Datadog Reviews

We are using a mixture of on-prem and cloud solutions to bridge the gap with healthcare entities in the service of providing patients with the medication they need to live healthy lives.

Since we're a heavily regulated company, a lot of our solutions grew from on-premises monoliths. However, as we scaled out, it became harder and harder to move forward with that architecture. Today, we're investing heavily in transforming our systems from monoliths into distributed systems.

With this change in mind, the ability for us to connect the dots using Datadog has been invaluable.

We have an API that serves as a critical aspect of our system for generating new requests for us to process in service of a patient. This service has many tentacles, and it was always hard to track down how issues from this API are affecting things downstream. Since we've added more instrumentation in this API, Datadog has changed our status from a reactive posture to a proactive one.

It has also served as a prime example to other applications on what the benefit of a well-instrumented system is for that application and other applications around it. Due to this, more and more people are using Datadog.

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.

Once Datadog has gained wide adoption, it can often be overwhelming to both know and understand where to go to find answers to questions. Currently, we use a combination of documentation and COPs to ensure that folks know how to leverage what we have in Datadog properly.

While the guides for Datadog go a long way, a way to customize the user experience from "advanced" to "novice" mode would go a long way.

I've been using the solution for two years.

It has never failed us and therefore I consider it to be very stable.

It's magic. For the most part, we just installed the product and a lot of it just worked out of the box.

Technical support is excellent.

We have used Splunk, Sentry, and a suite of hand-made solutions. We switched since the Datadog solution was both comprehensive and cohesive. It was also easier to onboard people since the solution was well-documented and standardized.

For the most part, it was really painless to set up.

We implemented the solution in-house.

We're still early on in our transformation process. That said, we are gaining a lot of steam in terms of adoption. Both the engineering team and the product team are seeing tremendous value from this solution.

Adding more tooltips and links to documentation or how-tos within the application would really go a long way for those trying to get their feet wet with Datadog.

Our company deploys the solution for our customers as an observability tool to define SLOs and SLIs along with logs and metrics. 

The solution includes incident, post-mortem, and root cause analysis that provides a level of truth for incidents and issues with applications. 

We have SREs and teams in operations, management, and applications who all access to the solution and ensure proper integrations. 

Our company is adopting SRE practices and the solution helps us to align the practices with our site reliability. We get more insights about issues at the outset which helps us to make better decisions such as continuing with agility or stopping to fix issues. 

We are at the beginning stages of using the solution but are defining it as our company standard for use by all teams. 

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.

The solution should provide alerts for cloud outages that would allow us to report potential service impacts directly to applications or on the dashboard. Alerts are important because there is a need to determine the impact on your SLAs, SLOs, and SLIs to decide whether to move toward disaster recovery or another environment. 

I would like the ability to share dashboard screenshots via email rather than having to direct others to the dashboard because it sometimes requires permissions. 

I have been using the solution for one year. 

Our SRE teams report that the solution is very solid, stable, and reliable. 

We are using the SaaS model so do not manage scalability because the product takes care of our scaling needs with no issues. 

I don't have direct access with support but our SRE teams work with them and are satisfied. 

Our company used other products in the past but wanted to move to the cloud. We found that the solution was a very good fit for us. 

The initial setup is not that easy because there are many choices for configuration, workloads, servers, and containers. 

We utilized technical support to help us understand integration and prepare patterns for other applications. 

We created small configurations and then utilized technical support to configure an application selected from our portfolio. 

We utilize a team approach for implementations that sometimes includes SREs. 

The solution is fairly priced but history and log storage can get costly depending on your needs. 

I rate the cost a four out of ten. 

The solution is appropriate for companies that are moving to the cloud and want a very solid tool for observability, logging, and everything related to SRE practices. 

I rate the solution a nine out of ten. 

Datadog is a SaaS solution we tried for URL and synthetic monitoring. You record a transaction going into a website and replay that transaction from various locations. Datadog is mainly used by the admin, but three or four other guys had access to the reports and notifications, so it's five altogether.  

We probably tried no more than 8 percent of what Datadog can do. There are so many other bits and modules. I've only gone into about half of what APM can do in the Datadog stack.

We could detect outages on particular websites or problems in specific locations. If I had paid for the full solution, I'm sure I could get a lot of value out of Datadog.

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

Datadog needs more local Asia-Pacific support, and if they don't have a SaaS solution in Asia-Pacific, they should offer an on-prem version. I'm told that's not possible. 

I have used Datadog for about two or three years.

I was only using Datadog to monitor on a small scale. 

I'd rate Datadog support four out of 10. It was primarily an issue with support in the Asia-Pacific region. I sent them several emails, and they responded around three weeks later. 

They said it went around the houses. Nobody knew who to respond to. That's not good enough. They should have at least told me they'd received the email. I used to work in support.

Neutral

We were just trying Datadog, and we've switched temporarily to Site24x7. We're looking for one of the bigger ones. They've all given us proposals, whereas Datadog hasn't come forward with a proposal for what they could do.

I used Datadog because I already had a relationship with them at a previous company. However, that guy's moved on now, and I wanted to see how good they were. 

Setting up Datadog is pretty straightforward. I have a lot of experience doing that sort of thing. It took maybe a day and a half to deploy because I was picking externally facing websites.

I deployed it by myself. One person is enough for the small system we had. However, if we were moving forward, I'd recommend at least two or three people to manage it. 

Datadog would've cost around $850 a month based on the loads we were doing, and you could estimate roughly what you would be paying monthly. I liked their pricing model. It was flexible, so you only paid for what you used. I rate Datadog pricing eight out of 10. 

