We are using Datadog to improve our monitoring and observability so we can hopefully improve our customer experience and reliability.

I have been using Datadog to build better actionable alerts to help teams across the enterprise. Also by using Datadog we are hoping to have improved observability into our apps and we are also taking advantage of this process to improve our tagging strategy so teams can hopefully troubleshoot incidents faster and a much reduced mean time to resolve.

We have a lot of different resources we use like Kubernetes, App Gateway and Cosmos DB just to name a few.

As soon as we started implementing Datadog into our cloud environment people really like how it looked and how easy it was to navigate. We could see the most data in our Kubernetes environments than we ever could.

Some people liked how the logs were color coded so it was easy to see what kind of log you were looking at. The ease of making dashboards has also been greatly received as a benefit.

People have commented that there is so much information that it takes a time to digest and get used to what you are looking at and finding what you are looking for.

The selection of monitors is a big feature I have been working with. Previously with Azure Monitor we couldn't do a whole lot with their alerts. The log alerts can sometimes take a while to ingest. Also, we couldn't do any math with the metrics we received from logs to make better alerts from logs.

The metric alerts are ok but are still very limited. With Datadog, we can make a wide range of different monitors that we can tweak in real time because there is a graph of data as you are creating the alert which is very beneficial. The ease of making dashboards has saved a lot of people a lot of time. No KQL queries to put together the information you are looking for and the ability to pin any info you see into a dashboard is very convenient.

RUM is another feature we are looking forward to using this upcoming tax season, as we will have a front-row view into what frustrates customers or where things go wrong in their process of using our site.

The PagerDuty integration could be a little bit better. If there was a way to format the monitors to different incident management software that would be awesome. As of right now, it takes a lot of manipulating of PagerDuty to get the monitors from Datadog to populate all the fields we want in PagerDuty.

I love the fact you can query data without using something like KQL. However, it would also be helpful if there was a way to convert a complex KQL query into Datadog to be able to retrieve the same data - especially for very specific scenarios that some app teams may want to look for.

I've used the solution for about two years.

We previously used Azure Monitor, App Insights, and Log Analytics. We switched because it was a lot for developers and SREs to switch between three screens to try troubleshoot and when you add in the slow load times from Azure it can take a while to get things done.

I would advise taking a close look at logging costs, man-hours needed, and the amount of time it takes for people to get comfortable navigating Datadog because there is so much information that it can be overwhelming to narrow down what you need.

We did evaluate DynaTrace and looked into New Relic before settling on Datadog.

We use Datadog as our main log ingestion source, and Datadog is one of the first places we go to for analyzing logs.

This is especially true for cases of debugging, monitoring, and alerting on errors and incidents, as we use traffic logs from K8s, Amazon Web Services, and many other services at our company to Datadog. In addition, many products and teams at our company have dashboards for monitoring statistics (sometimes based on these logs directly, other times we set queries for these metrics) to alert us if there are any errors or health issues.

Overall, at my company, Datadog has made it easy to search for and look up logs at an impressively quick search rate over a large amount of logs.

It seamlessly allows you to set up monitoring and alerting directly from log queries which is convenient and helps for a good user experience, and while there is a bit of a learning curve, given enough time a majority of my company now uses Datadog as the first place to check when there are errors or bugs.

However, the cost aspect of Datadog is tricky to gauge because it's related to usage, and thus, it is hard to tell the relative value of Datadog year to year.

The feature I've found most valuable is the log search feature. It's set up with our ingestion to be a quick one-stop shop, is reliable and quick, and seamlessly integrates into building custom monitors and alerts based on log volume and timeframes.

As a result, it's easy to leverage this to triage bugs and errors, since we can pinpoint the logs around the time that they occur and get metadata/context around the issue. This is the main feature that I use the most in my workflow with Datadog to help debug and triage issues.

