Apache SkyWalking Reviews

My main use case for Apache SkyWalking is a project that started in 2023 for a retail client facing serious performance issues on their new distributed architecture on AWS. The technical criticality is clear. We have an observability black hole on a high-traffic payment flow where we cannot distinguish if latencies are caused by microservices on Amazon EKS or by calls to legacy on-premises databases. We chose Apache SkyWalking through the AWS Marketplace to integrate it immediately into the existing infrastructure with the goal of monitoring a massive environment consisting of over 80 microservices and about 600 active pods. This solution allows us to manage and analyze volumes in the order of 50 million traces per day, correlating every single end-to-end transaction in real time from front end to database and pinpointing bottlenecks that are invisible with traditional logging systems.

By analyzing traces provided by Apache SkyWalking, we identified a high volume of redundant and inefficient calls between microservices specifically, and synchronous calls that could be converted to asynchronous processes. Without this insight, we were over-provisioning our AWS resources just to keep the system stable. Once we applied the optimization suggested by the trace data, the system became significantly more efficient. We were able to process 30% more transactions per second using the same infrastructure, which not only improved the end-to-end user experience during the high-traffic sales peaks but also optimized the client's cloud spend by increasing the overall density and performance of our existing 600 pods.

Apache SkyWalking provided full visibility into the black hole because before using it, we could not see what was happening when a request left Amazon EKS and went to our on-premises legacy databases. Apache SkyWalking's distributed tracing correlates these two worlds in a single view, showing us that 40% of the latency was actually happening in the network hop between the cloud and the physical data center, not in the code itself.

Second, it exposes hidden architectural flaws. By using the automatic dependency mapping, we discovered that some microservices were stuck in a cyclic dependency which was documented nowhere. This visual evidence allowed us to refactor the logic and immediately increased our throughput by 30%.

Apache SkyWalking gave us database-level insight without database access. Through its slow query monitoring, the Java agents captured the exact SQL statements that were hanging during peak sales hours. This meant our developers could fix the exact line of code or index without needing to wait for a DBA to pull logs, reducing our mean time to resolution.

There are many features that are useful to mention in this case because we obtained different benefits. Apache SkyWalking automatically drew the topology of the 600 pods where we discovered cyclic dependencies between services that no one had documented before and that were slowing down the system. Another valuable feature is resolving hybrid bottlenecks because we isolated a specific network issue between AWS and the physical data center. Without distributed tracing, infrastructure teams blame Java code and vice versa. Database tuning is also important because thanks to slow query metrics captured by the agent, we identified and rewrote the SQL queries that most impacted performance during sales peaks.

Apache SkyWalking can be improved with storage management complexity because with this volume of 50 million traces a day, managing data retention on OpenSearch is critical. We had to implement custom logic to purge old data to prevent storage costs from exploding. Another area for improvement is the alert configuration because the out-of-the-box alerting system is basic, and we had to manually write complex rules to avoid false positives in activities that require expert time.

Apache SkyWalking is the first solution for this purpose that I have used in my professional experience.

Apache SkyWalking is really stable for us.

Handling 50 million traces a day is not trivial. Apache SkyWalking architecture, backed by a well-sized OpenSearch cluster on AWS, handled the load without any loss.

Apache SkyWalking is really scalable because we can break down its scalability into two main areas: horizontal scalability of the backend and storage scalability with Amazon OpenSearch. The real beauty of the system is in its non-blocking nature. The agents use a sampling strategy and asynchronous reporting, meaning that even if the backend is under heavy load, it never slows down the actual retail application. It is built for high-concurrency environments which gives us the confidence to monitor 600 pods simultaneously without fearing a system-wide collapse.

The biggest impact is the features that allow us to stop the blame game between the network, cloud, and database teams. By looking at the colored lines on the topology map, which turn red when latency exceeds our threshold, we can instantly see exactly where the bottleneck is located. It transforms a four-hour investigation into a five-minute visual check, and that is the key factor in improving our MTTR and increasing the system's overall throughput by 20-30%.

