Datadog Reviews

We collect all data logs from all operating systems, such as Windows, Linux, VMware, and bare metal data centers. We also automatize the installation of the agent on servers. 

Now we are starting a POC to analyze the APM module. In the feature, the next step is to do a POC of security modules. 

The final idea is to have a unique portal for observability. This will make it easy to troubleshoot and for layer levels 1 and 2. 

We are looking into a lot of modules. We collect all data logs from all operating systems, including Windows, Linux, VMware, and bare metal data centers. We also automatize the installation of the agent on servers. 

We're developing POCs for APM and security modules. We'll also have a unique portal for observability. This will make it easy to troubleshoot. 

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

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.

We need a lot of modules since we collect all data logs from all operating systems. 

The most important module for us is log management. The second is the security module. The third one is the APM.

We've used the solution for one year.

We use Datadog for observability and monitoring primarily. Various cross-functional teams have built various dashboards, including Developers, QA, DevOps, and SRE. 

There are also some dashboards created for senior leadership to keep tabs on days to day activities like cost, scale, issues, etc. 

Also, we've set up monitors and alarms that kick off when any metrics go beyond the threshold. With Slack and PagerDuty integration, correct team members get alerted and react to solve the issue based on various runbooks.

Using Datadog metrics has helped the organization a lot in many manners. With one centralized monitoring place, it's a lot less effort to keep track of the system and applications' health. 

Using this also helps teams be proactive in dealing with any issues before they get escalated by customers. 

Lastly, having so many integrations makes the DevOps and SRE's lives a lot easier when automating the detection and resolution of any issues hidden in the system or applications. Overall, it has helped a lot.

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.

The solution can be improved in a few areas. 

The parallel editing of the dashboards should not cause users to lose the work of another person. 

Secondly, we would like to see more demos of tools that are in beta version, when they come live. I am sure they will help us a lot.

I've been using the solution for slightly over two years.

I find the solution to be very stable.

I totally love it. It is scalable. 

We previously used Sumo Logic.

The initial setup is not so difficult.

We implemented the solution in-house.

The ROI is very fair so far.

I can't recommend the licensing.

I was not involved in any pre-evaluation process.

I use the solution to manage security-related logs and metrics, as well as create detection rules for security events. I am a security engineer, so one area of interest is the CSPM product, giving us the ability to look at findings across the cloud environment. 

The great part about the Datadog security products is that they incorporate the context of the resources/hosts where the security event is found. This allows us to see exactly what is running on a host that we see as a security alert.

The greatest impact it has had is on the ability to democratize observability and put monitoring into the hands of the people. Teams can quickly get the information they need, without needing a bunch of training, since the UI is super intuitive and easy for beginners. This helps reduce time to resolution during incidents and gives context to developers quickly and easily. Context is really important since seconds matter when the ship is down, and you don't know why.

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.

One area where I was really looking for improvement was the CSPM product line. I had really wanted to have team-level visibility for findings, since the team managing the resources has much more context and ability to resolve the issue, as the service owner. However, this has been added to the announcement in a recent keynote. 

Personally, I've used it my entire time employed here, more than three years.

I primarily use the solution to learn, watch and monitor business and engineering metrics in the production and QA environments of my team. 

We create monitors on key business metrics and observe regressions and anomalies.

Less often, I leverage the events ability in Datadog to get notified about significant activities happening in my teams' deployments.

We learn about Datadog monitor alerts through Slack and often attempt to create SLOs using Terraform.

We use APM for observability.

Most recently, I learned about WatchDog Alerts that I will be heavily looking into.

Datadog simplified my ability to watch easily and add monitors on any metric emitted by any team at my organization.

Datadog APM immensely improved our ability to understand the reasons behind production issues. Its ability to navigate across services seamlessly to understand the time spent at each critical stage of a production request is helpful. This, combined with Datadog's historical ability to show business metrics aside, helped get more powerful insights much more quickly.

Datadog's seamless integration with Slack and PagerDuty helped us to receive alerts right to the most common notification methods we use (our mobile devices and Slack).

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. 

I would like better navigability across pages. The UI/UX is powerful, yet less intuitive. A lot of times, I somehow navigate across buttons and pages, and I end up forgetting how to get back to a particular view that was more insightful. 

