The APM, RUM, synthetics, and infrastructure host maps have been some of the most popular and commonly used features. 

Overall, the Data UI and the usability of customer features continue to improve. 

The RUM session data and replays are much more convenient and applicable than other tools I have worked with in the past, and by combining multiple capabilities or features together, there is full visibility across the technology stacks and can identify specific bottlenecks or areas for risk and vulnerabilities to be likely to exist. 

Watchdog insights take the work out of the hardest part, helping us identify the issues before our customers.

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The most valuable aspects of the product include the APM and profiler.

These two have given us insights into things that are very difficult to track down given the standard OS (Linux) tools. 

The native memory tracking is super difficult to see exactly where it comes from. I attended a course (continuous profiling), and it showed me the potentially very important capabilities.

If you add these details to a standard dashboard, or a sub-dashboard for techy people, or even just a notebook, it would be easy to identify issues before they occur.

Combining these details with the basic tools (infra, logging, APM, and good rules), Datadog can easily show the details that a true engineer would need. It isn't just for monitoring, however, I see the value in it for engineers.

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I like that you can build out a dashboard pretty quickly. There are some things that come out of the box that you don't really need to do, which is great because they're default settings. Once you install the agent on the machine, they pick up a lot of metrics for you that are going to be 70 or more percent of what you need. Out of the box, it's pretty good.

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We like the distributed tracing and flame graphs for debugging. This has been invaluable for us during periods of high traffic or red alert conditions. It has also informed our developers on how our various systems are interconnected and the downstream effects of the problems we might encounter for certain services.

We're still working on getting widespread adoption of these products. Still, we're already seeing a shift in the developer's perspective from application-specific and starting to look at things from a more holistic systems perspective.

While this is not part of the question, this is relevant: Now that I've learned more about RUM, this will be something that we will heavily leverage moving forward to give us a whole complete view of our system from the front and back end perspective.

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The most valuable features of Datadog are the flexibility and additional features when compared to other solutions, such as AppDynamics and Dynatrace. Some of the features include AI and ML capabilities and cloud and analysis monitoring 

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The solution allows flexibility and heightened observability for presenting data, creating indicators, and setting service-level objectives. There are interesting options for monitors and features that offer flexible ways to analyze data.

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The most valuable features are the dashboards and the reporting.

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I like the amount of tooling and the number of solutions they sold with their monitoring. Datadog was highly intuitive to use. 

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Most of the features in the way Datadog does monitoring are commendable and that is the reason we choose it. We did some comparisons before picking Datadog. Datadog was recommended based on the features provided.

Most of the monitoring tools nowadays are have or are going to have embedded artificial intelligence and machine learning to make monitoring and logging more proactive and intelligent. Datadog has incorporated some artificial intelligence.

The solution does not require a lot of maintenance.

The solution had all the features we were looking for and we were able to create a central dashboard as per our requirements.

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For use, the most valuable features we have are infrastructure and APM metrics.

The seamless integration between Datadog and hundreds of apps makes onboarding new products and teams a breeze. 

We rely heavily on the API crawlers Datadog uses for cloud integrations. These allow us to pick up and leverage the tags teams have already deployed without having to also make them add it at the agent level. Then we use Datadog's conditionals in the monitor to dynamically alert hundreds of teams. 

With the ServiceNow integration, we can also assign tickets based on the environment. Now our top teams are using the APM/profiler to find bottlenecks and improve the speed of our apps

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Datadog has flexibility.

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Datadog provides tracing and logging, whereas Dynatrace focuses on tracing, and Splunk is more of a logging tool. Datadog's advantage is that we don't need two tools. 

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Flame graphs are pretty useful for understanding how GraphQL resolves our federated queries when it comes to identifying slow points in our requests. In our microservice environment with 170 services. There are complex transactions over the course of a single user request since we essentially operate as a middle layer with 90 back office systems we integrate to.

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The APM and tracing provide visibility and the ability to get right to root cause issues while being able to deploy new services without much need for custom instrumentation quickly

The active monitoring (static monitors, threshold monitors) has been very helpful. We get a lot of value out of anomaly detection. SLOs and monitoring of SLOs have been extremely valuable.

The metrics and out-of-the-box infrastructure metrics that come with the Datadog agent installation are quite helpful to the organization. We have made use of both the custom metric implementation as well as the log-based metrics which are extremely convenient.

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The most valuable features include:

• Integrated observability data ingestions: All data that Datadog collects is connected. This allows easily connected logs with failed requests, and slow database questions with services and requests.
• Broad integrations allow us to monitor our entire production environment in a single place, not just cloud resources. Since all parts stream metrics, logs, and events to Datadog, we can have unified dashboards and manage monitors and incidents all from the same page.
• A high level of configuration. We can configure and modify many parts, from how data is collected from our applications to how Datadog parses and visualizes it. This means that we always get the best experience, and we don't need to find ten different products that do small things well or settle on one product that does everything badly.

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For use, the most valuable features we have are infrastructure and APM metrics. The seamless integration between Datadog and hundreds of apps makes onboarding new products and teams a breeze. 