The mean time to resolution, the time to diagnose a critical incident, drops from four hours to less than one hour and 45 minutes. Coverage also increases; we went from siloed visibility to 100% tracking of critical payment transactions. These are our success metrics.

It is a good experience with pricing, setup cost, and licensing, but I am not in charge of this; the customer is in charge of the purchasing of the tool.

Apache SkyWalking is a very nice tool and an exceptional tool for managing volume and complex architecture on AWS without the prohibitive cost of commercial suites. I would give this product a rating of 9 out of 10.

Public Cloud

Amazon Web Services (AWS)

The main use for Apache SkyWalking is application monitoring and troubleshooting during integration.

The best features of Apache SkyWalking are distributed tracing, service topology visualization, and trace-correlated logs and metrics. This makes it much easier to follow requests across multiple services, identify where an integration failure occurs, and troubleshoot performance issues. I also find the dashboards, alerting, and profiling capabilities useful for investigating issues during testing.

These specific features of Apache SkyWalking have made troubleshooting more collaborative and efficient. Instead of relying only on application logs, our team, including the QA and development teams, can follow the same trace, quickly identify the service causing an issue, and share concrete evidence. This helps us respond to integration failures faster and makes communication between QA, developers, and operations more focused.

The dashboards of Apache SkyWalking are particularly useful for getting a quick overview of service health, performance, and dependencies. The alerting capabilities also help the team detect unusual behavior and performance degradation earlier, rather than waiting for users or testers to report an issue.

Apache SkyWalking has improved our ability to troubleshoot distributed applications and resolve integration issues more quickly. It gives QA, developers, and operations better visibility into service interactions, which reduces the time spent identifying root causes. It has also improved collaboration because we can share traces and monitoring data as concrete evidence when investigating defects.

Regarding the metrics, we saw improvements mainly in troubleshooting and incident resolution. For example, Apache SkyWalking helped reduce the time needed to identify the affected service during integration failures from roughly 30 to 60 minutes of manual log investigation to around 10 to 20 minutes when a trace was available. We also had better visibility into response times, error rates, and service dependencies, which helped us identify performance regressions earlier.

Apache SkyWalking could be improved by making the initial setup and configuration more straightforward, especially for teams that are new to observability. More guided tutorials, ready-to-use dashboard templates, and perhaps some troubleshooting documentation would help reduce the learning curve.

I would also prefer to see even more out-of-the-box integrations with common CI/CD, cloud, logging, and incident management tools, so teams can connect Apache SkyWalking to their existing workflows with less configuration. The UI is already quite powerful, but it could be made more intuitive for new users, particularly when navigating between services, traces, logs, and dashboards.

I have been using Apache SkyWalking for around one year.

Apache SkyWalking has been very stable in our experience.

Apache SkyWalking is highly scalable, particularly for distributed and cloud-native environments.

Regarding customer support for Apache SkyWalking, I would give it eight out of ten because it is good. It is more community-driven than traditional commercial support. There is strong official documentation, GitHub discussions, and issue tracking, as well as Apache mailing lists and Slack channels where users can ask questions and troubleshoot problems. The main limitation is that there is not the same dedicated, guaranteed response support model you would typically get from a commercial observability vendor.

The pricing experience was quite positive because Apache SkyWalking is open source, so there are no traditional software licensing fees. The main costs are associated with deployment infrastructure, storage, monitoring, and the time required for initial setup and configuration.

We have seen a positive return, mainly through time saved during troubleshooting rather than direct licensing savings. In our experience, Apache SkyWalking reduced the time spent investigating integration issues by roughly 30 to 40%. For instance, an issue that previously could take 30 to 60 minutes of manually checking logs across different services could often be narrowed down to the affected service within 10 to 20 minutes using distributed traces and service topology.

We evaluated several options such as Grafana with Tempo and Elastic APM. We chose Apache SkyWalking because it provides a more complete observability platform, combining distributed tracing, metrics, logs, service topology, profiling, and alerting in one solution.

