Datadog Reviews

The primary use case is application monitoring. We also use it set custom metrics and watch our AWS metrics, as well as data.

At my current job, I have only use it a couple months. However, I used it for a few years at a previous company.

It lets us react more quickly to things going wrong. Whereas before, it might have been 30 minutes to an hour before we noticed something going on, we will know within a minute or two if something is off, which will let us essentially get something back up and running faster for our customers, which is revenue.

Its most valuable feature is the monitoring, such as all the custom metrics that Datadog imports from AWS. In addition, the specific monitoring where you can set up an alert to a bunch of different services. 

Some of their newer solutions are interesting, like their logging, but they are not fleshed out. They could use more metrics or synthetics, which would be really helpful.

I would love to see support for front-end and mobile applications. Right now, it is mostly all back-end stuff. Being able to do some integration with our front-end products would be awesome.

It is very stable. Both times that I have worked with Datadog, we haven't had any issues with them going down. Or, if they did, we didn't know, which is good.

At the previous company that I worked at, we threw a lot at them all at once.

Because this is a newer integration, we are putting less stress on the tool. We are still working on integrating it into our platform.

It has scaled great. I haven't run into any problems anywhere that I've used it. They have handled everything that we have needed them to.

We are a 100 person company with 20 engineers.

The technical support is great. They respond quickly. They know what they are talking about and dig right in. If they don't know the answer, they can get it to us very quickly.

The integration and configuration through AWS was pretty smooth. It was easy to set up and start using. The documentation was clear. So, it worked really well.

We did the integration and configuration through AWS ourselves.

We haven't seen ROI at my current company. The solution is too new. 

At my last company, we did see ROI, specifically around response time. We could get to mission critical things that were down and losing revenue on immediately. So, the product paid itself back.

The pricing and licensing through AWS Marketplace has been good. It would be nice if it was cheaper, but their pricing is reasonable for what it is. Sometimes, for their newer features, they charge as if it's fully fleshed out, even though it is a newer feature and it may have less stuff than their other items. So, if they would scale the pricing appropriately as they add more stuff to it, that would makes sense. The pricing should reflect the abilities of the features.

We looked into self-hosting something, like Prometheus. We also evaluated New Relic.

We chose Datadog for its ease of use in getting set up and what they offered us.

Take the time to explore it and see all the metrics which are available. The metrics make the reporting better. Spend the time and learn the metrics. The things that they can send and give you are good. Learn how to aggregate them and how to write more complex queries, which they do a good job of showing how to do, but I found that newer people don't do this. They just try to use the baseline set of features. Doing the more complex stuff adds significant value.

We have PagerDuty integrated with it, as well as all of AWS. Those are the big ones we have running through it. It integrates well. It essentially replaces CloudWatch, so we can just use Datadog, which is nice. The biggest thing that they provide is putting everything in one spot.

I have just used the AWS version.

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 primarily use this product for availability and performance monitoring, log aggregation.

Datadog gave us awesome visibility across all of our applications.

The most valuable features are logging, the extensive set of integrations, and easy jumpstart.

In the past two years, there have been a couple of outages.

We have been using Datadog for two years.

The outages that we have had in the past two years were fixed in a matter of minutes.

So far we did not have any issues with scaling, and everything is working great.

Support is awesome.

We did use NewRelic, but the logging feature was not as good as it is in Datadog.

The initial setup is straightforward and everything is very well documented and easy to start using.

We implemented it in-house.

We evaluated a custom ELK solution, Sumo Logic, and Logentries.

Datadog is already covering much more than we normally need with exceptional quality. This is a great product.

We use Datadog for application logs, error tracking, performance tracking, alerting, and overall production state surveillance. 

It helps us improve observability and ease of maintenance through better information for our support teams and their issue qualification. 

We also use dashboards to keep all the information at ready and easy to access. SLOs notably for our uptimes but also our feature usage. It also feeds our alerting for our on-call SREs into PagerDuty by launching alerts when specific parameters are exceeded.

