Datadog Reviews

One of the things we use it for is the same thing that we use FullStory for, which is to replay customer interactions with our platform. However, it also does the monitoring. It's like monitoring cloud tools. We're really mostly monitoring our own software to make sure that everything is functioning properly. We can check a bunch of things, and we can even play back customer sessions. It’s basically monitoring our application.

It really provides a lot of visibility in terms of how our software is working. If there are any problems, it surfaces them right away. We get alerts in Slack. It's really an essential tool for a company that provides software as a service.

I really like the replay, the ability to replay sessions, as I'm in sales engineering, so I sometimes need to know what my prospects are doing during a proof of value. I can actually see all the mouse moving and clicking on buttons and stuff like that. I can actually tell what they've been doing. There’s a lot of the other monitoring stuff as well. The development team uses it for monitoring and finds it very helpful.

It’s been kind of in the middle of many different things. The dashboards and the performance of the software have been great.

I haven't really noticed anything that they could improve upon. Maybe they could add in some features to go both ways, to maybe make some configuration changes, etc. That's a little bit outside of what Datadog does, though. It's really very full-featured, so I don't really have any complaints.

I haven't really fully looked at the documentation as I know where I need to go and look at things. It could probably be a little bit of a better user experience. There are so many functions there that sometimes navigating your way around is a little bit hard. They have a really nice menu system. However, there's so much there. It's possible that I skipped a guided tour when I started.

It’s not intuitive to everyone. There are a lot of technical features.

I’ve been using the solution for the last five months. However, the company may have used it for a year and a half.

The solution has been stable and reliable.

We haven’t had a problem with scalability. It’s been good.

We have 25 to 30 users on it currently. Our entire organization is under 60 people. Although not everyone is on it, a lot of our staff are. The sales, engineering, and customer success teams are all on it.

We may increase usage. No doubt that will come naturally with time. We’re hiring more people, and likely new hires will use it.

I have not had occasion yet to reach out to support.

We’ve also been using FullStory.

I wasn’t part of the implementation. The one thing I will say is that when they added the functionality to review sessions, it made our use of another product, FullStory, almost obsolete. I'll have to see if we will continue using FullStory or if we can rely completely on Datadog.

I am a customer and end-user.

We’re on the most recent version and keep it updated.

I’d rate it nine out of ten. The user experience could be slightly better.

Our primary use case is log management and we also use the solution for monitoring the application and underlying infrastructure. I'm an IT test manager. 

I appreciate that they are constantly adding new features, some of which we haven't yet had a chance to implement. 

I'd like to see more flexibility in the customization and they have a few settings which need to be changed but we are unable to make those changes as users or as the administrator. The tagging to get the different parts of the monitoring interconnected is a bit tricky and takes time to work out. 

I've been using this solution for 18 months. 

The stability is good. 

I would say that the amount that we are monitoring is not that large and we've never had any scalability issues. We have around 50 users in our department. 

The availability or accessibility to customer service is not always good, although they generally provide solutions once you do manage to get hold of them. 

We have previously used different tools for different parts of the monitoring. We changed to AWS when we moved to the cloud. We also found that the effort in maintaining Grafana and Prometheus and keeping it up to date was taking too much time.

The initial setup was straightforward, we used a service provider and they also maintain our operation in general.

We have a four-year contract with Datadog, and the solution is pay-as-you-use. 

I would suggest using the documentation, which is quite good. It's best to start with existing integrations, and then do the customization step-by-step.

I rate this solution eight out of 10. 

We are evaluating Datadog for observability and monitoring requirements that we have in our company. In our use case, our intention is to provide some kind of framework for multiple app teams to use the tool for our cyber ability and engineering practices.

They have a very good foundation in capturing metrics, logs, and traces. It's a very nice tool for that and it allows you to apply these monitoring tools in almost any technology.

Even if you have several layers, containers, EC2 instances, build machines or whatever you need in your infrastructure, Datadog can integrate with all of them across multiple cloud providers. It's a great product.

One of the improvement opportunities that we have identified in my project concerns how hard it is to manage an organizational structure when you have multiple things in one organization, and you want to provide some kind of isolation between them. At the same time, from the management perspective, you want to see an overall overview of what is happening in your business unit, or as a whole division. This is the kind of limitation we're facing.

I'm not sure what kind of features are in the roadmap right now, but I encourage the development of features for defining your organization, and allowing the visibility of what kind of metrics you can get. Those features would be really useful for us. 

I have been using Datadog for about six months.

It's a very scalable product. Right now we are using the SaaS version, so we don't need to worry about the infrastructure or whatever is needed for the platform it is running on. All the capturing of data is sent to the SaaS product and that can be as scaled as needed.

So far their support is pretty nice. They have established many meetings and training sessions, and they are supporting our requirements very well. I don't have any complaints with Datadog support.

While it is an expensive product, I would rate the pricing level at four out of five. 