Apache SkyWalking's AI capabilities have a good approach to governance and security because the AI assistant is designed to work within existing permissions and is read-only, so it does not automatically make changes to the monitored environment. It also supports authentication, TLS, and access controls over the underlying observability data.

The AI output of Apache SkyWalking is very useful and generally reliable for investigation and troubleshooting, especially because it is grounded in live Apache SkyWalking data such as metrics, traces, logs, and topology, rather than relying only on the model's general knowledge. However, I would not treat it as completely correct. I would still need to validate things before moving to production.

Start with a focused use case rather than trying to monitor everything at once. I would give Apache SkyWalking an overall rating of nine out of ten.

Public Cloud

Amazon Web Services (AWS)

My main use case for Apache SkyWalking includes not only monitoring microservices and APIs but also managing the entire health of the application. I will explain the domains and backgrounds where we currently use it. It does more than check microservices or heavy queries. It can be integrated from the IT point of view, where your IT team can easily integrate it on the DevOps side or where applications are being deployed. It is widely used for managing the entire health of the application, checking the current status and health of the application, and how your services are currently running. When I mention services, this essentially means your queries, including database queries or backend logic that has been written to perform either up-syncing or down-syncing of data into the database or retrieving something, updating or inserting queries. Apart from that, it can be used for checking how your APIs are working, such as out of multiple API calls, how many are succeeding versus failing. If there is a particular timeout, I can see the frequency of recurrence and the time duration of the timeout or how long the network is unreachable.

Even in payment applications where we have multiple applications, some related to payment processing may be failing or insert queries may not be working. There could be multiple layers on the backend side of the architecture design, and during issue resolution, it is very difficult to analyze where the actual pain point area is. Apache SkyWalking really helps identify that a particular API call failed on the payment side, perhaps deep down three layers in the architecture design, so you can see that it is failing because of a specific reason, such as network timeout, unreachable network, the bank server being down, or a third party payment server integration not responding due to heavy traffic load.

In some other domains, beyond checking health, if your applications or servers are running on pods or Kubernetes containers, you can check the health of your pods as well. We have moved from outsystems to Mendix and other Java hosted applications and .NET, which all utilize Kubernetes and nodes. You can easily check which node is working fine and which is not in good state, how much traffic is currently passing through those containers or nodes, how they are integrated, and which one is responding fine versus not responding well enough. These are many areas where you can easily identify issues with the help of Apache SkyWalking. Because of its open use case platform, it helps from the licensing point of view and covers a wide area of use cases.

In terms of projects, I would like to share a couple of examples. One of our patient services applications was facing issues with API failures. It was initially identified that this might be because of Java database upgradation, the fact service getting down, or perhaps a global outage of some database server, so the entire API services was getting affected. Then some fact line services started getting impacted, and because of that, a few of our Mule APIs were not working fine. Since the project had the dependency of cross-functional team members, each team was trying to identify where the actual cause was lying. At a high level, we thought that the Java API might not be connecting properly with the fact API or the Mule API internally calling the fact API, which was not getting reached properly. Someone was trying to reach out to the Mendix team to see if they could figure out and find the logs, and it could be the .NET or other applications depending on what kind of application the team was currently working on. With the help of Apache SkyWalking, you can definitely have this in place and easily identify that for this particular time duration, this was the API call that went off and this was the feature that got stopped, and these are the documents that did not reach properly. You can easily identify the area and reach out to that team, stating that you need to check out these particular APIs, and you can reach out to the support team or the vendor if needed so that on the particular SLA, those can be taken care of on priority.

Apart from that, there is one more use case I would like to share regarding one of the applications on the local platform we built. Apache SkyWalking can be integrated there also because most of the time when a lot of traffic is coming for a particular second, there is sometimes a huge spike on Grafana or the logs and it is very hard to see that for a particular instant this much huge traffic is coming while your CPU or memory is quite low. You need to increase your space, but the logging is not able to maintain properly or pods are getting crashed and new pods are getting recreated. It is very hard to identify the logs to understand what is happening. Even in that area, you can easily integrate Apache SkyWalking and easily identify your Kubernetes containers and node health.

