Datadog Reviews

We're in the process of doing a Proof of Concept with the solution right now.

One of the solution's greatest aspects is its overall simplicity. It is very easy to use. It's easier to handle than other brands we have access to.

The monitoring capabilities that the solution provides are very good.

We find they have a very helpful alert system.

The product has been very stable. We've liked the performance provided.

The initial setup is simple and easy to handle. It's not hard at all.

The experience we've had with technical support has been very positive so far. They are helpful.

The integration capabilities have been pretty good overall. We have no complaints.

We haven't used the solution too much yet to assess what features it is missing or would improve. The support in Latin American is a point that would mark as a point to improve

The stability of the solution is excellent. There are no bugs or glitches. It doesn't crash or freeze. It's very reliable in terms of performance.

Technical support has been helpful overall. They are knowledgeable and responsive. We are quite satisfied with the level of service they provide to our company. 

We are actually still using actually Elasticsearch and Kibana, however, we prefer to use Datadog due to the fact that it is very simple. We really enjoy how it operates

We found the initial setup to not be overly complex. It's rather straightforward and easy to execute. A company shouldn't have any issues with the process.

I'm not sure if the license is expensive or cheap. It's managed by the customer and we've recently come on board.

I am an end-user and customer. I don't have any business relationship with the product itself. We work with clients on the infrastructure and IT developer infrastructure mostly. We work with a variety of solutions, including Dynatrace, Datadog, Elasticsearch, etcetera.

In general, due to its simplicity and ease of use, I would rate the solution at a nine out of ten. We've been very satisfied with the solution overall.

We use the solution for logs from all our applications. In Datadog, for monitoring logs, our team creates an automation for implementing massive logging in all our systems. Now, we are deploying it in our core systems.

We are using it to indicate issues and alert our operations team. With this, we better monitoring of our applications and logs.

However, the main difficulty is implementing the solutions in our Kubernetes cluster, separated just as logs to the specific namespace as the volume of logs is tremendous.

The solution is useful for monitoring logs. 

We can create an automation for implementing across all our systems. Right now, we are deploying it in our core systems.

The greatest value is integrated monitoring as it is able to quickly detect issues and improve our time to market.

Our main challenge is implementing the solutions in our Kubernetes cluster, separated just as logs to the specific namespace since the volume of logs is tremendous.

I've used the solution for one year. 

Our company is transitioning to using the solution for monitoring and analytic services we provide to customers. Once fully rolled out, there will be 80-100 users companywide. 

The solution's SaaS model is easy to manage and works well in single- or multi-cloud environments. 

The Saas format can handle heterogeneous or multi-language applications much better than using a combination of tools. 

Graph filters for logs need to be set manually which works well for JSON but not for unstructured logs. 

Making structured logs for high-performance applications is over our heads so we had to dump some technical streams for our logs. 

I recently started using the solution. 

The solution is stable and passed an our internal assessment.

The solution is scalable. 

We received product support through the sales department and they were helpful. 

The solution is better than what we previously used. 

The initial setup is straightforward which is impressive.

I rate setup an eight out of ten. 

We implemented the solution in-house and are continuing to roll it out. 

Licensing is based on the retention period of logs and metrics. 

I rate the solution an eight out of ten. 

We primarily use the product for tracing, metrics, and alarms in various deployment environments.

The product has provided our company with improved observability, which has helped make the incident response more targeted and quicker.

APM is great and has provided low-effort out-of-the-box observability for various services. 

Monitors are helpful, and definitions are simple. 

Terraform support is nice as it allows us to create homogenous monitoring environments in various deployment environments with little additional effort. It also facilitates version control of monitor definitions, etc. 

The Golang profiler is generally good with the exception of delta profiles; it has provided helpful observability into Heap Allocations which has helped us reduce GC overhead.

Delta traces on the Golang profiler are extremely expensive concerning memory utilization. In a Kubernetes environment where we would like to set per-pod memory allocations as low as possible, the overhead of that profiler feature is prohibitive. In one case, our pods (which were provisioned to target 250 MB and max at 500 MB memory) got stuck in a crash loop due to out-of-memory, which was caused entirely by the delta profiles feature of the profiler.

