we can see the red points.

We use OpenTelemetry. OpenTelemetry and Splunk APM are similar in terms of observability and monitoring. We use it for observability standardization, which allows us to collect traces and metrics, making it easier to work with different monitoring tools, including Splunk APM. It is more flexible because it allows us to instrument our applications without being locked into a specific monitoring vendor. 

It supports collecting traces, metrics, and logs from our applications, providing a comprehensive view of our performance and health endpoints. This data can be fed into Splunk APM, giving us in-depth analysis and insights about our application.

Splunk APM is a robust tool with many capabilities. There are always areas for potential improvement to enhance its functionality and user experience. 

For Splunk APM, there could be simplified navigation, like streamlining the user interface to make navigation more intuitive for our users, especially those new to APM, which can enhance usability. We can provide more customization options for dashboards and visualizations to help users tailor the platform to their specific needs. 

There could be more integration capabilities with a wider range of third-party tools and platforms would also be beneficial. By focusing on these areas, Splunk APM can enhance its value proposition, improve user satisfaction, and better meet the evolving needs of organizations monitoring their application performance.

I have been using it for a year. 

I never had an issue with the stability. It worked fine. 

My team has used alternatives to Splunk APM, like Datadog and New Relic.

The initial setup was easy. To fully deploy it, we had to add some signal effects into our applications and just deploy it. It took like 20 minutes. That’s it.

We took some help from our teams and my senior manager and also from other teams across our company. We connected and did all this together.

For deployment, one person can do it, actually, but as we are junior developers, we took help from our senior manager, like three to four people. 

Splunk is good like this now. I don’t think any updates would be required, but there are some regular updates and upgrades of Splunk APM, like software updates, version upgrades, and all. 

These provide more powerful monitoring capabilities and help ensure the system remains reliable, secure, and aligned with organizational needs. Regular updates, performance tuning, and proactive management help in maximizing these benefits of the Splunk solution.

We’ll see the results after the deployment. It’s not that late, and that’s the reason we are using Splunk APM.

 Splunk made our job easier in a way. It will give the points when we use any dashboards, and there are no delays in everything, like performance. It will give the error issues very clearly, and it will monitor 24/7. It will show the issues, and it is very effective. It will pinpoint the exact cause of the issues, and it will help us troubleshoot the issues very fast.

It benefits the IT staff in other teams, like operations, improves efficiency, and manages the IT environments more effectively. When it centralizes the logs and search analytics, the powerful capabilities allow IT teams to perform in-depth troubleshooting, identify root causes, and analyze complex issues with ease. 

Splunk also provides real-time visibility into IT infrastructure, and we have connected with cross-functional teams around our team to work with Splunk APM. It supports proactive management, enhances security, and improves operational efficiency. It facilitates better collaboration across the team.

The pricing is  based on several factors, including the scale of deployment. The pricing model typically includes considerations like the number of hosts, features, and capabilities. 

Overall, I would rate the solution a nine out of ten. 

We are using the Splunk Observability Cloud for monitoring purposes and troubleshooting, and we are using that infrastructure in real time, in which we have infrastructure monitoring, application monitoring, log observer, and RUM synthetic monitoring. For troubleshooting purposes, we are installing the open telemetry collector agent on some of the servers, including Intel, Windows, and UNIX servers. 

I have also worked on the agent upgrade from version 0.103 to 0.1113, which is ongoing right now.

We are also using the dashboards and detectors in Splunk Observability Cloud. For client needs, we are creating dashboards, reports, and detectors as well. For the detectors, we mostly work on host-down situations. When a server is down, we troubleshoot using the detector infra host down and identify the root cause of the failure, such as why it was down or not reporting to Splunk Observability Cloud. We find out the root cause by using that detector when the alert gets triggered and cleared.

We use the tracing features in the Splunk Observability Cloud, primarily for application performance monitoring. It helps us figure out service maps for root cause analysis. It provides visibility and helps address blind spots in data collection.

Splunk Observability Cloud offers a transparent, customized tool with real-time visibility. We use AWS, ReactJS, Python, and Java for tracing. It helps create customized dashboards and service maps based on customer requirements. It has AI that automatically generates visualizations, allowing us to create more reports based on customer needs. My seniors are primarily working on creating dashboards, reports, and for monitoring purposes.

Their technical team is performing well. About a year ago, Splunk Observability Cloud was slow and lacked features compared to now. It didn't provide exact details for any searched server in the metrics, but the situation has improved significantly, and we can now retrieve complete data on when servers were down or up.

The best features in Splunk Observability Cloud are the metrics; I can see any logs or anything related to the server or services we want to monitor, and the metrics are a good function. It provides exact details. It offers unified visibility for logs, metrics, and traces.

In Splunk Observability Cloud, I notice room for improvement in synthetic monitoring. It does not provide output based on server names. It only gives a response when we input a URL. I'm not sure if this issue is specific to my organization, but it would be beneficial if server details could be retrieved directly in synthetic monitoring.

I have been using this solution for two years and two months.

I would rate its stability an eight out of ten.

I would rate its scalability an eight out of ten.

Around 100+ users access Splunk Observability Cloud in my organization, including the cloud SRE team, Windows Intel team, Linux team, and AD team.

My client base primarily consists of enterprise financial services.

If any issues arise, we can raise a vendor case, and resolutions are provided in a timely and accurate manner. 

Positive

In my organization, we also work with Sentry, Datadog, PagerDuty, and Dynatrace. Splunk Observability Cloud offers more features than Datadog, which also provides APM monitoring, log observer, and metrics, but does not match the feature set of Splunk Observability Cloud.