Apache SkyWalking offers the best features for integrating into the IT department to check microservices, the entire end-to-end health of the application, the node, Kubernetes, which queries are running fine, and which queries are running slow. From the SLA side, most queries should get completed within 200 milliseconds. If it is taking longer than that expected time, someone has to take the initiative to see where the room for improvement is.

I have been using Apache SkyWalking while encountering a couple of scenarios in the IT department along with a couple of projects we were working on. That is where I was doing some self-exploration to see how we can try to get through the bottlenecks of the root causes and how we can easily identify what the RCA is, why lots of microservices and APIs are getting failed, and what the bottleneck is. Because that project had a dependency of cross-team members, that is where I got to know about Apache SkyWalking and explored it. It is a really wonderful tool to go ahead with the IT team.

Apache SkyWalking helps me visualize data and performance by easily visualizing how the entire ecosystem is currently working. For example, if we have lots of Kubernetes containers in place and nodes being interconnected to multiple projects or products inside the organization, manually it is very hard to check out and take the export of the health of the containers and see how the traffic is going through which container is fine or bearing a lot of load and how we can shift it. Manually, it is going to take a lot of time. Visualizing it with the help of Apache SkyWalking is going to be a game-changer in such a way, reducing your time on that. You can easily visualize how the entire ecosystem is currently working. You can see where the current health is pretty much good and where the health of the system is degrading so that concern can be put into that sector as soon as possible.

Apache SkyWalking has positively impacted my organization by reducing the time of the team so that they can put in more efforts into their other tasks, saving a lot of time, improving our SLA in resolving any issue, providing good RCA analysis to the leadership team, and helping us in monitoring the entire health in a shorter time span.

Apache SkyWalking can be improved by enhancing a few things. The learning curve is definitely there, so it needs a good learning curve. Your engineers or experts need to be pretty much handy and sound on the technical side to use this platform. If it comes to customization, that is where it needs a deep understanding. The normal configuration you can easily do, but on the heavy customization side, it needs a good learning curve.

Secondly, on storage management, because you are doing the entire health checking and continuously monitoring the entire ecosystem, it occupies a lot of space. You need to either purge that storage and have something that can be recycled or put in some additional space in the archive section, and you can easily retrieve that after a certain period of time. If a million of records or traces are there, it becomes a heavy task. Storage management is something where it can be improved or explored much more to provide much more ease and convenience to the users who are opting for it.

Thirdly, some UI modifications can also be done to make it much more beautified. I would add that Apache SkyWalking should improve the storage complexity and how we can easily manage that, and some customization and heavy customization learning curve can be reduced. Making the UI much more convenient and much more beautified for the end user would be beneficial.

Apache SkyWalking performs well in terms of reliability and uptime. The only issue is with managing its storage complexity, which is the major one. Apart from that, it is pretty good.

Apache SkyWalking integrates with other tools or platforms in my environment easily if you have a good learning curve on that and are able to understand the technicalities. The DevOps team will be able to do that easily.

Apache SkyWalking handles security and compliance requirements in my environment effectively, and I have found it helpful in identifying where the pain point area is while helping us in the RCA.

My advice for others looking into using Apache SkyWalking is that if someone is really interested in identifying the entire health of the application and how the ecosystem is currently working without giving load to the developers to write down any particular code to check health statuses, they can definitely go with Apache SkyWalking. That helps in identifying the entire thing for their application ecosystem, Kubernetes, cloud services, nodes, and monitoring their microservices call, the API calls, and current functioning. Deep down inside the architecture planning, it helps in identifying and monitoring and managing the entire application, helping you to reduce your SLA in solving out your issues, any kind of hot potato incidents or hot fixes or any impediments which the team is getting blocked with. That is where it helps a lot in identifying the area of improvement and also sharing the reports with the higher leadership team members. I would rate this solution an 8.5 out of 10.