Multistep Datadog synthetics lack the feature of basic arithmetic. For our use case, performing basic arithmetic on the output of previous steps to produce input for subsequent steps would be extremely useful.

I've used the solution for nine months.

The solution is primarily used for better understanding the health of applications, modern environments, and many other solutions, which are the main focus of Datadog and many other monitoring tools.

With Datadog specifically, I can look at the health of the technology stack and services, and also integrate multiple metric sources, security, business data, and much more. This makes it a real software solution for centralizing data and unifying monitoring silos in one place. Datadog is like a hub - not just a monitoring software.

The solution primarily has helped the organization by helping us better understand the health of applications, modern environments, et cetera. 

We can see the health of the technology stack and services. We can also integrate multiple metric sources, security, business data, and much more. It centralizes data and unifies monitoring in one place. It's Helping reduce costs with other solutions, and also reduces costs with teams that might waste time with manual troubleshooting.

Understanding better the health of applications, modern environments, and many other solutions, is the main focus of Datadog and many other monitoring tools.

With Datadog I can look at the health of the technology stack and services. I can also integrate multiple metric sources, security, business data, and much more. It's great for centralizing data and unifying monitoring silos. 

Datadog is a hub, not just a monitoring software. The biggest value of Datadog is looking at the big picture, not only one part of it.

Datadog could have a better business analysis module. 

Other vendors have specific business collections and analyses. With Datadog, I don't see much of it. It's possible to do this with custom metrics, dashboards, etc. However, none of those are business-focused - and that is what is lacking in Datadog.

I've used the solution for two years. 

We use the SaaS version of the product.

We use it for monitoring and instrumentation of security. We secure our databases and servers. It is typically for the security of apps, services, and systems. We are using its latest version.

It has reduced some challenges, and it has optimized the time spent on monitoring and management activities. It has improved the visualization and the ability to monitor and control.

Datadog increases our visibility. It puts all the data in one log so that we can use that log in a contextual manner. Some operational optimizations definitely have happened with this solution. In general, the user community is happier than before. We are basically asking them every quarter how happy they are on a scale of zero to five. That needle has moved but not significantly. If it was 3 earlier, it is still less than 3.5 now, but the user experience is better than before. 

Because of this monitoring, we are empowered to publish certain dashboards for the business folks as well. We have three to five senior business folks who are looking at their investments and operations optimization. They are basically putting money on the table for this.

Its integration definitely stands out. It provides seamless monitoring of all our systems, services, apps, and whatever else we secure and monitor. 

Visualizations have become simpler with dashboards. We are getting visibility into systems, services, and apps stack through a single pane of glass, which is good. We are able to put logs in context.

It can have an artificial intelligence component. Even though I can seamlessly look at end-to-end security, it would be better to have alerts and notifications powered by an AI engine. I am not sure if they have an AI component. We have not reached out to them or looked at it, but this is something that I keep on talking about within our company in terms of features. Such a feature would be good to have, and it would further optimize my Security Ops team's abilities.

I have been using this solution for almost six months now.

It is stable. There is nothing critical about it. I've not heard of any significant issues in terms of operating this solution in the last six months.

We have only been using it for six months, and we haven't scaled it. Six months are nothing for such a solution.

We do monitoring as a service, and we have a hundred team members in the team. There are between 30 to 50 users who actively use it in some way.

We had Carbon Black. We didn't switch from Carbon Black to Datadog. Datadog was something different because of the visualization capability and bringing everything together. We acquired a couple of companies, and Datadog was being purchased. We just validated the purchase specification, features, and assessments. It was not a one-on-one sort of exchange of Carbon Black with Datadog.

It was easier than what we had been using in the past. It is a SaaS-based solution, and it was supposed to be a straightforward setup.

It is too early for that. I have not yet seen the impact on my budgetary lines or process optimization. I had ten people in my Security Ops team earlier, and I still have ten people. They are definitely happier as users than before, but what does that give to the organization is not yet clear to me. 