Particularly as Datadog starts offering more platform capabilities like APM, Watchdog, Shift left initiatives like instrumentation, continuous testing, intelligent test runner, and Synthetic and real user monitoring, the UI can become more and more clunky, giving users a very frustrating experience. 

I've used the solution for five to six years.

We primarily use the solution for observability.

The solution has helped with our POV phase.

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

The FinOps needs improvement. 

The stability is good.

The scalability is good.

We previously used AppDynamics and Dynatrace.

We also evaluated AppDynamics and Dynatrace.

We mostly use it to handle log aggregation, monitor our web application, and alert us on data pipeline failures. 

Our system is fully on AWS, and so we pipe in all of our Cloudwatch logs into Datadog to have a central place to index and search logs. 

Our web app is built on an Elastic Beanstalk backend, and we use the Datadog agent to keep track of all of the requests that hit our backend and all of their components. 

We also use the prebuilt AWS pipeline dashboards to monitor our batch jobs and lambdas.

Datadog has made it much easier to have a central place for people to look for logs and made it much easier to notify them of any elevated error rates or failures. 

It is also easier to get high-level views of platform health, whereas looking directly at AWS tends to provide very specific insight into particular surface areas or products. 

By having the whole team onboard onto Datadog, we also have a single source of truth that everyone can use when triaging and resolving incidents that occur across any surface area.

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.

The product is quite complex, and there are so many features that I either didn't know about or wasn't sure how to use.

One thing that could be improved is somehow surfacing interesting or relevant products that might be applicable given our infrastructure. 

Additionally, the billing can sometimes be confusing and opaque, especially around not making it obvious what the implications can be if you add different AWS integrations. This has caused some unexpected costs in the past due to engineers not understanding how Datadog pricing works.

We've used the solution for around two years.

This was the first solution we tried.

It is quite expensive, especially if you don't know how the pricing works.

Our primary use case is log management and we also use the solution for monitoring the application and underlying infrastructure. I'm an IT test manager. 

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

I'd like to see more flexibility in the customization and they have a few settings which need to be changed but we are unable to make those changes as users or as the administrator. The tagging to get the different parts of the monitoring interconnected is a bit tricky and takes time to work out. 

I've been using this solution for 18 months. 

The stability is good. 

I would say that the amount that we are monitoring is not that large and we've never had any scalability issues. We have around 50 users in our department. 

The availability or accessibility to customer service is not always good, although they generally provide solutions once you do manage to get hold of them. 

We have previously used different tools for different parts of the monitoring. We changed to AWS when we moved to the cloud. We also found that the effort in maintaining Grafana and Prometheus and keeping it up to date was taking too much time.

The initial setup was straightforward, we used a service provider and they also maintain our operation in general.

We have a four-year contract with Datadog, and the solution is pay-as-you-use. 

I would suggest using the documentation, which is quite good. It's best to start with existing integrations, and then do the customization step-by-step.

I rate this solution eight out of 10. 

We are evaluating Datadog for observability and monitoring requirements that we have in our company. In our use case, our intention is to provide some kind of framework for multiple app teams to use the tool for our cyber ability and engineering practices.

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.

One of the improvement opportunities that we have identified in my project concerns how hard it is to manage an organizational structure when you have multiple things in one organization, and you want to provide some kind of isolation between them. At the same time, from the management perspective, you want to see an overall overview of what is happening in your business unit, or as a whole division. This is the kind of limitation we're facing.

I'm not sure what kind of features are in the roadmap right now, but I encourage the development of features for defining your organization, and allowing the visibility of what kind of metrics you can get. Those features would be really useful for us. 

I have been using Datadog for about six months.

It's a very scalable product. Right now we are using the SaaS version, so we don't need to worry about the infrastructure or whatever is needed for the platform it is running on. All the capturing of data is sent to the SaaS product and that can be as scaled as needed.

So far their support is pretty nice. They have established many meetings and training sessions, and they are supporting our requirements very well. I don't have any complaints with Datadog support.

While it is an expensive product, I would rate the pricing level at four out of five. 

Normally, the primary reason why people use these kind of tools is observability, but right from the beginning you have to understand what observability is, what it means for your company, and how the tool is going to help you to capture the proper metrics for making your applications observable.