Public Cloud

Amazon Web Services (AWS)

I have been using Apache SkyWalking for three years to monitor systems, cloud-native applications, and my Kubernetes environment. With this, I am able to analyze and troubleshoot applications in real time without experiencing any lags.

I use Apache SkyWalking mostly for database monitoring, particularly for my MySQL database, where I am able to monitor response time and identify any latency or errors. Apache SkyWalking has enabled me to associate application logs to identify errors that have been helpful in my work.

When monitoring my MySQL database with Apache SkyWalking, I am able to report issues and get an overview of the multiple services running in my SQL. I receive alerts if there are any anomalies in my SQL, and Apache SkyWalking has been very useful in helping me troubleshoot my MySQL in real time without any challenges.

Another important aspect of my use case with Apache SkyWalking is its role in designing my Kubernetes monitoring to help me monitor pods, nodes, and the container workloads currently running. With this, I am able to know the performance metrics for the applications running in my Kubernetes.

In my opinion, the best features that Apache SkyWalking offers include service topology mapping where you can automatically discover service dependencies. Another valuable feature is application performance monitoring, where you can track response time in real time. Additionally, metrics collection allows you to collect metrics from databases, operating systems, and applications. Another useful feature is log correlation, which helps link logs and allows you to connect log entries for faster incident investigation.

Apache SkyWalking has impacted my organization positively by supporting integration with external systems in place in the environment. The log correlation feature has hastened incident investigation and helped connect requests with distributed traces. Multi-language agent support is a modern feature that makes Apache SkyWalking suitable for organizations using diverse technology stacks, such as Python, PHP, and Rust. Since it is open source, it is a fully useful tool. The fact that it supports Kubernetes and other cloud-native deployments has helped the organization remain secure from a security perspective. It has also helped scale large microservices environments since I can monitor different applications simultaneously using Apache SkyWalking, which has optimized application performance and boosted system health through increased visibility.

Regarding Apache SkyWalking's AI capabilities and governance and security, since Apache SkyWalking is an observability platform that contributes to operation security and monitoring for artificial intelligence systems through tracing, metrics, logging, and anomaly detection, I believe that from a governance perspective, it is not a dedicated AI governance solution. Organizations seeking comprehensive AI governance will typically pair Apache SkyWalking with governance processes and specialized tools that address model life cycle management, AI risk compliance, and responsible AI practices. For AI and compliance, I would rate it a two, but for operation monitoring and security, I would rate it a five.

Regarding Apache SkyWalking's AI capabilities in terms of accuracy and reliability outputs, Apache SkyWalking does not use AI algorithms to make predictions or decisions, so it does not have an algorithm accuracy matrix. It is an observability platform, not an AI model.

I rate Apache SkyWalking eight out of ten because it is a powerful open-source observability platform that is well-suited for organizations running microservices and cloud-native applications. However, the learning curve and deployment complexity may require experienced administrators, which prevents it from receiving a perfect score.

I have been using Apache SkyWalking for three years.

Apache SkyWalking is stable in my experience.

For Apache SkyWalking's scalability for my organization, I would rate it an eight. With that rating, we are able to accomplish a lot regarding the features in place, which has helped reduce significant time wastage in monitoring applications across the environment.

The customer support for Apache SkyWalking is perfect.

I have never switched to any other solution or used any other solution before Apache SkyWalking.

Apache SkyWalking has been deployed using a typical cloud environment with agents installed in a cloud environment. For the back-end, I typically use Elasticsearch. For the agents, I have a cloud server being used. For the BTM analysis platform, I have a different server in the cloud that provides service topology and stores the processed data.

A relevant metric that we were able to capture is the time saved in associating multiple applications simultaneously to identify any downtime or errors. Since we are able to monitor applications at once in the same environment, significant money has been saved because we did not have to deploy multiple solutions for application reviews. Apache SkyWalking has enabled us to save considerable money in this regard.