We rely heavily on the API crawlers that Datadog uses for cloud integrations. These allow us to pick up and leverage the tags teams have already deployed without having also to make them add them at the agent level. Then we use Datadogs conditionals in the monitor to dynamically alert hundreds of teams, and with the ServiceNow integration, we can also assign tickets based on the environment. Now, our top teams are using APM/profiler to find bottlenecks and improve the speed of our apps.

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Datadog has clear dashboards and good documentation. 

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I really enjoy the RUM monitoring features of Datadog. It allows us to monitor user behavior in a way we couldn't before. 

It's useful to be able to obfuscate sensitive information by setting up custom RUM actions and blocking the default ones with too much data. 

I also like being able to generate custom metrics and monitors by adding facets to existing logging. Datadog can parse logs well for that purpose. The primary method of error detection for our external website is synthetic tests. This is extremely valuable for us as we have a large user base.

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The log stream has been the most useful thing. Having so many logs on so many different running containers means it is very inconvenient to view them individually. Datadog's log aggregation is really helpful since it lets me and every other engineer on my team login, view, and share logs when we need to debug our application. 

APM has also been extremely helpful for debugging issues and profiling and optimizing our apps. Dashboards have also been really helpful for communicating needs and priorities to engineering leadership. 

It is very easy to get buy-in with graphs to back things up.

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We've found it most useful for managing Rstudio Workbench, which has its own logs that would not be picked up via Cloudwatch. 

Datadog allows for much better visibility across our entire fleet and has saved us countless eng hours as a result. 

We plan on trying out offerings such as APM moving forward too.

Some things that Datadog does very well:

• Technical documentation (the docs are clear, concise, and include realistic code samples)
• Overall education efforts (e.g. the codelabs/workshops)

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Datadog is easy to use and easy to deploy. It's a better solution compared to others on the market in terms of being budget friendly for our customers.

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The features I have found most helpful are synthetic monitoring, APM, and alert features. The monitoring functionality, in general, and tagging infrastructure are great.

Synthetics have become bread and butter for us as we have migrated many tests over to Datadog. We have simplified and consolidated our synthetic tests while also making them more robust with the help of your tagging. 

A large portion of our monitoring is based on synthetics results, and alerts integrate seamlessly without an incident queue system. We use dashboards heavily. 

The metrics capabilities are extremely helpful, and we use virtually all of the widgets.

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The most valuable feature for me so far is logging. We do not do integration tests, so we rely a lot on tracing all the requests and we report errors to different teams in the company together with logs that we take from Datadog.

Since I am an API developer, I do not use so much with the other features. Also, I have been in the company for only four months. I have only worked with monitors and alters.

I value tracing the request and being able to tell other teams which component, service, or line of code has an issue.

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The service catalog is very useful. We use this type of offering for our Microservices applications, and it gives a good view of flow. It is a must when we have different developers working on different services. Having the trace and log features have been useful in order to locate the microservice for the on-call person.

The dashboard is great. It is helpful to get a view of specific things that we need to monitor for our application. It has been a good way to watch specific things and add them together.

The application performance monitoring is an excellent aspect. This module had a few functionalities that we needed for the application health check. This needs to have some more features to consolidate the view into one tree, however.

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Datadog's ability to group and visualize the servers and the data makes it relatively easy for the root cause analysis.

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The tags are quite useful. They are providing the capability to give meaning to on-premises hardware (since it was not possible outside of cloud solutions and containers) as well to tag traces and logs. 

The full stack of integrations made it easier to monitor the different technologies and platform providers, including Software as a Service providers, that otherwise would need a lot of work and customization to be able to see what is happening. We'd also need to use several other separate tools that would require an increase in the required staff to operate them. Datadog gave us the opportunity to have a single platform for observability.

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The most valuable aspect is the APM which can monitor the metrics and latencies. There's a low error rate, and any alerts can be tagged to the service requests and sent via email to the required DLs. 

We can create incidents as well in our internal tools, like SNOW and Netcool.

The monitoring enables different dimensions of metrics to monitor the services and infrastructure. 

We have cloud infrastructure monitoring in Kubernetes nodes, pods containers, and ingress metrics.

Alerts are sent to an email in case of any issues. The metrics are used to create alerts.

The solution offers good dashboards, service maps, traces and flame graphs, HTTP status codes, power packs, service catalogs, and profiling.

While the logs module is not activated, we are using all other modules.

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The most valuable features I have found are network monitoring, testing, and integration tools. The visibility into our network has allowed for quick diagnosis of failures, identification of underutilized or over-utilized resources, and allowed for cloud cost optimization opportunities. The ability to correlate metrics has proven useful in determining downstream or upstream issues influencing the device, machine, or database having issues.

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The observability on offer is the most useful aspect of the product.

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The UI, basically, is the most valuable aspect of the solution. I really like the look and feel of the solution. It's not very distinctive now since other players have caught up, however, they were the first in the market to present such an effective UI. 

The many dozens of integrations that the solution brings out of the box are excellent.

It's easy to set up.

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The most useful aspects of the solution include RUM, session replay, and APM.