We looked at several URL and APM monitoring solutions like Site24x7 and Pingdom. They weren't big players like Dynatrace or any of the those that had already provided us a request for information. 

Even with our negative experiences, I'd still give Datadog an eight out of 10. Datadog is a complete solution with easy-to-use templates and excellent scalability. People should know exactly what they're going to configure before they try it out. The trial is brief. Don't start a trial until you know exactly what you're going to do. 

You must be certain that you can meet any internal security requirements. If you're in the Asia-Pacific region, you might not be able to run something that's running abroad.

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 

Datadog could improve the flexibility with AI and ML concepts. This will allow customers to be more leveraged towards publishing.

I have been using Datadog for approximately one year.

Datadog is stable. We did not have a single outage.

I have found Datadog to be scalable.

We have approximately 2,000 users using the solution in my organization.

The support from Datadog is excellent.

I have previously used AppDynamics and Dynatrace. 

Datadog's initial setup is easy because they have helped us come up with the easiest way of instrumenting any of the features which need to be deployed.  We worked on it with their engineers and we were able to happily do it. We have done approximately 60 application monitoring through Datadog since our deployment.

We have a very tiny team of four members that do the maintenance of Datadog.

The price of Datadog is reasonable. Other solutions are more expensive, such as AppDynamics.

Datadog is far better than any other monitoring tool in introducing any of the new capabilities because they think before Amazon AWS and Microsoft Azure before they introduce the concepts. Datadog is a good tool to have for monitoring your own infrastructure.

I rate Datadog a ten out of ten. 

We're using the product for logging and monitoring of various services in production environments. 

It excels at providing real-time observability across a wide range of metrics, logs, and traces, making it ideal for DevOps teams and enterprises managing complex environments. 

The platform integrates seamlessly with our cloud services, but browser side logging is a little lagging. 

Dashboards are very useful for quick insights, but can be time consuming to create, and the learning curve is steep. Documentation is vast, but not as detailed as I'd like.

The solution has made logging and tracing a lot easier, and the RUM sessions are something we did not have previously. Datadog’s real-time alerting and anomaly detection help reduce downtime by allowing us to identify and address performance issues quickly. 

The platform’s intelligent alert system minimises noise, ensuring your team focuses on critical incidents. This results in faster Mean Time to Resolution (MTTR), improving service availability. 

It consolidates monitoring for infrastructure, applications, logs, and security into a single platform. This enables us to view and analyse data across the entire stack in one place, reducing the time spent jumping between tools.

Real user monitoring has made triaging any possible bugs our users might face a lot easier. RUM tracks actual user interactions, including page load times, clicks, and navigation flows. This gives our organization a clear picture of how our users are experiencing your application in real-world conditions, including slow-loading pages, errors, and other performance issues that affect user satisfaction. We can then easily prioritize these, and make sure we offer our users the best possible experience.

I'm not sure if this is on Datadog, however, Vercel integration is very limited. 

They need to offer better/more customization on what logs we get and making tracing possible on Edge runtime logs is a real requirement. It is extremely difficult, if not completely impossible, to get working traces and logs displayed in Datadog with our stack of Vercel, NexJs, and Datadog. This is a very common stack in front end development and the difficulty of implementing it is unacceptable. Please do something about it soon. Front end logs matter.

I've used the solution for a little over a year.

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.

Positive

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.

Our company has a microservice architecture, with different teams in charge of different services. Also, it is a start, which means that we have to build fast and move very fast as well. So before we were properly using DD, we often had issues of things breaking, but without much information on where in our system the breaking happened. This was quite a big-time sync as teams were unfamiliar with other teams' codes, so they needed the help of other teams to debug. This slowed our building down a lot. So implementing dd traces fixed this

DataDog has many features, but the most valuable have become our primary uses.

Also, thanks to frequent concurrent deployments, the DataDog alerts monitors allow us quickly detect issues if anything occurs.

The monitors can be improved. The chart in the monitors only goes back a couple of hours, clunky. Also, it can provide more info, like traces within the monitors. We have many alerts connected to different notification systems, such as Slack and Opsgenie. 

When the on-caller receives notifications fired by the alerts, we are taken to the monitors. Yet often, we have to open up many different tabs to see logs, traces and info that is not accessible on the monitors. I think it would make all of the on callers' lives easier if the monitor had more data

We've used the solution for three years.

Datadog is expensive. 

The tool's deployment is easy. 

The solution's pricing depends on project volume. 

I rate Datadog a seven out of ten. 

The main use case is observability and reliability as part of a platform/delivery engineering solution. We use the product to assist tenants and clients within the company to get more ramped up on SRE/DevOps.

The solution has provided us with a lot more insight into service-level metrics, which is especially useful with APM/tracing. It gives us all-up dashboards and alerts to assist with incident management.

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

Custom-level metrics could be improved.

Billing should be more transparent.

I've used the solution for three to four years at multiple companies.

The solution is very stable.

The scalability is fantastic so far.

Technical support has been great so far.

The initial setup is straightforward. The documentation we use is very clean and concise.

We handled the initial setup in-house.

I would advise others to be cautious around custom metrics and be picky when setting them up.

We primarily use the solution for logging and APM, and for real user metrics.

The solution has helped out organization gain improved visibility.

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

The product needs a better Datadog agent installation.

I've used the solution for one year.

We previously used App Dynamics.

Before choosing Datadog, we looked at Splunk.