I did not choose any other options apart from Apache SkyWalking.

Apache SkyWalking is very useful and a nice application. I am able to perform root cause analysis, conduct monitoring, and have end-to-end visibility across distributed applications. There is also automated service discovery with the topology mapping. I believe Apache SkyWalking is on a safer side, and I do not have anything that I would need them to add or change. I am comfortable with what it offers right now. Apache SkyWalking is the best and will not fail you. Regarding cloud deployment, it is a very good application. I give Apache SkyWalking an overall rating of eight out of ten.

Public Cloud

Other

Apache SkyWalking is my primary tool for application performance monitoring and distributed tracing. On a daily basis, I use Apache SkyWalking to monitor application health and trace requests across microservices to identify performance bottlenecks, monitor response time, and troubleshoot application issues before they impact end users.

In an e-commerce deployment consisting of multiple microservices such as user management, product catalog, payment gateways, inventory, and order processing, Apache SkyWalking provides end-to-end request tracing. When a customer places an order, I can track the request as it travels through each service. If latency occurs in the payment services or inventory databases, Apache SkyWalking immediately identifies the affected component and helps us resolve issues much faster.

Apache SkyWalking provides visibility into complex distributed systems where traditional monitoring tools often struggle to pinpoint root causes.

Apache SkyWalking offers several best features including distributed tracing across microservices, real-time application performance monitoring, service topology visualizations, Kubernetes and cloud native integrations, and low monitoring overhead. The platform is open source and has a highly scalable architecture.

Service topology visualizations represent the most valuable feature. It provides complete visibility into how requests move between services, making troubleshooting significantly faster and reducing the time required to identify the root cause analysis and performance issues.

Apache SkyWalking has significantly improved application visibility and reduced troubleshooting times while enhancing security reliability. Operations teams can proactively detect issues before they affect customers, leading to better service quality and improved user experience. I have achieved approximately 50% reduction in troubleshooting time, a 30% improvement in incident resolution speed, faster root cause identification, and improved application uptimes. Additionally, reduced mean time to resolution (MTTR) represents specific outcomes and metrics I can share.

Apache SkyWalking provides strong observability and monitoring capability. From a governance and security perspective, it supports secure communications, role-based access controls (RBACs), and data collection controls. Organizations can implement security best practices to ensure compliance and meet governance requirements.

Areas for improvement include simplified initial deployment and configurations, better documentation for advanced use cases, and more built-in dashboards and reports.

A more user-friendly onboarding experience would help organizations adopt the platform more quickly, especially teams that are new to distributed tracing technology.

I have been using Apache SkyWalking for the last two years.

The tracing and tracing data generated by Apache SkyWalking is highly accurate and reliable when agents are properly configured. The platform will deliver its best results and consistently provides actionable insights that help operations teams make informed decisions.

Apache SkyWalking is deployed in a hybrid cloud environment across Kubernetes and OpenShift clusters, with the backend component providing scalability and high availability.

Apache SkyWalking is an open-source solution that I deploy directly using Kubernetes manifests, Helm charts, or in some environments, operator-based deployments.

If you have hosted microservice-based applications in any environment and want to track requests and transactions between microservices, Apache SkyWalking is an advanced monitoring tool that will help monitor microservice-based applications and check application health.

I would rate this product an 8 out of 10.

Hybrid Cloud

Amazon Web Services (AWS)

We have running Kubernetes clusters as well as the database and other servers with the main use case for Apache SkyWalking being the need for monitoring metrics and traces, and the slow endpoints of the applications, and the service-to-service communication chart and the graph of how the services are communicating with each other. Everything was under one umbrella of Apache SkyWalking.

We were facing a slow endpoint in our application, and this specific example of how I use Apache SkyWalking in my day-to-day work helped us there and also query some queries of the database which needed to be optimized. Apache SkyWalking helped us there.