Normally, the primary reason why people use these kind of tools is observability, but right from the beginning you have to understand what observability is, what it means for your company, and how the tool is going to help you to capture the proper metrics for making your applications observable.

We have used this solution primarily for application performance monitoring. To do this, we needed to make sure we had the right data in the system so that people could be able to monitor their applications end-to-end.

The infrastructure monitoring capabilities are really valuable. You can just log on and see everything that is happening within an IT environment.

Their security features could be improved. We looked at their Security Monitoring feature but it was early in its development. Datadog are just getting into the security space so I'm sure this will improve in the future.

This is a stable solution. 

This is a scalable solution. 

To get started with this solution, I would recommend front-loading it with some sort of data or process and filter out to view only the information you need.

I would rate this solution a nine out of ten. 

We implement these solutions for our clients. We have implemented Datadog as an SIEM solution.

Because of our client focus, it is easy for us to sell. This is because it is easy to use and easy to set up.

While I like the ease of use, when compared with Tenable Nessus they could still improve their usability. They are okay, but there is room to be better.

They could have more integration.

They could be more intuitive as well. For example, the intuitivity of the user interfaces, and how long it takes for users to learn how to use Datadog.

It is not impossible to use, or impossible to do the administration with it but when you put these two next to each other, meaning Nessus and Datadog, Nessus comes out as the winner.

I have been using Datadog for two years.

We are not using the latest version, we have missed at least one update.

We have no issues with the stability of Datadog.

Datadog is a scalable product.

We have two customers who are using this solution.

Technical support runs 24/7. The technical support is absolutely fine.

We are also using Nessus. My experience using Tenable Nessus is better.

It is easier to install than to use it.

I was not the one doing the handling the installation. I'm a senior consultant, and I was coordinating, planning, and interacting with clients. But the actual installation, I was not involved with. 

The installation could be done in an hour or so.

It is not complex, two professionals are enough to complete the installation and maintenance of Datadog.

With Datadog, it's a monthly fee. They prefer monthly subscriptions.

I would recommend this solution for medium enterprises with 100 to 1,000 employees. 

Small business is too small for the way that Datadog operates. It is not the best for very large enterprises for a company with more than 1,000 employees.

I would rate DataDog an eight out of ten.

We deploy agents on-premise to collect data on on-premise VM instances. We don't use Datadog in our cloud network. We do have some Cloud apps that we have it on and we also have Containers. We have it on their headquarters, the main software for them is on their own Cloud.

Eventually, we're building out the process now and using it better. We plan to use Datadog for root cause analysis relating to any kinds of issues we have with software, with applications going down, latency issues, connection issues, etc. Eventually, we're going to use Datadog for application performance, monitoring, and management. To be proactive around thresholds, alerts, bottlenecks, etc. 

Our developers and QA teams use this solution. They use it to analyze network traffic, load, CPU load, CPU usage, and then Tracey NPM, API calls for their application. There are roughly 100 users right now. Maybe there's 200 total, but on a given day, maybe 13 people using this solution.

It hasn't improved the way our organization functions yet, because there's a lot of red tape to cut through with cultural challenges and changes. I don't think it's changed the way we do things yet, but I think it will — absolutely it will. It's just going to take some time.

I like that you can build out a dashboard pretty quickly. There are some things that come out of the box that you don't really need to do, which is great because they're default settings. Once you install the agent on the machine, they pick up a lot of metrics for you that are going to be 70 or more percent of what you need. Out of the box, it's pretty good.

I have been using Datadog every day since September 2020. I also used it at a previous company that I worked for.

Stability-wise, it's great.

It seems like it'll scale well. We're automating it with Ansible scripts and service now so that when we build a new virtual machine it will automatically install Datadog on that box.

The tool itself is pretty good and the customer service is good, but I think they're a growing company. I think better access to their engineers when we have a problem could be better. For example, if I asked the question, "Hey, how do I install it on this type of component?" We'll try to get an engineer on the phone with us to step us through everything, but that's a challenge because they're so busy.

Technically-wise, everything's fine. We don't need any support, everything that I need to do, I can do right out of the box. But as far as, in the knowledge of their engineers on how to configure it on given systems that we have, that's maybe at six because they're just not as available as I would've hoped.

We were using AppDynamics. Technically, we still have it in-house because it's tightly wound into certain systems, but we'll probably pull that off slowly over time. The reason we added Datadog and eventually we'll fully switch over is due to cost. It's more cost-friendly to do it with Datadog.

Yes, we looked at Dynatrace, AppDynamics, and New Relic. Personally, I wouldn't have chosen Datadog for the POC if it were up to me. Datadog was a leader, but New Relic was looking really good. In the end, the people above me decided to go with Datadog — it's a big company, so they wanted to move fast, which makes sense.