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The ease of use is the primary aspect. I have used, at previous jobs, the ELK stack and Splunk for log management. Both of them were useful, yet required a lot of manual effort to get set up (and a lot of continuing effort to tweak. A simple monitoring solution turned into a full-time job! However, Datadog has so far been a breeze to use and set up. It looks at what I am sending it and figures out what it is almost by magic. Even the manual configuration makes sense and gives very fast and thorough results

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The most valuable aspects of the solution include: 

CI visibility, which helps us in making sure our CI systems are running efficiently and are not blocking our developers from releasing new software and fixing bugs.

Logs, which help us in debugging issues where we can search for logs and can make sure they are relevant to the issues we are looking at.

APM, which can help us to stay on top of our applications by giving us the confidence that our apps are running.

Monitoring. We use monitoring a lot to ensure we know about potential issues and fix them before they affect our customers.

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The monitoring feature has been the most valuable.

I really like the dashboard. Monitoring has a straightforward tie-in to business value at my company (i.e. declaring incidents, etc). Things like having a dashboard and APM make my job easier. That said DevX is a little bit of a harder sell to executives in my company.

The dashboard feature makes it so easy to inspect multiple metrics at once across services. It's truly been a lifesaver when I'm personally trying to understand why performance degradation is happening.

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The observability pipelines are the most valuable aspect of the solution. 

Platform monitoring for the services that are deployed in AWS is helpful. It gives a better way to monitor the services. With Datadog, we ensure observability and maintain uninterrupted customer service. 

We can host the data pipelines between the cloud and the on-prem. Issues are easily reported.

The data streams are good. Data lineage is something that really helped in ensuring tracking of the data and metrics and also the volumes processed.

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We use structure logging a lot to triage production issues. The querying, attributes and tags manipulation, and customization have been very helpful in isolating and filtering environments. The integration with Winston logger has also been a breeze.

First and foremost, was that structured logging, tags, and attributes have not only allowed us to narrow down to a problem quickly in production, they have also let us create dashboards from these logs to understand more user behaviors, such as how many users stop and leave our application before an upload has completed. That helps us understand how important processing time is to a user.

We also intend to use distributed tracing more to understand where the error has occurred in a particular request.

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The dashboards are great. They are an easy way to give visibility into what we need to watch with others who are not SMEs.

I enjoy the custom metrics. With this, we can take things that were once logs and then retain them longer.

We are able to parse logs. To be honest, this was only useful due to the fact that we had not yet set up the Datadog agent properly in PHP. Once we did this, the Datadog log parsing was no longer needed.

The ability to pin to a date and time is very helpful. This allows us to pinpoint exactly what was happening.

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The infrastructure is the most valuable. It has a high-level insight into the infrastructure model of the application and provides important detailed data on the host and metrics, which is the main concern of our customers. It provides confirmation that the layer where the application is running is monitored and will be alerted when it is down and not functional. The customers can have ease of mind knowing their metrics are accurately being measured. The value of data provided, including service name, logs, and all other pertinent details tied to the host, makes it a valuable source of data

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I primarily prefer to utilize the profiling and tracing feature. It can potentially be used as a more-informed alternative to logs.

Beyond that, anything I've wanted to do, I found a way to get it done through Datadog. It allows for testing, logging, hardware monitoring, system performance, memory consumption, advanced observability, AI assistance, cross-team collaboration, and business analytics. Datadog helps some of the world’s biggest brands transform faster with the help of true AIOps, AI-assisted answers, UX and business analytics, cloud observability, and smart AI assistance.

It's all supporting my desire to build a great application, and in a centralized SaaS application, it's hard to say anything can beat it.

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I find the greatest feature is being able to search across logs from various microservices. As a member of the security team, I find that I often need visibility into other teams' services in order to get a good picture of our security posture.

I also am a fan of the ability to easily create monitors and get alerts into Slack quickly, without too much overhead. For example, I often need to create monitors where I am not too sure where the baseline lies. Having the ability to create anomaly monitors makes this process much more straightforward. Anomaly monitors are great for a security team.

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The CCM, Workflows, Logs, APM, and RUM are all useful aspects of the solution. I resell all solutions in Datadog, so all features are important.

I'm a Datadog partner in Brazil, and I monitor all my applications with Datadog too. 

The solution works within all sectors, including, governmental, financial services, services in general, and telecom. 

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The most valuable aspects of the solution include:

• The charting application metrics
• help with the business, prioritization, software design, and infrastructure design.

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The dashboards have proven most helpful in ensuring that teams can track the performance of their apps. On a more practical scale, the alerts have proved invaluable for triaging and bringing services back online.

Being able to tie the alerts generated through Datadog monitors has allowed us to quickly and effectively respond to infrastructure and software issues that would have otherwise hamstrung the organization and prevented us from accomplishing our day-to-day tasks. This is naturally invaluable.

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The network map is useful. With it, we have the ability to see the data flow across the entire network path across all the applications is highly valuable as the data from this service helps identify network bottlenecks, non-performant applications, and bad endpoints.

This is especially crucial for a high-availability website aimed at market data applications where low latency is crucial.

The host map gives a clear picture of the entire infrastructure, and the ability to switch between logs, metrics, and traces is very handy when it comes to debugging issues on the fly.

I love the ability to install the integrations and agents quickly. This is a well-made product.