I use OpenTelemetry to get the metrics out and the traces out of the application and the Kubernetes cluster and the servers, which is how I use Apache SkyWalking. I get that into Apache SkyWalking and its own good GUI. It is easy to integrate into the system. We have the Java applications, so it had also a dependency which we can add into the pom.xml of the Java application in the Spring Boot. This is the current situation where we used it and we got facilitated.

The communication from one service to other service with the best features Apache SkyWalking offers is the chart, the graph it displays, and the communication bubble which moves from one application to another, and also the name of the application and the connection to the database server. The whole chart explains the infrastructure very well to any other person. This is one of its beautiful GUI features. Additionally, we can get everything at one place. We don't need other tools such as ELK and other trace tools such as Zipkin or Jaeger. We just get everything in one single place.

These features have helped my team or made my work easier when we diagnosed our slow endpoints. Additionally, when we were going to deploy the new applications, we just had to confirm how it is communicating with other services and if it is able to make the connection to the database server. At that point, we were just being feasible while recognizing what the state of the application is. The alerting system for the machines, for the VPS, and for the Kubernetes cluster helps us in a lot of ways and in a lot of areas.

Apache SkyWalking has impacted my organization positively. We implemented it to diagnose the slow endpoints and we just started and integrated the other things, the DB, the Kubernetes clusters, the metrics, the logs, and the alerting systems. Apache SkyWalking helped us in every perspective.

Comparably, if we compare Grafana and Apache SkyWalking, they are both nearly equal in order to provide and send the alerts, which shows how Apache SkyWalking has benefited my organization. Apache SkyWalking made a difference that we can get everything at one place while with other tools, we have to deploy a single by single, one by one.

Features are good, but there is something about the UI, which I would suggest. There is space to enhance it and make it look less pointy from the edges and not like an old UI. There is a space where we can develop a more eye-catchy UI. Otherwise, it is all good.

Apache SkyWalking can be improved by addressing the UI, which is a thing that needs to be less complicated. A simple documentation on how to integrate it with Kubernetes and in the different Kubernetes clusters provided by different cloud providers such as GCP, OKE, the EKS, and even integrating Apache SkyWalking with Ranchers too would be beneficial. A good UI which should not be pointy and should be eye-catchy and acceptable would be beneficial.

I have been using Apache SkyWalking since 2021 and in the start of 2022. This is the exact date I started working with Apache SkyWalking.

Apache SkyWalking will take time to get mature, which is my perspective on its stability. Right now, I don't think it is stable.

Before Apache SkyWalking, I previously used ELK, Grafana, and Zipkin.

It is fine regarding my experience with pricing, setup cost, and licensing.

There is nothing in terms of evaluating other options before choosing Apache SkyWalking.

There are modifications which I already mentioned while explaining why I chose seven out of ten for Apache SkyWalking. These relate to AI, the deployment procedure, and the integrating procedure of Apache SkyWalking. There should be good documentation. The other thing is about the user interface.

I haven't experienced Apache SkyWalking's AI capabilities regarding its governance and security yet.

I haven't used Apache SkyWalking's AI capabilities regarding its accuracy and reliability of output.

Everything is good in terms of improvements needed for Apache SkyWalking that I haven't mentioned yet.

It depends upon their usage for others looking into using Apache SkyWalking. I won't recommend it to everyone if he or she is not operating a very big infrastructure. Then there is no need to use it. If someone is using a bigger infrastructure and wants everything in the right place and all in one place, then Apache SkyWalking would be ideal for that person. I rated Apache SkyWalking seven out of ten.

Public Cloud

Google

My main use case of Apache SkyWalking is for application performance monitoring.

I have been using Apache SkyWalking for application performance monitoring, service monitoring, and distributing tracings. I found it useful for monitoring application performance and tracing requests across different services. The dashboard and service dependency information helped with troubleshooting and identifying performance issues. The initial setup can be somewhat complex, but once configured, the platform is easy to use and provides valuable observability information. Overall, I have a positive experience with Apache SkyWalking and would recommend it for all teams working with distributed applications.