It is a bit complicated. For deploying Splunk Observability Cloud, we first need an access token, after which we connect to our AWS Cloud account and provide the access token. We must set up CloudWatch or AWS Lambda and forward the metrics or logs from all sources to AWS.

The implementation took about 45 days.

The return on investment varies based on requirements; for smaller tasks, we can leverage our team's capabilities effectively, so I can estimate around a 20% efficiency gain.

Currently, we are providing outputs to clients within the required time frames. If a client requests any dashboard, logs, APM monitoring, or synthetic monitoring, we have been able to deliver output on time, achieving approximately an 80% efficiency in response.

Splunk Observability Cloud is expensive.

For operational performance, we created monitoring within the Splunk Observability Cloud for most servers with agent installation. We upgraded the open telemetry collector from version 0.82 to 0.103, then again to a newer version, enhancing visibility and use cases, especially after the upgrade, which has improved operational purposes.

My impressions of Splunk Observability Cloud for focusing on business-critical initiatives are positive. I manage six tools, but Splunk Observability Cloud is one of my favorites, and I aspire to build my career specializing in it because it has great features, more attention in the market, and is a relatively new tool with promising growth.

I would recommend Splunk Observability Cloud to other users for its accurate data fetching, dashboard creation, report generation, and synthetic monitoring capabilities.

I would rate Splunk Observability Cloud a nine out of ten.

We primarily use Splunk Real User Monitoring to analyze performance bottlenecks and application transactions. It allows us to see how applications are experienced on the user side, making it easy to capture any bottlenecks or performance issues.

The most valuable features include user time tracking and the ability to analyze application load times. Splunk provides advanced notifications of roadblocks in the application, which helps us to improve and avoid impacts during high-volume days. It is very useful for identifying performance bottlenecks.

It would be beneficial to have more enhanced features with capabilities to adapt more integrated applications. Improvements in dashboard configuration, customization, and artificial intelligence functionalities are desired. There is room for improvement in customer support due to delays and standard feedback responses.

I have been working with Splunk Real User Monitoring for almost two years.

In terms of stability, I would rate it a nine out of ten. It is a very stable solution.

Splunk Real User Monitoring is definitely scalable. I would rate its scalability a nine out of ten.

Technical support is rated an eight. There is some delay in their in-depth responses and standard answers to questions.

Positive

I worked with Splunk alongside Dynatrace. Before Splunk, I did not use any other services.

It takes about an hour to set up the client for real-time monitoring.

We have a separate team for deployment, consisting of about three to four people.

We have achieved a return on investment between 10% to 20% as it helped in removing roadblocks, which could lead to more savings with wider usage.

Splunk is a little expensive, however, it is in line with the current market pricing. I would rate the pricing an eight on a scale of one to ten, as it reflects the going rate in the market.

I would recommend this product to other users because of its capabilities in monitoring and analytics. 

I rate the overall solution eight out of ten, considering the comparison with other products like Dynatrace.

The main use case with Splunk Observability Cloud is to capture the logs from the SD-WAN in order to check the health of the network and the flow of data from different sources to the central place.

The best feature of this product is the latency and processing of all the telemetry that is being received, which gives full visibility at the right time. 

One cannot protect and operate what they don't know. When there is this observability, it helps to see exactly what is present, the problems that may exist, and hence, it increases digital resilience by having proactive actions ahead, which increases the availability of the service.

The teams have utilized the ability to enrich data with custom metrics, as this enrichment is one of the key aspects used to have a clear understanding of which assets are being attacked, enabling necessary actions to be taken. The data has been enriched by adding customized information from customers' databases from different sources.

The pricing would be one area for improvement.

I have used the SIEM solution since 2019 and have had experience with Splunk Observability Cloud for the last year.

I would rate their customer service and technical support an eight out of ten.

Positive

I work for SI, and we deliver to different organizations based on their requirements. We are responsible for implementation, so we implement and they see the value out of it.

Splunk Observability Cloud has improved the operational performance of our clients.

It is expensive.

The AI component is one of their strengths; currently, most competitors are moving in the same direction. As SI professionals, we are seeing different improvements in the AI domain for different products, and they are at the leading edge with many vendors following them.

My overall rating for Splunk Observability Cloud would be a seven out of ten.

Splunk is primarily used for log monitoring, where I collect all my security logs, system logs, and application logs into a centralized place. This helps me customize my monitoring models.

Splunk has provided me with a centralized platform to manage multiple features. Instead of using various products, Splunk offers everything in one solution, which adds value to my organization.

The most valuable feature is the ability to customize dashboards based on my queries or any other customization I may need.

I'm still experiencing some features of the product. However, in future updates, I would like to see more predefined monitoring query solutions, which could be more effective.

I have been using Splunk Synthetic Monitoring for almost five years, primarily focusing on log monitoring.

Overall, the product is stable, and I would rate it an eight out of ten.

For scalability, I would give it a nine out of ten.

Technical support is good but could be improved, particularly concerning the time taken for ticket resolution.

Neutral

The main reason for choosing Splunk over other products is its comprehensive capabilities and flexible customization options. It is widely used and provides cloud solutions.

The initial setup was quite straightforward, and agent installation can be done quickly. However, the entire setup process might involve multiple people due to organizational policies.

The implementation process involved around five to ten people due to our organization's processes and need for multiple approvals.

Using Splunk has saved my organization about 30% of our budget compared to using multiple different monitoring products.

Splunk is a bit expensive since it charges based on the indexing rate of data. However, considering the features it provides, the pricing is quite affordable compared to other monitoring solutions.

Overall, I would recommend Splunk to anyone seeking a monitoring solution, thanks to its extensive capabilities and features.

I'd rate the solution nine out of ten.

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 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.