Our usage of Datadog has allowed us to improve our observability at great lengths. We have been able to track pain points more easily with it, and be able to define custom metrics to track our user's usage of the features we roll out.

Being able to generate dashboards has given higher management a better view of our teams' work and has allowed for better client information by our sales team as they have a more transparent way ofdealing with our upcoming features.

Dashboards and their versatility are among the most valuable features. They allow us to have internal facing trackers of our application's issues, usages, and features. They also allow us to have a better understanding of how users react to new features, and to display more information to other teams or also clients through uptime SLOs, et cetera.

We also found the Synthetics Tests and especially the Browser Tests very helpful. It is a nicer way to create end-to-end tests in a more user-friendly way than through code. They are very valuable in saving time compared to code-based testing.

Documentation is also very clear and interesting.

We would like to see some versioning system for the Synthetic Tests so that we could have a backup of our tests since they are time-consuming to make and very easy to damage in a moment of error.

I look forward to seeing the next features that will be released.

I have been using the product for a year and a half. The company has been using it for longer. I don't know the exact details.

We have yet to have a large-scale problem with stability using Datadog. It's very satisfying.

The scalability is very good.

I've had only a few experiences with customer support, and it went well. They were fast!

Positive

We did not use a different solution previously.

I wasn't there for the initial setup.

I wasn't there for the initial setup.

I cna't speak to the ROI.

I don't give advice regarding that.

I wasn't part of the decision-making process.

It would be nicer if the pricing information was easier to find in the documentation. Sometimes it helps to get an overall idea of the cost of certain options.

I primarily use the solution to learn, watch and monitor business and engineering metrics in the production and QA environments of my team. 

We create monitors on key business metrics and observe regressions and anomalies.

Less often, I leverage the events ability in Datadog to get notified about significant activities happening in my teams' deployments.

We learn about Datadog monitor alerts through Slack and often attempt to create SLOs using Terraform.

We use APM for observability.

Most recently, I learned about WatchDog Alerts that I will be heavily looking into.

Datadog simplified my ability to watch easily and add monitors on any metric emitted by any team at my organization.

Datadog APM immensely improved our ability to understand the reasons behind production issues. Its ability to navigate across services seamlessly to understand the time spent at each critical stage of a production request is helpful. This, combined with Datadog's historical ability to show business metrics aside, helped get more powerful insights much more quickly.

Datadog's seamless integration with Slack and PagerDuty helped us to receive alerts right to the most common notification methods we use (our mobile devices and Slack).

The most valuable aspects include:

• The ability to monitor any team's metric in my company (transparency)
• The ability to create/clone dashboards for myself (ease of use)
• Its integration with Slack (it is very powerful)
• The ability to add monitors on any metric emitted by any team at my organization
• (Through Datadog APM) the ability to understand the reasons behind production issues. Its ability to navigate across services seamlessly in order to understand the time spent at each critical stage of a production request is key. This, combined with Datadog's historical ability to show business metrics aside, helped me get more powerful insights much more quickly.
• (Through integrations like Slack and PagerDuty) the ability to receive alerts right to the most common notification method we use (our mobile devices and Slack), which saves a lot of time and helps us maintain focus. 

I would like better navigability across pages. The UI/UX is powerful, yet less intuitive. A lot of times, I somehow navigate across buttons and pages, and I end up forgetting how to get back to a particular view that was more insightful. 

Particularly as Datadog starts offering more platform capabilities like APM, Watchdog, Shift left initiatives like instrumentation, continuous testing, intelligent test runner, and Synthetic and real user monitoring, the UI can become more and more clunky, giving users a very frustrating experience. 

I've used the solution for five to six years.

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.

Datadog is expensive. 

The tool's deployment is easy. 

The solution's pricing depends on project volume. 

I rate Datadog a seven out of ten. 

Datadog has clear dashboards and good documentation. 

The solution needs to integrate AI tools. 

I avail support from our internal team. 

I rate Datadog a nine out of ten.