Datadog Reviews

The application performance monitoring is pretty good. 

The Log Explorer could be better. I don't think it has log manipulation as Splunk does.

I've only been using Datadog for the past one or two months. 

Datadog is stable. 

The initial setup is pretty easy. It's a kind of agent-based application monitoring. It's similar to Splunk and Elastic Stack.

Although I don't have that much experience with it, overall, on a scale from one to ten, I would give Datadog a rating of eight. 

We use it to monitor our infrastructure, particularly our different EC2 instances, and our containers. We also use it to capture our logs.

We have a better grasp of what is occurring during the deployment cycle. If something fails, we have an idea what has failed, where it has failed, and how it failed to better mitigate the situation.

It is a good one stop location where we keep all our data for our infrastructure, and it's also easier to navigate between different things.

We want to reduce having to go to different screens to obtain all the information. However, they are moving in the right direction from what we have noticed.

Stability has never been an issue. We throw all of our servers and containers at it. We have now started to throw our on-premise logs at it too.

At the beginning, when we started throwing logs at it, there was a bit of hiccup. However, this was during their beta period, so hiccups were expected.

It pretty much vacuums up any information that we throw at it. So, stability hasn't been an issue.

It scales depending on the time of year. Right now, we have about 25 to 50 instances, and in each instance there are probably five different containers, not including logging for all those containers.

We used their technical support, especially during rollout. They were really good. We worked hand in hand to try to figure out how to configure everything.

For the monitoring of different EC2 instances, you install them into Datadog. 

We use Chef to install Datadog's package, then that calls out all the information from the instance.

We did evaluate other vendors.

We chose Datadog because we were looking for an all-in-one package. They also do log caching and integrate with other systems well.

Take advantage of Datadog's trial period, and really beat it up, then give them a call.

We use the web service for this product.

Sometimes it's more user friendly for development teams. There are some parts of Datadog that are more understandable for development teams. For example, the APM in Datadog works more manually and works like the tools in New Relic or Grafana or Elastic. It is easier to understand for software development teams. 

It could use some additional features when working with metrics like Grafana or like New Relic has. Datadog does not use library technologies like Dynatrace does. Datadog has machine learning too, but it does not have this option in all layers of monitoring like infrastructure service process in applications. 

I've used Datadog for about one month. 

Datadog is a great tool, but it does not cover 100% of all libraries and technologies, like Dynatrace does.

I give Datadog an eight out of ten. 

We have a web infrastructure that uses Amazon Web Services containers with everything included, and we use Datadog to monitor them all.

The most valuable feature of Datadog is its logs. We can use the logs to conduct analysis and root cause analysis.

They could look into improving the integration.

I'd like to see better pricing and more integration in the next release.

I have been working with Datadog for approximately one year.

Datadog is a stable solution.

The scalability is good.

Datadog is currently used by 15 people in our organization.

We intend to continue and expand our use of this solution.

We contacted technical support, and they were extremely helpful.

The installation is reasonably straightforward.

We are a small organization. We have one administrator who is responsible to maintain and keep this solution up to date.

Our licensing fees are paid on a monthly basis.

It's quite expensive. They could improve the price by making it affordable.

I would recommend this solution to others.

I would rate Datadog an eight out of ten.

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.

• Monitoring
• Analytics
• Tracing
• APM

It's hosted. We don't have to do it, and they handle a large amount of data with backups and all of the other things that we no longer have to manage. 

There are things about it that we would like to be fixed, such as it is taking averages of average. This results in data that we don't expect, but overall we are happy with it.

It is incredibly stable.

We have had no issues with scalability.

We have needed technical support because we were dealing with averages of averages.

The integration and configuration are incredibly simple. The SaaS offering is remarkably easy to set up, especially if you're coming from a Graphite environment or anything that uses a StatsD. Datadog is a custom StatsD client, and it adds additional functionality, like tags, etc. However, out-of-the-box should work with native StatsD, so it is incredibly easy to drop in replace if you are using StatsD for metrics.

In terms of employee time: While the instructor costs were transferred to Datadog, it freed up our engineers to work on things which were of valuable to our business rather than maintaining a service that we don't make money on.

It costs the same amount it would if we were hosting it ourselves, so we are incredibly happy with the cost.

We did look at several vendors. What it came down to is we did not want to manage the metric services ourselves anymore, and Datadog matched what it cost for us to host it ourselves.

Check out Datadog. It is awesome.

The ingestion points are unlimited and support customization. We haven't had anything yet that we haven't been able to integrate with it.

We have only used the SaaS offering, but not AWS nor on-premise.

We mainly use it to send metrics about CV and memory usage, in addition to the number of files descriptors on a socket.

We are working as an SMS segregator. Therefore, we send a lot of SMS message to customers. This product holds one of the most important dashboards for our traffic from each server or cluster on our Gateway. It gives us very good information, mainly for the operations team and other sales guys, about what each account is sending, how often, etc.

Using the data, our operation teams works with the dashboards to get their statistics, analytics, etc.

The ability to create dashboards and matrices with graphs. This information is useful to us.

I would like testing for data in the future. That would be really nice.

Also, I would like some additional enhancement in the visuals.

The stability is good. For every message that we send, we have a corresponding metric. We send one to two million messages per server a day.

The scalability is good.

We have over a 1000 accounts on three servers. There are other servers, which work as a helper server. However, the servers which help aid traffic or do the heavy lifting are three main servers, currently. They are hosted on Amazon: large machines with large instances.

I have not used Datadog's technical support.

The AWS integration and configuration is pretty good. It has multiple languages and platforms.

Give it a try. It is a good tool for creating statistics and analytics with data. 

Anyone who uses a large amount of data and want insights on the analytics of their data. They can just dump into the tool, and it will do all the heavy lifting.

We use it for our infrastructure network and servers.

From our customer's and our perspective, it has not brought much change in terms of the process. Datadog did not change anything about the process. It only provided a single tool instead of having to use multiple tools.

Its logs are most valuable.

Its pricing model can be improved. Its settings should be improved for a better understanding of billing. They should also provide some alerts when there is an increase in usage. For example, if there is a 20% more increase from one week to another, the customer should get an alert.

I have been using this solution for two years.

It is pretty stable.

The perimeter in which we worked was not big enough to get any information about scalability.

Their technical support is fine.

One of our customers was using another solution. They switched because of the scalability of the perimeter. Their previous solutions were limited in terms of the kind of servers and virtual machines. They had multiple tools, and Datadog provided them a single tool for architecture and infrastructure.

Its initial setup is complex.

We installed it ourselves.

I am not satisfied with its licensing. Its payment is based on the exported data, and there was an explosion of the data for three or four weeks. My customer was not alerted, and there was no way for them to see that there has been an explosion of data. They got a big invoice for one or two months. The pricing model of Datadog is based on the data. The customer was quite surprised about not being alerted about this explosion of data. They should provide some kind of alert when there is an increase in usage.

We evaluated various in-house and external options.

I would advise others to take a step-by-step approach rather than a from-zero-to-everything approach.

I would rate Datadog an eight out of ten.