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The feature I have found to be the most valuable is the filtering feature in logs. It is really easy to type plus and minus to filter out different logs. I use it to navigate the noise. 

I use synthetic tests as well. It is easy to navigate the menu and create tests. 

Much of the UI is very straightforward, and I do appreciate the ability to search for any documentation on the various features when I need to as well. The DASH monitoring boards are nice to give an overview of various performances and allow us to track use cases.

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The product is very useful for tracking down anything that’s gone wrong. I’ve been using it to make sure everything is working correctly after deployment and to make sure we don’t suffer performance degradation. We've found it great for tracking down anything that’s gone wrong in real-time.  

The logs are helpful. They are necessary for any application. Prior to this, our solution would have been to SSH into a machine and tail log files. This, however, is untenable for many reasons, and one of the first things I wanted to change.

The RUM has been great. Seeing real users interacting with our website is quite helpful.

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Dashboards and their versatility are among the most valuable features. They allow us to have internal facing trackers of our application's issues, usages, and features. They also allow us to have a better understanding of how users react to new features, and to display more information to other teams or also clients through uptime SLOs, et cetera.

We also found the Synthetics Tests and especially the Browser Tests very helpful. It is a nicer way to create end-to-end tests in a more user-friendly way than through code. They are very valuable in saving time compared to code-based testing.

Documentation is also very clear and interesting.

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Correlation between logs and APM has been the most important feature that we've found in Datadog to date. Previous solutions around log collection or APM instrumentation were rather cumbersome to connect. We previously needed to use different solutions for each which were not connected and required complex queries and a lot of time investment by key employees.

The search and filtering capabilities are rather helpful as well. The aggregation of all currently available properties has been great. It's excellent that available options drop as filters are refined. This allows for a nuanced view of available data.

We intend on exploring other products at Datadog, so this list may expand.

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The most useful feature is the APM. Being able to quickly view which requests are time-consuming, and which calls have failed is invaluable. Being able to click on a UI and be pointed to the exact source of the problem is like magic. 

I'm also very intrigued by log management, although I haven't had quite a chance to use it very effectively. In particular, the trace and span IDs don't quite seem to work for me. However, I'm very keen on getting this to work. This will also help my developers to be more diligent and considerate when creating log data.

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The most valuable aspects include:

• The ability to monitor any team's metric in my company (transparency)
• The ability to create/clone dashboards for myself (ease of use)
• Its integration with Slack (it is very powerful)
• The ability to add monitors on any metric emitted by any team at my organization
• (Through Datadog APM) the ability to understand the reasons behind production issues. Its ability to navigate across services seamlessly in order to understand the time spent at each critical stage of a production request is key. This, combined with Datadog's historical ability to show business metrics aside, helped me get more powerful insights much more quickly.
• (Through integrations like Slack and PagerDuty) the ability to receive alerts right to the most common notification method we use (our mobile devices and Slack), which saves a lot of time and helps us maintain focus. 

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Datadog has a lot of features to be able to drill down deep into the swath of logs that our platforms generate. 

It has a lot of ability to make fancy and deep searches using regular expressions and to graph them into useful and interesting dashboard graphs. 

The plethora of built-in/downloadable integrations make it much easier to set up for our platforms. Otherwise, we'd have to parse the log files ourselves, which would take a great deal of effort. Had to do it before when had to use an ELK stack for logging, which was painful.

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Distributed tracing is the most valuable feature. We have hundreds of microservices, and knowing how top-level requests weave throughout all of them is invaluable. 

At one glance, we can clearly see which service is slow and then switch over to the infrastructure view or container view to debug why the slowness is happening. This is true of all their other integrated products as well; the more you add, the more insights you get when looking at traces.

We also use RUM extensively. This helps us cover the last mile of application performance. Without it, we wouldn't know if our browser applications were functioning slowly for our users.

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The ease of setting up metrics and alerting and integrating with Slack has significantly reduced the friction of keeping the team up to date on the platform's health. Before creating custom Cloudwatch metrics was never very intuitive, and also it was non-trivial to set up integrations with other services we use, especially Slack. 

It also provides a good way to gain the context needed when trying to fix issues, as it's a central place to look through logs, requests, AWS metrics, and more - overall contributing to the health of our platform.

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I appreciate that they are constantly adding new features, some of which we haven't yet had a chance to implement. 

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Metric graphing and Dashboards are the most valuable features because they give us good observability into our system and work well to alert us when interesting things happen. We use this functionality daily.

We value the monitoring capability since it allows us to be pushed alerts, rather than have to observe graphs continually. The integrations with Slack and PagerDuty enable us to be interrupted appropriately and keep a running tab on the system without bothering us unnecessarily.

The online process monitoring has been extremely helpful, as it gives engineers the ability to see the live status of all the processes running our systems without them having to log in.

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Their interface is probably one of the easiest things to use because it lets non-developers and non-engineers quickly get access to metrics and pull business value out of them. We could put together dashboards and give it to people who are non-technical, then they can see the state of the world. 

They have a very good ecosystem for their integrations. They have a lot of different integrations, and we use a lot of them. We have integrations with Amazon for ECS, RDS, and all of the subsystems of Amazon. We also have Docker and Splunk integrations. The integrations are great because they're definitely vetted and not third-party integrations. They're part of the Datadog ecosystem and seamless.