More helpful log search keywords/tips would be helpful in improving Datadog's log dashboard. I recently struggled a lot to parse text from raw line logs that didn't seem to match directly with facets. There should be smart searching capabilities. However, it's not intuitive to learn how to leverage them, and instead had to resort to a Python script to do some simple regex parsing (I was trying to parse "file:folder/*/*" from the logs and yet didn't seem to be able to do this in Datadog, maybe I'm just not familiar enough with the logs but didn't seem to easily find resources on how to do this either).

I've used the solution for 10 months.

Beware that the cost will fluctuate (and it often only gets more expensive very quickly).

The primary use case for this solution is to enhance our monitoring visibility, determine the root cause of incidents, understand end-user behaviour from their point of view (RUM), and understand application performance.

Our technical environment consists of a local dev env where Datadog is not enabled, we have deployed environments that range from UAT testing with our product org to ephemeral stacks that our developers use to test there code not on there computer. We also have a mobile app where testing is also performed.

Datadog has greatly improved our organization in many ways. Some of those ways include greater visibility into application performance, understanding of end-user behavior, and a single pane of glass view into our entire infrastructure.

Regarding visibility, our organization previously used New Relic, and when incidents or regressions happened, New Relic's query language was very hard to use. End-user behavior in RUM has improved our ability to know what to focus on. Lastly, the single pane of glass view with maneuvering between products has helped us truly understand root causes after incidents.

APM has been a top feature for us. I can speak for all developers here: they use it more often than other products. Due to a standard in tracing (even though it is customizable), engineers find it easier to walk a trace than to understand what went wrong when looking at logging.

Another feature that I find valuable, though it isn't the first one that comes to mind, is Watchdog. I have found that has been a good source of understanding anomalies and where maybe we (as an organization) need more monitoring coverage.

I am not 100% sure how this is done or if it can be though I've had a lot of education I've had to do to ramp developers up on the platform. This feels like the nature of just the sheer growth and number of products Datadog now offers.

When I first started using the Datadog platform, I thought that was a big pro of the company that the ramp-up time was much quicker, not having to learn a query language. I still believe that to be true when comparing the product to someone like New Relic though with the wide range of products Datadog now offers it can be a bit intimidating to developers to know where to go to find what they want.

I have been using the solution at my current company for almost four years, and have used it at my previous company as well.

A while ago, we used New Relic, and we switched due to Datadog being a better product.

We did the implementation in-house.

The value compared to pricing is reasonable, though it can be a bit of a sticker shock to some.

We did not evaluate other options.

We are using Datadog to improve our cloud monitoring and observability across our enterprise apps. We have integrated a lot of different resources into Datadog, like Kubernetes, App Gateways, App Service Environments, App Service Plans, and other Web App resources.

I will be using the monitoring and observability features of Datadog. Dashboards are used very heavily by teams and SREs. We really have seen that Datadog has already improved both our monitoring and our observability.

The ease and speed of which you can create a dashboard has been a huge improvement.

The different types of monitors we can create have been huge, too. We can do so many different things with monitors that we couldn't do before with our alerts.

Being able to click on a trace or log and drill down on it to see what happened has been great.

Some have found the learning curve a bit steep. That said,they are coming around slowly. There is just a lot of information to learn how to navigate.

The different types of monitors have been very valuable. We have been able to make our alerts (monitors) more actionable than we were able to previously.

Watchdog is a favorite feature among a lot of the devs. It catches things they didn't even know were an issue.

RUM is another feature a lot of us are looking forward to seeing how it can help us improve our customer experience during tax season.

We hope to enable the code review feature at some point to so we can see what code caused the issue.

I would like to see the integration between PagerDuty and Datadog improved. The tags in Datadog don't match those in PagerDuty, and we have to make it work. Also, I would like to see if the ability to replicate a KQL query in Datadog is made easier or better.

I would like to see the alert communications to email or phones made better so we could hopefully move off PagerDuty and just use Datadog for that.

There are also a lot of features that we haven't budgeted for yet and I would like for us to be able to use them in the future.

