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.

Less than one year.

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.

Public Cloud

• 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.

Three to five years.

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.

One to three years.

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 this solution to monitor our Kubernetes clusters, nodes, deployments, daemon sets, replica sets, and pods.

I've been using this solution for a few months at my current company as a member of the Kubernetes team we use Datadog to provide monitoring and telemetry for our team and our customers.

This solution has improved our organization by giving us deeper insight into what's running in our clusters and their performance of it.

I have found the logging and tracing features the most valuable.

The documentation could be improved regarding setting up the agent properly and debugging.

I've been using this solution for a few months at my current company.

Private Cloud

Amazon Web Services (AWS)

The fact that its hosted so you don't have to rely on infrastructure to do the work is a very valuable feature.

At other places I've worked, we had to rely on different types of infrastructure -- Datadog does all that. Its very time saving. Datadog has alerting events and metrics all in one place; This was a huge plus, other solutions were trying to treat monitoring as a multi-faceted problem. Datadog treated it as one problem. Also, We no longer use Nagios for alerting, we use Datadog’s alarms, and then we push the data into PagerDuty.

The performance, especially when were drilling into metrics we've been running for a year and a half. When you're launching old data it can get slow.

We have been using the solution for the last year and a half.

No issues with deployment.

No serious issues with stability -- has been pretty great.

We pushed them sometimes, meaning when we would do something their platform didn't support. But they were always very cooperative and proactive.

Customer Service: Very good customer service.Technical Support: Hit or miss, always very proactive and friendly but could be faster sometimes. Almost always have the right answer, but sometimes could be a bit quicker.

At this job I've only used Datadog. Past jobs I've used New Relic, and assortment of others.

Very straightforward. We got Datadog running in a couple of hours, and deployed it across our production environment, We just installed StatsD, started sending metrics, and it just worked. Deployment was super easy and their setup and integration made us feel like it wasn't a vendor lock-in.

Initial cost was a couple of days of work, day to day nothing really.

Yes, we evaluated a bunch of option, including New Relic, as well as toying with the idea of hosting our own, a couple of others as well. We didn't want to manage our own, our corporate culture is to let other companies be the experts when possible. In the case of Datadog, the product exists -- made sense to let them be the experts.

I'm an advocate of Datadog -- go for it.