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Profiling provides really good insights, and APM has really good tracing visibility. 

The SLA and SLO definitions and the monitoring are also really important and very valuable parts of the product and make great Datadog features. 

Datadog support is also really valuable as they provide support for the product through the chat as well. 

The Datadog premium support has helped us to provide faster outcomes for a problem. 

Also, rather than having an email thread, it would be better to get the support on call and sort out the issue, which is the support we get from Datadog CSM.

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We are providing managed services to our customers across multiple industries. 

Datadog delivers observability, monitoring, and alerting capabilities we need to operate at scale. 

Operating custom cloud native workloads as well as ISV products such as Atlassian Jira or Confluence is also something we do. Integrating Synthetics, infrastructure, and application performance monitoring, as well as piping all logs through Datadog allows us to operate while grabbing alerts in real-time.

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The APM and container monitoring are excellent aspects of the solution.

It helps with monitoring users and what they access.

We can identify security loopholes and attack patterns while quickly responding to issues.

Our team can now identify pushbacks and get insights into application components. We can gather data for reporting and post-mortem, and we can track fixes and test coverage. Datadog allows us to gain confidence in the fix/improvement.

I've been able to present data to the team/ management based on the team's dashboards.

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My favorite feature is creating dashboards as that empowers me to sleep calmly at night and not to keep watch on critical system metrics. Be it DB metrics or computer-related metrics, it's always easy to view them. 

The ease of correcting these dashboards and widgets when needed is amazing. 

The only issue I face is when more than one person editing these dashboards simultaneously, one or the other person sometimes loses his/her work. That said,  they will resolve that soon. With the variety of widgets, it's so easy to plot the data in a timely manner, and that makes monitoring a lot easier.

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The APM is very useful for tracking down anything that’s gone wrong. I’ve been using it to make sure everything is working correctly after deployment, to make sure we don’t suffer performance degradation, and to track down anything that’s gone wrong in real-time  

The logs are great and necessary for any application. Before this, our solution would have been to SSH into a machine and tail log files. This is untenable for many reasons, and one of the first things I wanted to change.

The RUM is useful for seeing how real users interact with our website.

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We started with Log analytics in the beginning stages of our monitoring journey. Those were very insightful, but obviously only as useful as we made them with good logging practices.

The dashboards we created are core indicators of the health of our system, and it is one of the most reliable sources we have turned to, especially as we have seen APM metrics impacted several times lately. We can usually rely on logs to tell us what the apps are doing.

APM and Traces have been crucial to understanding how users are actually using the app. That drives a lot of our decisions around refactoring and focusing our limited engineering resources.

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The most valuable feature is the dashboards that are provided out of the box, as well as ones we were able to configure. Specific Dashboards that were provided that made things easier were EC2, RDS, Kubernetes dashboards.

We also use the logging tool, which makes searching for specific error logs easier to do.

Datadog Logging provides the capability for us to use AWS logs such as VPC Flow Logs, ELB, EC2, RDS, and other logs that provide lots of relevant operational data but are not actionable. Datadog provides a tool that can provide us analytics and insights that are actionable for visualizations, dashboards, alerting, and intuitive searching.

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The feature I have found most valuable is when I can reuse existing monitors and alerts for new dashboards. 

It is nice that things are so integrated, and any individual thing I build out can likely be re-used across the suite.

Other features I have found useful include the stack traces, especially when it links back to something specific I can look for in the code base. I also like that you can group similar alerts together to look for trends over time instead of one in isolation and see if there have been regressions.

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The CPU profiler has been interesting even though it isn't our core use case. 

We are finding that Datadog has way more offerings than originally expected, so we are constantly finding new parts of it that would be convincing to use. 

The log and ingestion are very similar to our current Elasticsearch setup. We find the tracing and overall integration/ecosystem to be the most valuable part. Basically, the CPU profiler is a good example of a value add for a problem we knew we had yet was low priority and had hacky workarounds. The value proposition is in the ecosystem as a whole.

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The biggest thing I liked was the combination of all the things - monitoring, log aggregation, and profiling. We have different tools for each of them in our organization and all of them are open-source. These are not very well maintained and there is no customer support. 

Having an industry-standard product is ideal for us as we are short on manpower. Profiling is another amazing feature. Currently, we rely on some open-source solutions, and it's all done locally. Having it done on Kubernetes would give us more insights and help with performance. Alerting is again a nightmare for us. Datadog solves all of these issues.

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The ability to easily drill down into log queries quickly and efficiently has helped us to resolve several critical incidents. We heavily rely on dashboards that are showing us various queues and load on CPU and memory for servers. 

We also have a view of the information required when we begin the patch and/or upgrade processes. 

I've arranged several monitors to alert the Site Reliability Engineering team when various metrics show a server that might be reaching capacity. We use it to send an email suggesting we increase the size of the cloud instance.

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The tools I have found useful include the Datadog cloud security posture manager and cloud workload security.

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The enterprise version of a CMS platform enables businesses to transmit content to their customers. 

The tool is fully customizable to the end user, including out-of-the-box integrations as well as APIs for custom plugin support. 