I would rate Datadog a seven out of ten. It is too early to say whether we are getting our money's worth, but we have felt the difference in terms of optimization and user experience.

We are using the infrastructure and app monitoring side, such as process monitoring. We are using it in a very traditional way. We are not using the APM capabilities. When it comes to something like containers, we will generally use it on the host but not inside the container itself. 

We are using it with our customers and in-house day-to-day.

It provides more cloud data. They tend to just get the way a service would be designed on the cloud. Datadog can handle a server disappearing and account for it, but they will kick somebody out. 

The ease with which we can filter, use metrics, and give accounts to customers, then let the customer filter, set up metrics, and alerts. This has been a big win for us. This can't be done with a lot of the other platforms. This has made things considerably easier. Where we used to get "What's my performance?" Here, have access. Go nuts. Tell us if you need it. Now, our customers no longer ask us for all that, as they want to go do it themselves. This has made our lives infinitely easier.

The only thing that they were missing that has throw us from the beginning (they are still missing it) is consistency in the APIs. There are a couple of guys on the automation side who complain rightfully over how hard it is because every new feature which comes out has a new way of interfacing with the API. This was our big, red flag in the beginning, but given the price and other features, it wasn't enough for us to discount. We said "That we would live with this one red flag", but it is still a red flag.

Stability of the product has been a concern for us outside of the primary monitoring agents.

It does not have the best interface.

We haven't noticed any issues in the primary use case for which we are using it. 

The reason we're not using or looking at the APM space right now is due to platform availability. Datadog doesn't support enough platforms, which they know. Every customer that we have is running PHP, and we cannot use APM with any of our customers because of that. Even if they are 95 percent running Java, if Datadog doesn't have PHP, we can't use it because it won't integrate.

Scalability has not been a concern at all. We have had customers with steady state loads: low and high. Our smallest customer is a friends and family startup which has about three instances. We have steady state loads which are more than 500. Then, we have customers with two instances all summer, but do seasonal work in the winter and can scale to more than 1000 instances. 

We have never noticed a hiccup on Datadog with any of our scaling. It has always grown to meet our program.

We have used technical support for certain integrations. We use a lot of Ansible and Chef, and we have had a lot of problems with both of these automating components. Technical support was helpful within their limitations.

We switched when we started getting heavy into the cloud. We used to use ScienceLogic, New Relic, AppDynamics, Zabbix, etc. It was hodgepodge. 

We were very strong in the APM space. We had all of our APMs going through AppDynamics, which suited a lot of our customer use cases in the cloud. However, when our customers started to get more specific, they wanted traditional core monitoring and the other on-premise traditional vendors, like ScienceLogic, weren't cutting it. That is when we started to look at Datadog. We went back and forth for a while between Zabbix and Datadog. In the end, Datadog won out based on feature price and everything together.

The integration with the AWS environment has been pretty seamless. There have been a few services that we don't use that they don't have book support for. However, usually that happens when it is a new service which is really unpopular. Most of the time, our customers shouldn't have been using that service to begin with, since it's a legacy thing that we inherited. I can't think of a single case where we haven't told the customer "You have to get off of that." 

It has saved us a lot of trouble in implementation.

The pricing came up a bit compared to their competitors. It is not that the price has risen, but that the competitors have gone down. They keep adding more features that I would have expected to be baked in at a more nominal price. I have been increasingly dissatisfied with the pricing, but not enough to jump ship. It is still pretty good.

Check the APIs very carefully. Without fail, this is the single biggest complaint for automation and operations. It is not that it can't be done. Just make sure that you have the technical expertise to work around it.

We use a mixture of both AWS and on-premise. There are actually three scenarios: 

1. Some of our customers purchase it for AWS. 
2. Some of them were accounts that we set up directly on Datadog for our customers. 
3. In some cases, customers already have a relationship with Datadog. 

Those are the three scenarios. Some have a mixture of scenarios due to regulatory reasons.

Observability is a key use case, as is security.

With this product, we get more visibility into our K8s clusters and more intelligent alerting.

Datadog dashboards are pretty great.

We need more integration with security tools like Drata.

I've used the solution for three years.