I've used the solution for about two years.

Our primary use case is custom and vendor-supplied web application log aggregation, performance tracing and alerting.

We run a mix of AWS EC2, Azure serverless, and colocated VMWare servers to support higher education web applications. Managing a hybrid multi-cloud solution across hundreds of applications is always a challenge.

Datadog agents on each web host, and native integrations with GitHub, AWS, and Azure gets all of our instrumentation and error data in one place for easy analysis and monitoring.

Through the use of Datadog across all of our apps, we were able to consolidate a number of alerting and error-tracking apps, and Datadog ties them all together in cohesive dashboards.

Whether the app is vendor-supplied or we built it ourselves, the depth of tracing, profiling, and hooking into logs is all obtainable and tunable. Both legacy .NET Framework and Windows Event Viewer and cutting-edge .NET Core with streaming logs all work. The breadth of coverage for any app type or situation is really incredible. It feels like there's nothing we can't monitor.

Centralized pipeline tracking and error logging provide a comprehensive view of our development and deployment processes, making it much easier to identify and resolve issues quickly.

Synthetic testing has been a game-changer, allowing us to catch potential problems before they impact real users. Real user monitoring gives us invaluable insights into actual user experiences, helping us prioritize improvements where they matter most.

The ability to create custom dashboards has been incredibly useful, allowing us to visualize key metrics and KPIs in a way that makes sense for different teams and stakeholders.

These features form a powerful toolkit that helps us maintain high performance and reliability across our applications and infrastructure, ultimately leading to better user satisfaction and more efficient operations.

I'd like to see an expansion of the Android and IOS apps to have a simplified CI/CD pipeline history view.

I like the idea of monitoring on the go, yet it seems the options are still a bit limited out of the box. While the documentation is very good considering all the frameworks and technology Datadog covers, there are areas - specifically .NET Profiling and Tracing of IIS-hosted apps - that need a lot of focus to pick up on the key details needed.

In some cases the screenshots don't match the text as updates are made. I spent longer than I should have figuring out how to correlate logs to traces, mostly related to environmental variables.

I've used the solution for about three years.

We have been impressed with the uptime and clean and light resource usage of the agents.

The solution has been very scalable and customizable.

Sales service is always helpful in tuning our committed costs and alerting us when we start spending outside the on-demand budget.

We used a mix of a custom error email system, SolarWinds, UptimeRobot, and GitHub actions. We switched to find one platform that could give deep app visibility regardless of whether it is Linux or Windows or Container, cloud or on-prem hosted.

Generally simple, but .NET Profiling of IIS and aligning logs to traces and profiles was a challenge.

We implemented the solution in-house.

I'd count our ROI as significant time saved by the development team assessing bugs and performance issues.

Set up live trials to asses cost scaling. Small decisions around how monitors are used can have big impacts on cost scaling.

NewRelic was considered. LogicMonitor was chosen over Datadog for our network and campus server management use cases.

Excited to dig further into the new offerings around LLM and continue to grow our footprint in Datadog.

The current use case for Datadog in our environment is observability. We use Datadog as the primary log ingestion and analysis point, along with consolidation of application/infrastructure metrics across cloud environments and realtime alerting to issues that arise in production.

Datadog integrates within all aspects of our infrastructure and applications to provide valuable insights into Containers, Serverless functions, Deep Logging Analysis, Virtualized Hardware and Cost Optimizations.

Datadog improved our observability layer by creating a consistent, centralized service for all of our varied cloud-based applications. All of our production and non-production environment applications and infrastructure send metrics directly to Datadog for analysis and determination of any issues that would need to be looked at by the Infrastructure, Platform and Development teams for quick remediation. Using Datadog as this centralized Observability platform has enabled us to become leaner without sacrificing project timelines when issues arise and require triage for efficient resolution.

All of Datadog's features have become valuable tools in our cloud environments.