The content is fully managed within our systems using AWS as the back-end cloud provider. Assets are kept in secure buckets, and we can utilize the Kubernetes infrastructure to deliver our product to end users and internal authors. 

Using the CMS allows for business people to manage content without needing development efforts. Allowing this type of setup for business users has been a key reason for our success.

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The K8 monitoring is extremely useful in Datadog. Preset dashboards that it provides help to speed up the work. 

The metrics summary is useful. Tracing with a span breakdown is helpful for us. We like the dashboarding with power packs and logging correlation with traces and logs. 

The Flame graph for tracing helps determine where the latency is the highest. 

Dashboards are created as a standard set and then exported into other Datadog instances for other teams. 

These dashboards would be updated regularly and pushed out to the teams. Unfortunately, there is no way to automatically push or deploy code in a quicker way. Each team I work with has its own Datadog instance.

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The solution's real-time user management and session analytics as well as the APM, are the solution's most valuable aspects.

We could easily identify the production box, and we could handle alerts.

The initial setup is very straightforward. 

It's stable. 

They have flag integrations, mail integrations, and other options as well.

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• It is very easy to use.
• It is easy to configure.
• It has a nice UI.
• Datadog provides everything that we need.

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The management of SLOs and their related burn-rate monitors have allowed us to onboard teams to on-call fast. 

Management of resources using infrastructure-as-code has been a recent game-changer for us. Combining the two has allowed us to provide product teams with a total solution for getting their applications attached to user-focused alerting and monitoring within a matter of days rather than months - and has clearly impacted our ability to discover and respond to significant production incidents.

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Datadog is providing efficiency in the products we develop for the wireless device engineering department. We had to provide more developer integration tools and also needed to help in creating easy queries that would help in creating efficient toolsets for management to make decisions based on these metrics.

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We're investigating many modules. We collect all data logs from all operating systems (Windows, Linux, VMware, and bare metal data centers). We also automatize the installation of the agent on servers. 

We're doing POCs in APM and security. 

Soon, we'll have a unique portal for observability. This will make troubleshooting easy at levels 1 and 2. 

The most valuable aspect for us is to have everything in the same place.

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Monitoring has been invaluable, and as we start to look to other products, bringing in logs and APM traces will create a full picture of what we need to do to resolve incidents. We're also interested in our users' perspectives and when things are slowing down for them.

Additionally, we are interested in the workflows announced today as an actionable decision tree that will allow our teams to see what is wrong and know what to do based on the connected runbook/notebook. I feel that this is the final piece that will make DataDog so much more valuable than its competition.

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The infrastructure monitoring capabilities are really valuable. You can just log on and see everything that is happening within an IT environment.

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Seeing log trends and patterns and aggregate search was a huge first step for us. We then began using other features of the Datadog platform by enabling APM. After that, we did other integrations.

For us to have visibility into our app stack and the hardware we run has been highly beneficial.

We leverage APM, log management, and at least ten other integrations. Our DB, web servers, network, storage, and other areas are now monitored and hooked up to dashboards.

Dashboarding has also proven useful when information is going to be viewed by anyone in the organization.

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As I mentioned above, we use Datadog quite extensively. In my day-to-day role, I primarily work with APM traces and logs to debug issues and unblock myself. 

I have found the traces and spans most useful in providing details about why certain services are performing poorly.

Additionally, the flexibility to create notebooks and dashboards and fully customize them gives us a lot of power to track the exact services and endpoints we are working on.

Furthermore, we are also using monitoring to page us if things break, and the Slack integration provides us instantaneous feedback on how things are performing.

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The most valuable feature I have found is the elastic container service. That is where our apps get deployed. This feature is really important to us since, without it, we would not be able to deploy our apps the way we wanted to and would have to use a different vendor. 

This service is also valuable to our team as we have a lot of familiarity with it and adapting to the services from our previous one was fairly seamless. 

Another feature that is very valuable for our team is the IAM. It allows us to control security.

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The most valuable feature is the custom events to track specific cases.

Monitoring time spent on views and events can be triggered. For example, for one of our products, we have created a custom dashboard that lets us track all the custom events and multiple entry points in the same part of the application.

Knowing the entry point helps us decide on improvements. We can collect important data about the overall usage of each module within our application. 

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APM and tracing are super useful. We use it for daily monitoring of CPU and memory. We can get alerts to tail to specific metrics.

We also find the tracing feature useful. We often run into bugs, and when a production issue happens, it is super useful to see the related services and sense where the problem is.

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Because of our client focus, it is easy for us to sell. This is because it is easy to use and easy to set up.

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The visibility that it provides is valuable. It is helping in being proactive around incident management. It is helping us to be able to get more visibility into our customers' applications so that we can assist them at the application layer. We also provide them the infrastructure from an AWS standpoint. We are able to make sure that our customers are aware of certain critical things around the analytical piece of either the network or the application. We're able to call customers before they even know about the issue. From there, we can start putting together some change management processes and help them a bit.

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One of the solution's greatest aspects is its overall simplicity. It is very easy to use. It's easier to handle than other brands we have access to.

The monitoring capabilities that the solution provides are very good.

We find they have a very helpful alert system.

