Datadog Reviews

We implement these solutions for our clients. We have implemented Datadog as an SIEM solution.

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.

While I like the ease of use, when compared with Tenable Nessus they could still improve their usability. They are okay, but there is room to be better.

They could have more integration.

They could be more intuitive as well. For example, the intuitivity of the user interfaces, and how long it takes for users to learn how to use Datadog.

It is not impossible to use, or impossible to do the administration with it but when you put these two next to each other, meaning Nessus and Datadog, Nessus comes out as the winner.

I have been using Datadog for two years.

We are not using the latest version, we have missed at least one update.

We have no issues with the stability of Datadog.

Datadog is a scalable product.

We have two customers who are using this solution.

Technical support runs 24/7. The technical support is absolutely fine.

We are also using Nessus. My experience using Tenable Nessus is better.

It is easier to install than to use it.

I was not the one doing the handling the installation. I'm a senior consultant, and I was coordinating, planning, and interacting with clients. But the actual installation, I was not involved with. 

The installation could be done in an hour or so.

It is not complex, two professionals are enough to complete the installation and maintenance of Datadog.

With Datadog, it's a monthly fee. They prefer monthly subscriptions.

I would recommend this solution for medium enterprises with 100 to 1,000 employees. 

Small business is too small for the way that Datadog operates. It is not the best for very large enterprises for a company with more than 1,000 employees.

I would rate DataDog an eight out of ten.

We deploy agents on-premise to collect data on on-premise VM instances. We don't use Datadog in our cloud network. We do have some Cloud apps that we have it on and we also have Containers. We have it on their headquarters, the main software for them is on their own Cloud.

Eventually, we're building out the process now and using it better. We plan to use Datadog for root cause analysis relating to any kinds of issues we have with software, with applications going down, latency issues, connection issues, etc. Eventually, we're going to use Datadog for application performance, monitoring, and management. To be proactive around thresholds, alerts, bottlenecks, etc. 

Our developers and QA teams use this solution. They use it to analyze network traffic, load, CPU load, CPU usage, and then Tracey NPM, API calls for their application. There are roughly 100 users right now. Maybe there's 200 total, but on a given day, maybe 13 people using this solution.

It hasn't improved the way our organization functions yet, because there's a lot of red tape to cut through with cultural challenges and changes. I don't think it's changed the way we do things yet, but I think it will — absolutely it will. It's just going to take some time.

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.

I have been using Datadog every day since September 2020. I also used it at a previous company that I worked for.

Stability-wise, it's great.

It seems like it'll scale well. We're automating it with Ansible scripts and service now so that when we build a new virtual machine it will automatically install Datadog on that box.

The tool itself is pretty good and the customer service is good, but I think they're a growing company. I think better access to their engineers when we have a problem could be better. For example, if I asked the question, "Hey, how do I install it on this type of component?" We'll try to get an engineer on the phone with us to step us through everything, but that's a challenge because they're so busy.

Technically-wise, everything's fine. We don't need any support, everything that I need to do, I can do right out of the box. But as far as, in the knowledge of their engineers on how to configure it on given systems that we have, that's maybe at six because they're just not as available as I would've hoped.

We were using AppDynamics. Technically, we still have it in-house because it's tightly wound into certain systems, but we'll probably pull that off slowly over time. The reason we added Datadog and eventually we'll fully switch over is due to cost. It's more cost-friendly to do it with Datadog.

Yes, we looked at Dynatrace, AppDynamics, and New Relic. Personally, I wouldn't have chosen Datadog for the POC if it were up to me. Datadog was a leader, but New Relic was looking really good. In the end, the people above me decided to go with Datadog — it's a big company, so they wanted to move fast, which makes sense.

If you're interested in using Datadog, just do your homework, as we did. We're happy so far I think; time will tell as we are still rolling things out. It's a very good company. It's going to be a year before we really can tell anything. If you do your homework, you'll find that if you're really concerned with cost, it's good.

There are some strengths that AppDynamics and Dynatrace have that Datadog I don't think will have down the road, but they're not things we necessarily need — they're outliers. It would be nice to have them, but we can manage without them.

Know what you want. There is no need to pay for solutions like Dynatrace or AppDynamics that are more expensive or things that are just nice to have if you don't absolutely need to have them. That's something people need to understand. You just have to make sure you understand what it is that you need out of the tool — they are all a little different, those three. I would say to anybody that's going with Datadog: you just have to be patient at the beginning. It's a very busy company right now. They're very hot in the market.

Overall, on a scale from one to ten, I would give Datadog a rating of eight. It does what we need it to do, and it seems to be pretty user-friendly in terms of setting things up.

Features-wise, I'd give them a rating of ten out of ten. The better access we get to assistance from the engineers on how to configure dashboards and pulling metrics that we need, that would bring it up a little bit. So overall it would be harder and it would have to be perfect for it. I would say maybe they could bring it to a nine.

Datadog has clear dashboards and good documentation. 

The solution needs to integrate AI tools. 

I avail support from our internal team. 

I rate Datadog a nine out of ten. 

Our primary use case would be using the dashboards and getting proper insights based on the dashboards.

The monitoring, SLO, and SLA have been better and easier since we started using the Terraform infrastructure. APM has been easier as we had to enable it through the CronJob directly.

Profiling has been made easier. We are able to get many insights into the code. Profiling provides really good insights right now. 

Logs are the most valuable and the best solution so far. Datadog can help solve any slow queries or database-related errors. 

The primary use case would be using the dashboards and getting proper insights based on the dashboards.

Monitoring has been better and easier since we started using the Terraform infrastructure.

APM has been easier as we had to enable it through the CronJob directly.

Profiling has made it easier in terms of getting many insights into the code.

The logs are the most valuable and the best solution. Datadog can help us to solve any slow queries or database-related errors.