Our primary alerts, based on metrics and synthetic transactions, are the most used and relied upon for decreased MTTA/MTTR across all of our platforms. This is followed by deep log analysis that enables us to quickly and easily get to a preliminary root cause that someone on the infrastructure, platform or development teams can take and focus their attention on the precise target that Datadog revealed as the issue to be remediated.

The two areas I could see needing improvement or a feature to add value are building a more robust SIM that would include container scanning to rival other such products on the market so we do not need to extend functionality to another third-party provider. The other expands the alerting functions by creating a new feature to add direct SMS notifications, on-call rotation scheduling, etc., that could replace the need to have this as an external third party solution integration.

I've been a Datadog user for almost ten years.

Datadog is very stable, and we've only come across a few items that needed to be addressed quickly when there were issues.

Scalability is very favorable, aside from cost/budget, which limits the scalability of this platform.

Both customer service and support need a little work, as we have had a number of requests/issues that were not addressed as we needed them to be.

Being an Observability SME, I have used many native and third party solutions, including Dynatrace, New Relic, CloudWatch and Zabbix. As previously mentioned, Datadog provides a superior platform for centralizing and consolidating our Observability layer. Switching to Datadog was a no-brainer when most other solutions either didn't provide the maturity of functions, or have them available, at all.

The initial setup was very straightforward, and the integrations were easily configured.

We implemented Datadog in-house.

For the most part, Datadog's ROI is quite impressive when you consider all of the features and functions that are centralized on the platform. It doesn't require us to purchase additional third-party solutions to fill in the gaps.

The setup was dead simple once the cloud integrations and agent components were identified and executed. Licensing falls into our normal third-party processes, so it was a familiar feeling when we started with Datadog. Cost is the only outlier when it comes to a perfect solution. Datadog is expensive, and each add-on drives that cost further into the realm of requiring justifications to finance expanding the core suite of features we would like to enable.

Yes, we evaluated several competing platforms that included Dynatrace, New Relic and Zabbix.

They should provide more inclusive pricing, or an "all you can eat" tier that would include all relevant features, as opposed to individual cost increases to let Datadog to become more valuable and replace even more third-party solutions that have a lower cost of entry.

Our primary use case for Datadog is to monitor and manage our fully cloud-native infrastructure. We utilize DataDog to gain real-time visibility into our cloud environments, ensuring that all our services are running smoothly and efficiently.

The platform’s extensive integration capabilities allow us to seamlessly track performance metrics across various cloud services, containers, and microservices.

With Datadog’s robust alerting and visualization tools, we can proactively identify and resolve issues, minimizing downtime and optimizing our system’s performance. This has been crucial in maintaining the reliability and scalability of our cloud-native applications.

Datadog has significantly enhanced our organization’s operational efficiency and reliability. By providing real-time visibility into our cloud-native infrastructure, Datadog enables us to monitor performance metrics, detect anomalies, and resolve issues swiftly.

The platform’s robust alerting system ensures that potential problems are addressed before they impact our services, reducing downtime and improving overall system stability. Additionally, Datadog’s comprehensive dashboards and reporting tools have streamlined our troubleshooting processes and facilitated better decision-making.

The most valuable feature of Datadog for our organization has been its real-time monitoring capabilities. This feature provides us with instant visibility into our cloud-native infrastructure, allowing us to track performance metrics and detect anomalies as they occur. The ability to monitor our systems in real-time means we can quickly identify and address issues before they escalate, minimizing downtime and ensuring the reliability of our services.

Additionally, the real-time data helps us make informed decisions and optimize our operations, ultimately enhancing our overall efficiency and performance.

While Datadog has been instrumental in enhancing our operational efficiency, there are areas where it could be improved.

One area is the user interface, which could be more intuitive and user-friendly, especially for new users.

Additionally, the pricing model can be quite complex and might benefit from more flexible options tailored to different organizational needs.

For future releases, it would be beneficial to include more advanced machine learning capabilities for predictive analytics, helping us anticipate issues before they occur.