The product has been very stable. We've liked the performance provided.

The initial setup is simple and easy to handle. It's not hard at all.

The experience we've had with technical support has been very positive so far. They are helpful.

The integration capabilities have been pretty good overall. We have no complaints.

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The most valuable features are logging, the extensive set of integrations, and easy jumpstart.

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The most valuable aspect of the solution is the APM.

The logging capabilities are quite useful. 

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The most valuable features are the graphs, dashboards, metrics, and the interface.

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Dashboards have been marrying the most valuable parts of Datadog. Dashboards use metrics that are very helpful for monitoring services. I recently used metrics to monitor the number of pods in Kubernetes, the spikes in requests in Kubernetes, and overall CPU and memory usage in our Kubernetes clusters. 

We can also use log analytics to further our understanding. We can handle debugging and find out why things are breaking in our applications. 

The log portion of Datadog has robust features to debug the applications we are running. I really appreciate the ability to use facets to par down the logs.

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The valuable features include the following: 

• We are currently utilizing a decentralized distributed framework for our deployment, including our monitoring/logging observability capabilities. Centralizing them, if contingent on our company privacy guidelines, will be a big help in tracking and responding to issues that come up and have the means to understand the origin of the log management tools that were demonstrated.
• The ability to fiddle around and manipulate how logs are outputted.
• The ability to track AWS Lambda functions, Cloudformation, and Cloudwatch allow someone that is not savvy to dip their toe into understanding their own product.
• The integration into AWS is key as well as our software is currently bound to AWS.

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We use metrics to track the metrics of our application. We use logging to log any errors or erroneous application behavior as well as successful behavior. We use events to log successful steps in our pipeline or failed steps in our deployment. 

We use a combination of all these features to diagnose bugs. It makes it much more efficient to look at all the data in one place. This speeds up our development speed so that we can be agile.

These features are the features that I use the most since it is incredibly difficult to track down intermittent bugs if I were to look directly under the hood in a CLI.

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I really like the replay, the ability to replay sessions, as I'm in sales engineering, so I sometimes need to know what my prospects are doing during a proof of value. I can actually see all the mouse moving and clicking on buttons and stuff like that. I can actually tell what they've been doing. There’s a lot of the other monitoring stuff as well. The development team uses it for monitoring and finds it very helpful.

It’s been kind of in the middle of many different things. The dashboards and the performance of the software have been great.

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They have a very good foundation in capturing metrics, logs, and traces. It's a very nice tool for that and it allows you to apply these monitoring tools in almost any technology.

Even if you have several layers, containers, EC2 instances, build machines or whatever you need in your infrastructure, Datadog can integrate with all of them across multiple cloud providers. It's a great product.

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It is incredibly easy to do common monitoring actions:

• Excellent autocomplete for everything in the UI.
• Using tags is very intuitive (in contrast to the cumbersome regex-like based querying in Graphite).
• Going from viewing a metric to creating a monitor alerting on a metric is very easy. This is very important as the easier it is to create monitors, the more monitors will be created by people. With Graphite and Sensu, the effort required to create and test a monitor was so great that we had only a handful of monitors. We now have over 300 monitors.

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We enjoy the multistep API tests.

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The solution is useful for ingesting data from various sources. This helps to monitor the log metrics of the system. It has alert mechanisms that can be enabled to notify the teams if something goes wrong.

Datadog offers good integration to different services. It provides for easy access and management.

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RUM with session replay combined with a future use case to support synthetics will help to identify issues earlier in our process. We have not rolled this out yet but plan for it as a future use case for our customer support process. This, combined with integrated automation for incident management, will drive down our MTTR and time spent working through tickets. Overall, we are hoping to use this to look at our data and perfection rate over time in a BI-like way to reduce our customer support headcount by saving on time spent.

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We really like the charts and visualization.

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Datadog has a very good visualization for my complete infrastructure and network traffic, which enabled me to create a capacity plan.

This product is great because it shows you the SQL and your application request in a single view.

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The fact that everything is under a single pane of glass is really valuable, as developers don't have to spend their time copying correlation IDs across tools to find what they need.

Thanks to the unified tagging system, it's really easy to jump around the different Datadog products without losing the context. That makes debugging really easy for developers because they can go from APM to logs to metrics in a few clicks.

Watchdog is also a great feature that helped us identify overlooked issues more than once.

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With respect to logs, we used to integrate various kinds of tools to achieve very basic tasks and it always felt like a very fragile solution. I think logs are by far the most useful feature and at the same time, the one that we could improve.

APM - This is either a hit or miss, allow me to explain: we use various programming languages, mainly PHP and Ruby, and the traces generated don't always provide all of the information we want. For example, we get a great level of detail for the SQL queries that the app generates but not so much for the PHP side. It's hard to track where exactly where all of the bottlenecks are, so some analysis tools for APM could make a good addition.

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The most useful aspects of the product are the dashboards and APM/tracing. The tools are powerful and intuitive to set up as well.

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The solution has valuable troubleshooting and instrumentation features. 

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I have found error reporting and log centralization the most valuable features. Overall, Datadog provides a full package solution.

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The Host Map, Live Process provides performance metrics of our application. The support team likes using Datadog for identifying resources affected and obtaining the logs. 

