We use the product for instrumentation, observability, monitoring, and alerting of our system.

We have multiple environments and a variety of pieces of infrastructure including servers, databases, load balancers, cache, etc. and we need to be able to monitor all of these pieces, while also retaining visibility into how the various pieces interact with each other.

Tracing data between components and user interactions that trigger these data flows is particularly important for understanding where problems arise and how to resolve them quickly.

It provides a lot of options for integrations and tooling to observe what is happening within the system, making diagnosis and triage easier/faster.

Each user can set up their own dashboards and share them with other users on the team. We can instrument monitors based on various patterns that we care about, then notify us when an event triggers an alert with platforms such as Slack or PagerDuty.

Our ability to rapidly become aware of problems focused on the symptoms being observed and entry points into the tool to rapidly identify where to investigate further is important for our team and our users.

The most valuable aspects of the solution include log search to help triage specific problems that we get notified about (whether by alerts we have configured or users that have contacted us), APM traces (to view how user interactions trace through the various layers of our infrastructure and services to be able to reproduce and identify the source of problems), general performance/system dashboards (to regularly monitor for stability or deviation), and alerting (to be automatically informed when a problem occurs). We also use the incident tools for tracking production incidents.

In some ways, the tool has a pretty steep learning curve. Discovering the various capabilities available, then learning how to utilize them for particular use cases can be challenging. Thankfully, there is a good amount of documentation with some good examples (more are always welcome), and support is very helpful.

While DataDog has started adding more correlation mapping between services and parts of our system, it is still tricky to understand what is the ultimate root cause when multiple views/components spike. Additionally, there are lots of views and insights that are available but hard to find or discover. Some of the best ways to discover is to just click around a lot and get familiar with views that are useful, but that takes time and isn't ideal when in the middle of fighting a fire.

I've used the solution for about four years.

It seems stable.

It seems to scale well. Performance for aggregating or searching is usually very fast.

Technical support is helpful and pretty responsive.

We did not use a different solution.

It's hard to say what ROI would be as I have not managed our system without it to compare to.

I don't manage licensing.

We did not evaluate other options.

It's a great tool with new features and improvements continuously being added. It is not simple to use or set up, however, if you have the right personnel, you can get a lot of value from what DataDog has to offer.

Internally our primary usage of Datadog pertains around APM/tracing, logging, RUM (real user monitoring), synthetic testing of service/application health and state, overall general monitoring + observability, and custom dashboards for aggregate observability. We also are more frequently leveraging the more recent service catalog feature.

We have several microservices, several databases, and a few web applications (both external and internal facing), and all of these within our systems are contained within several environments ranging from dev, sit, eat, and production.

Datadog has had a massive impact on our department. Before, we had loose logging dumped into a sea of GCP logs with haphazard custom solutions for traceability between logs and network calls. Datadog has helped standardize and normalize our processes around observability while providing fantastic tools for aggregating insight around what is monitored regularly, all wrapped in an easy-to-use UI.

Additionally, a range of types of users exist within our department, each with its own positive impact on Datadog. DevOps leverages it to easily manage infra, developers leverage it to easily monitor/debug services and applications, and business leverages it for statistics.

Personally I've found the RUM (real user monitoring) to be above and beyond what I've worked with before. Client-side monitoring has always been on the short end of the stick but the information collected and ease of instrumentation provided by Datadog is second to none.

Having a live dynamic service map is also one of my favourite features; it provides real-time insights into which services/applications are connected to which.

We are also investigating the new API catalog feature set, which I believe will provide a high-value impact for real-time documentation and information about all of our shared microservices that other dev teams can use.

In production, we intend to use trace IDs generated by RUM to attach to support tickets when a user experiences a traceable network error, and we want to display this trace ID to the user so if they were to contact us about a specific issue, they can provide us an exact ID displayed to them back to us. Currently, this is not possible out-of-the-box client-side without inventing our own solution for capturing these trace IDs, such as shimming the native fetch or returning the ID from the service response.

I've used the solution for approximately two years across our department and around a year or so of it being used practically and fully integrated into our systems.

Aside from one very brief bad update from the Datadog team around RUM where they broke the native 'fetch' for node in an update to RUM (which was resolved quickly) as it used to -- and may still -- modified the global 'fetch'; Datadog as a whole solution has been highly stable.

It's easy to implement and scale provided a there's a solid IaC solution in place to integrate across your system.

The Datadog support team is prompt and helpful when tickets have been submitted from our end. When their support team have been unsure, they've properly reached out internally to the relevant SME to help answer any questions we've had prior.

I've personally dabbled with some other open-source observability and monitoring solutions; however, prior to Datadog, our department did not have any solutions other than log dumps to GCP.

The initial setup was straightforward from my own experience, helping integrate within the application and service levels; however, our DevOps team handled most of the infra process with minimal complaints.

We handled the solution in-house.

I personally am not involved in the decision around costing; however, I am aware that when we first set up Datadog, we explicitly configured our services/applications to have a master switch to enable Datadog integration so that we can dynamically enable/disable targeted environments as need due to the costs being associated on a per service basis for APM/logging/etc.

I was not involved in the decision-making regarding the evaluation of other options.

I highly recommend Datadog, and I would explore it for my own individual projects in the future, provided the cost is within reason. Otherwise, I would highly recommend it for any medium-to-large-sized org.

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.

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.