Datadog Reviews

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 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.

I'm a senior cloud security engineer and we are customers of Datadog. 

In terms of the public cloud provider integration of AWS, I would say it's very easy and straightforward to integrate. We can automate that way as well, because it also provides the cloud formation template and is a way to have a central place to monitor and visualize metrics in a multi-account structure. It's something we really need because the company has many AWS accounts. Rather than jumping from one account to another, Datadog gives us the functionality of having everything on one platform, in one place.

I believe there is room for improvement with this solution. It wasn't easy for me to get a quick understanding of what this tool offers us as opposed to the added tools of AWS. By that, I mean in regards to finding a better way to apply some filters or to create some alarms. I don't get more advanced features in comparison to AWS but at least I get a centralized way of doing things, which can be done on the AWS side as well. It's more complicated because you have to configure some other services to stream their logs from multi accounts to one account. It could be more user friendly and include advanced examples in the documentation showing some use cases or customer case studies, so you can get a clear idea that this functionality provides something extra. 

I've been using this solution for about a month. 

This is a stable solution. 

It's an SaaS solution, so it should be scalable although I don't know the architecture of it.

We have support from a technical engineer during the POC, which is still ongoing. It's amazing. Their customization and support during the POC include weekly meetings, with a follow up of any issues through email and Slack. 

The initial setup in regards to integration with AWS was very simple.

I would recommend this solution even though I don't have much experience with it yet. The company is currently using New Relic and we are now investigating Datadog for two reasons; the cost and also the integration with microservices and Kubernetes. I feel like this is a good solution. 

There is some room for improvement, so I would rate this solution an eight out of 10. 

We use it for notifications, alerting, and capturing most of the information from Amazon, such as EC2 instances.

It has enhanced the performance of my team.

The product could do better with its notifications. 

I want more technical support than conferences because technical support helps with setting up the product much easier.

So far, it has been pretty stable. After we stand up and configure it, it works well.

We have managed to get up to 350 hosts in one of the clusters, and it works fine.

Datadog's support is pretty good.

The integration and configuration of the product in our AWS environment was easy. This was one of the many things that I liked about Datadog.

I have not seen ROI out.

We chose Datadog over the other products that we evaluated because it had better features: notifications, alerting, and metric capture. Also, Datadog had the skill sets that we wanted at the time.

Try out some of the other products in comparison. This is a good product if you are looking for notifications and custom metrics.

We have always used the cloud version of this product.

This product also integrates with Slack and PagerDuty.