Monitors are easy and quick to setup. Metrics are easily accessible and quick to use. The ability to send notifications based on metadata from the monitor is helpful. The setup for monitors is one time and it works for all workloads, whether it is Azure or any other cloud.

Logs rehydration helps us archive and rehydrate logs as we need. We don't need logs to be indexed at all times. Logs are required only for escalations and rehydrating does the job and provides cost savings.

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Its most valuable feature is the monitoring, such as all the custom metrics that Datadog imports from AWS. In addition, the specific monitoring where you can set up an alert to a bunch of different services. 

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The most valuable features have been: Sharable dashboards, TimeBoards, dogstatsd API, Slack Integration, Event logging API. CloudTrail Events, Tags, alerts, and anomaly detection. EBS Volume Snapshot Age, which they added upon request. We used PagerDuty integration for a while as well.

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The APM and K8s monitoring are the most valuable aspects of the solution. The K8s monitoring allows all customers to view their infra, even if they do not use K8s daily. They can just click on a few tabs to get all of the information they need. 

It is also very easy to install on our system. APM has helped debug applications on our system as well. We were able to view why a service has suddenly shut down.

We also use Datadog for SLOs/SLAs as well. We check the live endpoint of services to ensure they are still up and running.

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The performance of Datadog is good.

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I like Datadog because it is so easy use, I can easily connect to public clouds, and I have a view of observability. In my opinion it's the best tool in the market.

I like the interface so much. The interface and the integrations make it so easy to connect to the cloud or to the on-premise environment. It has so many monitors for alerting. I like all the versions and especially the APM model, on which I did a lot more testing with my applications. I have made applications for testing the tool that is in the resource. The APM is the goal for me in the Datadog.

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I have found some of the most valuable features to be the way things all come together that gives us a point of view that is useful. The panel is very beautiful and customizable. The configuration file was really helpful, we were able to configure the solution in the way we needed. The portability of this solution is more flexible than other competitors, allowing us to change cloud services when we want. We are not locked into a particular one.

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The solution is useful for monitoring logs. 

We can create an automation for implementing across all our systems. Right now, we are deploying it in our core systems.

The greatest value is integrated monitoring as it is able to quickly detect issues and improve our time to market.

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The solution's SaaS model is easy to manage and works well in single- or multi-cloud environments. 

The Saas format can handle heterogeneous or multi-language applications much better than using a combination of tools. 

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APM is great and has provided low-effort out-of-the-box observability for various services. 

Monitors are helpful, and definitions are simple. 

Terraform support is nice as it allows us to create homogenous monitoring environments in various deployment environments with little additional effort. It also facilitates version control of monitor definitions, etc. 

The Golang profiler is generally good with the exception of delta profiles; it has provided helpful observability into Heap Allocations which has helped us reduce GC overhead.

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Understanding better the health of applications, modern environments, and many other solutions, is the main focus of Datadog and many other monitoring tools.

With Datadog I can look at the health of the technology stack and services. I can also integrate multiple metric sources, security, business data, and much more. It's great for centralizing data and unifying monitoring silos. 

Datadog is a hub, not just a monitoring software. The biggest value of Datadog is looking at the big picture, not only one part of it.

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Its integration definitely stands out. It provides seamless monitoring of all our systems, services, apps, and whatever else we secure and monitor. 

Visualizations have become simpler with dashboards. We are getting visibility into systems, services, and apps stack through a single pane of glass, which is good. We are able to put logs in context.

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Datadog dashboards are pretty great.

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In terms of the public cloud provider integration of AWS, I would say it's very easy and straightforward to integrate. We can automate that way as well, because it also provides the cloud formation template and is a way to have a central place to monitor and visualize metrics in a multi-account structure. It's something we really need because the company has many AWS accounts. Rather than jumping from one account to another, Datadog gives us the functionality of having everything on one platform, in one place.

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The most valuable features of the solution include the APM, log monitor, SQL monitors, network monitors, and integrations.

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The dashboards are great. They allow for the visualization of log data over time to detect anomalous behavior.

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Its integration is most valuable because you can integrate it with various service providers such as AWS, .Net, etc.

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Its logs are most valuable.

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It is great that creating an incident is possible from Slack while having all the relevant data in Datadog. It allows us to not think too much about organizing incidents, which is a highly stressful situation.

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The application performance monitoring is pretty good. 

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It is a good one stop location where we keep all our data for our infrastructure, and it's also easier to navigate between different things.

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Sometimes it's more user friendly for development teams. There are some parts of Datadog that are more understandable for development teams. For example, the APM in Datadog works more manually and works like the tools in New Relic or Grafana or Elastic. It is easier to understand for software development teams. 

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The most valuable feature of Datadog is its logs. We can use the logs to conduct analysis and root cause analysis.

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It's hosted. We don't have to do it, and they handle a large amount of data with backups and all of the other things that we no longer have to manage. 

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The ability to create dashboards and matrices with graphs. This information is useful to us.

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I have found the logging and tracing features the most valuable.

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The fact that its hosted so you don't have to rely on infrastructure to do the work is a very valuable feature. View full review »

I don't have to worry about upgrades with the AWS version.

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