More third-party tools would also be valuable additions.

I've used the solution for six years.

DataDog has proven to be a highly stable solution for our monitoring needs. Throughout our usage, we have experienced minimal downtime and consistent performance, even during peak traffic periods. The platform’s reliability ensures that we can continuously monitor our cloud-native infrastructure without interruptions, which is crucial for maintaining the health and performance of our services.

DataDog’s scalability has been impressive and instrumental in supporting our growing cloud-native infrastructure. The platform effortlessly handles increased workloads and scales alongside our expanding services without compromising performance. Its ability to integrate with a wide range of cloud services and technologies ensures that as we grow, DataDog continues to provide comprehensive monitoring and insights.

Our experience with Datadog’s customer service and support has been exceptional. The support team is highly responsive and knowledgeable, providing timely assistance whenever we’ve encountered issues or had questions.

Their proactive approach to offering solutions and guidance has been invaluable in helping us maximize the platform’s capabilities.

The setup is straightforward.

We handled the setup in-house.

The pricing model can be quite complex and might benefit from more flexible options tailored to different organizational needs.

One area is the user interface, which could be more intuitive and user-friendly, especially for new users.

We use Datadog for log aggregation and management, metrics aggregation, application performance monitoring, infrastructure monitoring (serverless (Lambda functions), containers (EKS), standalone hosts (EC2)), database monitoring (RDS) and alerting based on metric thresholds and anomalies, log events, APM anomalies, forecasted threshold breaches, host behaviors and synthetics tests.

Datadog serves a whole host of purposes for us, with an all-in-one UI and integrations between them built in and handled without any effort required from us.

We use Datadog for nearly all of our monitoring and information analysis from the infrastructure level up through the application stack.

Datadog provides us value in three major ways:

First, Datadog provides best-in-class functionality in many, if not all, of the products to which we subscribe (infrastructure, APM, log management, serverless, synthetics, real user monitoring, DB monitoring). In my experience with other tools that provide similar functionality, Datadog provides the largest feature set with the most flexibility and the best performance.

Second, Datadog allows us to access all of those services in one place. Having to learn and manage only one tool for all of those purposes is a major benefit.

Third, Datadog provides significant connectivity between those services so that we can view, summarize, organize, translate and correlate our data with maximum effect. Not needing to manually integrate them to draw lines between those pieces of information is a huge time savings for us.

I use log management and monitors most often.

Log management is a great way for me to identify changes in behavior across services and environments as we make changes or as user behavior evolves. I can filter out excess or not useful logs, in part or in full, I can look for trends and I can group by multiple facets.

Monitors allow me to rest easy knowing that I'll be alerted to unexpected changes in behavior throughout our environments so that I can be proactive without having to dedicate active cycles to watching all facets of our environments.

In my four years using the product, the only feature request I, or anyone on my team, has had was the ability to view query parameters in query samples.

Otherwise, improvements are already released faster than we can give them sufficient time and attention, so I'm very happy with the product and don't have any specific requests at this time.

The cost does add up quickly, so it can be some effort to justify the necessary outlay to those paying the bills. That said, Datadog provides sufficient benefits to warrant our continued use.

I've used the solution for four years.

In four years of daily use I haven't noticed any periods of downtime.

It's amazing to me how performant Datadog is given how much data we pass to it.

We've opened probably six or eight support tickets in four years of use. In some cases, the problem or question was complex and took some time to resolve. That said, customer support was always able to debug the issue and find a solution for us, so my experience has been very positive.

I've used New Relic, Honeycomb, Grafana, Splunk, Prometheus, Graylog and others.

Given the breadth of configuration options, the initial setup was fairly involved for us. We also use several services and deploy the agent in various ways because we're using traditional servers, serverless, and K8s.

We implemented the solution in-house.

The solution can be pricey if you're using many services and/or shipping lots of data, but in my opinion, the value is greater than the cost, so I would suggest doing an evaluation before making a decision.