Datadog Reviews

We use different tools for log collection and monitoring. Using Datadog will combine different use cases into one product that will be easier to manage. 

The tools we use are open-source, so there is no commercial support. Having customer support would be ideal since we're a small team. 

Profiling would be another great feature to have. Currently, it's manual. Having Datadog would give us a standard, and we don't have to do much manual work.

It will solve a lot of our problems. We have different tools for each of them in our organization; they are open-source and therefore not very well maintained with there is no customer support. 

Having an industry-standard product such as Datadog would be ideal for us as we are short on manpower. Since this is a managed all-in-one product with readily available support, we will be able to focus on application logic rather than figuring out why a tool isn't working.

The biggest thing I liked was the combination of all the things - monitoring, log aggregation, and profiling. We have different tools for each of them in our organization and all of them are open-source. These are not very well maintained and there is no customer support. 

Having an industry-standard product is ideal for us as we are short on manpower. Profiling is another amazing feature. Currently, we rely on some open-source solutions, and it's all done locally. Having it done on Kubernetes would give us more insights and help with performance. Alerting is again a nightmare for us. Datadog solves all of these issues.

It can be overwhelming for new people as it has a lot of features. The UI could certainly be improved. Having less information with better organization could help newcomers. I haven't seen the documentation, however, a well-organized documentation would invite many varied users.

I've been using the solution for three years.

We are using Datadog for server metrics, log aggregation and searching, system monitoring, alerting the team about errors, and dashboards for our developers. It's used by the Site Reliability Engineering team and Management of all levels. 

It's assisting us in proving SOC II compliance. 

We're looking to improve our usage of Datadog's RUM and APM components to get better and more performance insights on our production environments. 

We're also looking to leverage more synthetic monitors and runbooks for anyone responding to incidents.

The ability to easily drill down into log queries quickly and efficiently has helped us to resolve several critical incidents so far this year, and we heavily rely on a series of dashboards showing us various queues and load on CPU and memory for servers. 

We also have a view of the information required when we begin the patch and/or upgrade processes. 

I've also set up several monitors to alert the Site Reliability Engineering team when various metrics show a server might be reaching capacity. We use it to send an email suggesting we increase the size of the cloud instance.

The ability to easily drill down into log queries quickly and efficiently has helped us to resolve several critical incidents. We heavily rely on dashboards that are showing us various queues and load on CPU and memory for servers. 

We also have a view of the information required when we begin the patch and/or upgrade processes. 

I've arranged several monitors to alert the Site Reliability Engineering team when various metrics show a server that might be reaching capacity. We use it to send an email suggesting we increase the size of the cloud instance.

Datadog could always lower the price! In general, more demos online and maybe more free hands-on tutorials for basic functionality would be good for less technical users. 

I would also prefer more chances to amend the contract more than twice a year. As a smaller but growing company, it can be difficult to adequately predict demand.

I've used the solution for more than three years.

We use Datadog for general observability into our infrastructure, as well as running analytics queries for our SLI/SLO platform. This helps all of our teams be informed of how well their products are actually performing in production, and aim their efforts at the thing that will provide the highest ROI. 

We also use it for general monitoring and alerting during load tests and service releases to detect any issues related to the deployments. This helps us maintain our high contractual uptime promises to our clients.

It has drastically reduced the amount of time we spend on debugging issues and tracking down the root causes of incidents. What might have taken days or hours with separate vendors in the past (or even single vendors with terrible UI) is now quick and easy. 

We've often gone from detecting an incident to identifying the needed fix within ten minutes or less and covered multiple domains like APM, Logs, Database performance monitoring, etc., in just a few clicks. This is extremely powerful.

Distributed tracing is the most valuable feature. We have hundreds of microservices, and knowing how top-level requests weave throughout all of them is invaluable. 

At one glance, we can clearly see which service is slow and then switch over to the infrastructure view or container view to debug why the slowness is happening. This is true of all their other integrated products as well; the more you add, the more insights you get when looking at traces.

We also use RUM extensively. This helps us cover the last mile of application performance. Without it, we wouldn't know if our browser applications were functioning slowly for our users.

There is occasional UI slowness and bugs. While the Datadog UI is generally miles above its competitors, there are a few cases where it falls short or has started to slow down over time. They also occasionally make poor UI redesign choices. They should continue focusing on this area to maintain the high standard they started out with.

I've used the solution for five years.

We've never had major stability issues.

Scalability has never been an issue, although there is occasionally UI slowness.

Support via tickets is absolutely terrible. It's the one obvious bad spot for Datadog. If we didn't have direct relationships with many of their product managers, our experience would be much worse.

Negative

We previously used New Relic. It had a terrible UI and the integration between products was not great. Datadog is miles ahead of them and is continuing to increase that distance.

The initial setup is straightforward, and the docs are done well.

We managed the implementation in-house.

Our ROI is high.

I'd advise users to negotiate rates. Datadog's off-the-shelf rates are pretty high.

We have only used and looked into New Relic.

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.

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.