For my use case, I typically use Apache SkyWalking mainly for daily monitoring and troubleshooting processes, which helps us track service performance and request flows, response times, errors, and dependencies across different services. When an issue occurs, I use Apache SkyWalking to identify which service or component is causing the problem and follow the trace to understand the root cause. I also use it to monitor application health and performance trends, which helps us detect issues early and reduce troubleshooting time. Overall, Apache SkyWalking gives us greater visibility into our distributed applications and makes it easier for the development and DevOps team to identify and resolve production issues.

The best features of Apache SkyWalking are its comprehensive monitoring capabilities.

The two features that stand out to me are distributed tracing, which helps define the specific service, and service performance monitoring, which is vital for day-to-day operations. I can monitor response time, error rates, and service health and dependencies through Apache SkyWalking dashboards, which helps me detect performance problems early and makes production troubleshooting much faster.

I actually use these features on a day-to-day basis to troubleshoot distributed applications, with tracing and performance monitoring helping me find the root cause of issues, understand service dependencies, and improve application reliability.

Apache SkyWalking can be improved by making the dashboards easier to customize, improving alerting options, and adding more detailed troubleshooting information. Better integration with other monitoring and logging tools would make it more useful.

I would like to see improvements in dashboard customizations and alerting, as well as easier integration with other monitoring and logging tools.

I have around five plus years of experience with Apache SkyWalking.

The initial setup can be somewhat complex, but once configured, the platform is easy to use and provides valuable observability information.

I primarily deploy Apache SkyWalking in a public cloud environment, which helps me monitor cloud-based applications and microservices, track performance, and troubleshoot issues.

I use AWS, Azure, and Google Cloud as cloud providers.

I have not purchased Apache SkyWalking through the AWS marketplace; I use an open-source monitoring solution in my cloud environment.

There have not been any major improvements needed so far, as the current features meet my main monitoring and troubleshooting needs.

I would recommend starting small by instrumenting the key services first and using Apache SkyWalking to monitor performance, traces, and errors, then gradually expanding it across the systems.

In terms of comparison, my review of Apache SkyWalking helps me structure my thoughts into specific areas such as system performance, usability, and architecture. I would rate this review a nine out of ten.

Public Cloud

Other

Our main use case for Apache SkyWalking is for monitoring Java application servers such as Tomcat and WebSphere Liberty.

We have continuous information about the status of the application servers.

The best features that Apache SkyWalking offers are its user interface with graphical panels and charts. What I appreciate most about the user interface and panels is the ability to use different time periods in the chart and track resource consumption over time.

Using Apache SkyWalking has had a positive impact on my organization because it has enabled us to identify the causes of various problems more quickly. Identifying causes is now approximately five times faster than before.

Apache SkyWalking can be improved by responding more quickly to new versions of monitored products, including, for example, taking into account changes in Java class names.

I have been using Apache SkyWalking for about two years.

Apache SkyWalking is stable in my experience.

We haven't had any issues with Apache SkyWalking's scalability because we use it in a relatively small environment where we monitor dozens of servers.

My experience with Apache SkyWalking customer support has been good. I have contacted the support community several times and the responses have been very quick.

Positive

Before Apache SkyWalking, we used Broadcom CA Spectrum. We decided to switch from Broadcom CA Spectrum to Apache SkyWalking because of the license cost, as Apache SkyWalking is free.

The initial installation of Apache SkyWalking went smoothly according to the available documentation.

I can't say how much ROI we've gotten from using Apache SkyWalking because I don't know the price of the original product (Broadcom CA Spectrum).

Our experience with pricing, setup cost, and licensing for Apache SkyWalking is positive since it is free, which was the reason for our decision to use it.

We did not evaluate other options before choosing Apache SkyWalking.

I recommend others looking into Apache SkyWalking to try it. I have given this review an overall rating of 8.

On-premises