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.

Integration should have been easier. It is very tough to go to all the services and enable Datadog integration for each AWS service. 

We can add the AWS services and the services on one page and show only the services that are enabled. A similar approach should be for any other integration.

Lately, chat support has a longer waiting time. We would love to get faster chat support. We also need additional support for sending the flare files

We use Datadog to monitor our Kubernetes clusters. 

We have 3 different clusters for different parts of the SDLC. We run the Datadog agent DaemonSet as well as the Datadog cluster agent. Our services have the APM installed by default. 

To create monitors, we use Terraform. This is provided out-of-the-box for our service owner. 

We run EKS on top of K8s, therefore, we also make use of some of the AWS monitoring capabilities that can be integrated into Datadog. 

We are hugely reliant on Datadog for all aspects of our system.

With Datadog, we were able to gain observability in our system. 

The installation step is pretty straightforward. 

It's easy to use by non-DevOps users. For instance, our engineers do not interact with K8s often; therefore, it is hard for them to debug. However, with Datadog, they are able to view their containers and deployments with a single click. 

We also heavily use the tags to help us identify who the service owners are. This is super useful when we need to track owners for patching or pick up new features we implemented.

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.

There is not much that needs to be improved. 

The UI is super user-friendly. The deployment process is easy. We enjoy using the integrations with Slack and PagerDuty. 

Customer support is awesome from our experience. There is a lot of documentation for us to be able to use if we need to. 

I'm not sure if Datadog can monitor K8s deployments in real-time. For instance, being able to see a deployment step by step visually. This would be helpful if there were any incidents during the deployment. 

In general, Datadog is a great solution.

I've used Datadog since I joined my company about a year ago.

We haven't had issues with the stability.

The scalability is really great.

We've had no issues with the product or support. 

The initial setup is super simple, and the documentation was helpful.

We managed the initial setup process in-house.

We've witnessed ROI in our DevOps.

Datadog provides us with a solution for data ingesting for all of our application metrics, resource metrics, APM/tracing data etc. 

We use it for use in dashboards, monitoring/alerting, SLO targets, incident response etc. 

We have a lot of applications across multiple languages/frameworks etc., and have deployed in Kubernetes across multiple regions in AWS, along with underlying managed resources such as SQS, Aurora, etc. 

Datadog makes understanding the state of these seamless. We are a company with millions of daily active users, and this level of detail is excellent.

Datadog has allowed us to rapidly spin up alerting and monitoring that helps our incident responders get alerted quickly when our SLOs are in danger and helps to quickly resolve issues. 

It is the single most important tool we have from an SRE perspective. 

It also provides us with an easy way to get information at a glance for all of our services through APM and create unified dashboards that track our underlying resources, such as databases, queues, etc., alongside application data. 

It has been invaluable to our organization.

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.

Managing dashboards as IaC is a bit hard to work out at times. I use custom tools to convert JSON dashboards to Terraform resources. Ideally, I'd like for some sort of building tool for this to be built into the app. For example, a templating system that can easily be exported to IaC would be transformative for us. 

There are also some aspects of the API that can be a bit verbose - especially in the area of new features like SLOs - and take some time to understand. That said, overall, they're well-documented enough to be a minor concern for us.

I've been using the solution for over five years.

I have never seen a major outage that prevented us from using Datadog, although I can't speak for other teams/time zones

This product is massively scalable - I haven't seen any issues as we continue to onboard new technologies and teams

Datadog provides us with a number of direct lines to support, although I haven't personally required their assistance.

We previously used LightStep for APM and switched to Datadog to unify all of our application data.

Most elements are quite simple to set up. However, some types of data collection require organization-wide engineering buy-in.

We handled the initial setup in-house.

I am using the solution for monitoring metrics, logs, traces, etc. It's mainly for making dashboards as well as monitoring our services. 

We also use Datadog to help centralize our incident management to show the logs, where issues spiked, and some metrics. 

We use Datadog to do troubleshooting in Kubernetes, specifically in our Azure Kubernetes service. Beyond that, we are looking to use open telemetry in tandem with Datadog to further our log-tracing efforts. In the future, this may be expanded.

This solution improves our organization as now we have higher visibility into our application that we otherwise would not have. 

Since the Datadog agent comes in three forms, agentless, scraping, and through the API, it is very flexible. It is this flexibility in how to report our logs that keeps our logs centralized and organized. 

One major drawback of Datadog is the cost. Sometimes we set up flows in place to monitor resources that end up logging more than we thought, and the bill is too high.

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.

The documentation leaves a lot to be desired for new users. The documentation is way too much text and has no real information just to help get people started. Sometimes it doesn't help to read an entire essay just to get a grasp on how the logs or metrics work.

I've used the solution for two years.

Our use case is mainly deploying into our applications for monitoring/logging observability. We currently have our microservices feed into an actuator that exists in each instance of our application that extends to a local and central Grafana for client and internal visibility. The application we use is Grafana.

Logging captures application and system logs that are ported to each application instance for querying.

Whenever anything occurs that is considered unhealthy from a range of health checks, we have notification rules configured internally and externally for a prompt response time.

We have been able to be a more confident, knowledgeable, and capable team when everything is being ported into a centralized format. Beforehand, knowledge was isolated to individuals. Knowledge in terms of what information represented and where it was led to a lack of confidence. By having everything in one place, rules out that confusion and allows us to respond better to issues.

It also allows for personal growth as our team is learning the application from the ground up, and each person is enhancing their own skills.

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.

The ability to find what you are looking for when starting out could be improved. It was a bit overwhelming trying to figure out what is the best solution. It led to many prototypes or time spent just perusing documentation. If we were able to select bundles or template use cases, we would hit the ground running quicker.

I've used the solution for one year.