If you're interested in using Datadog, just do your homework, as we did. We're happy so far I think; time will tell as we are still rolling things out. It's a very good company. It's going to be a year before we really can tell anything. If you do your homework, you'll find that if you're really concerned with cost, it's good.

There are some strengths that AppDynamics and Dynatrace have that Datadog I don't think will have down the road, but they're not things we necessarily need — they're outliers. It would be nice to have them, but we can manage without them.

Know what you want. There is no need to pay for solutions like Dynatrace or AppDynamics that are more expensive or things that are just nice to have if you don't absolutely need to have them. That's something people need to understand. You just have to make sure you understand what it is that you need out of the tool — they are all a little different, those three. I would say to anybody that's going with Datadog: you just have to be patient at the beginning. It's a very busy company right now. They're very hot in the market.

Overall, on a scale from one to ten, I would give Datadog a rating of eight. It does what we need it to do, and it seems to be pretty user-friendly in terms of setting things up.

Features-wise, I'd give them a rating of ten out of ten. The better access we get to assistance from the engineers on how to configure dashboards and pulling metrics that we need, that would bring it up a little bit. So overall it would be harder and it would have to be perfect for it. I would say maybe they could bring it to a nine.

We primarily use Datadog for logs, APM, infrastructure monitoring, and lambda visibility.

We have built a number of critical dashboards that we display within our office for engineers to have a good understanding of the application performance, as well as business partners to understand at a high level the traffic flowing through the app.

We started with logging, as our primary monitor, and have shifted to APM to get a deeper understanding of what our system is doing, and how the changes we are making impact the apps.

Datadog has given us near-live visibility across our entire cloud platform. We are finally in a state where we are alerting our users about degraded performance well before the helpdesk tickets start rolling in.

We are making major architectural decisions based on the data we are getting from Datadog. It also gives us an idea of where the complexity really lies in some older, monolithic apps. 

We have used the APM endpoint monitoring to prioritize work on slower endpoints because we can see the total count, as well as the latency. That has been a big driver in our refactor work prioritization.

We have struggled to get more business-centric measures in our code to surface actual business values in our reports, but that is our next initiative.

We started with Log analytics in the beginning stages of our monitoring journey. Those were very insightful, but obviously only as useful as we made them with good logging practices.

The dashboards we created are core indicators of the health of our system, and it is one of the most reliable sources we have turned to, especially as we have seen APM metrics impacted several times lately. We can usually rely on logs to tell us what the apps are doing.

APM and Traces have been crucial to understanding how users are actually using the app. That drives a lot of our decisions around refactoring and focusing our limited engineering resources.

Continued improvement around cost and pricing model is needed. It is pretty complex and takes a fair amount of intimate knowledge to know exactly how turning on a single function is going to impact your bill, especially when you don't see the metrics for a day or two. 

We have recently had a number of issues with stability and delays on logging, monitoring, metric evaluation, and alerts. More often than not in the past month, it seems that we get the banner across the to of our dashboards that some service is impacted. They don't always show up on the incident page, either.

We have been using Datadog for two years.

Overall, it has been fairly stable for us. There are the occasional issues with importing data, that has usually been resolved in a short time. We have never had an issue where that data was lost, just delayed, and eventually backfilled. 

It seems (anecdotally, of course) that there have been a few more stability issues lately. We have noticed several days that we are getting in-app alert banners indicating that some metric or log ingestion was delayed, or the web app itself was experiencing severe slowness. 

Overall, these issues are resolved rather quickly - kudos to their engineering teams. I hear that they actually use Datadog to monitor Datadog. 

Datadog is very scalable but just watch the cost.

Technical support is hit and miss; there are a number of nuances to how this tool should be implemented, and it is difficult to re-explain how our infrastructure and applications are set up every time we need an in-depth investigation to understand what is broken.

Previously, we used AppDynamics. The pricing model didn't seem to fit with actual cloud spend. Now we may have swung the pendulum a little too far, and seem to be dealing with pricing on every facet of the application. 

The initial setup was pretty straightforward. Additional tweaks and configuration have been a bit more difficult as we get deeper and deeper into the guts of the integrations. Making sure we are keeping up with a rapid release schedule, and keeping our server clients in sync with our app packages has been troublesome. There have been some major changes in the APM that have introduced a number of bugs and broken some of our dashboards and alerts.

Our in-house team handled the deployment, with a lot of tickets created for the Datadog team.

ROI is difficult to measure completely. Our first year spend compared to our second and now going into the third year spend have been significantly different.

My advice is to really keep an eye on your overage costs, as they can spiral really fast. We turned on some additional span measures and didn't realize until it was too late that it had generated a ton.

Frankly, we love the visibility it gives us into our applications, but it is a bit cumbersome to ensure we are paying for the right stuff. Overall, the cost is worth it, as it helps us keep system-critical applications up and running, and reduces our detection and correction times significantly.

We evaluated Dynatrace and AppD before choosing this product.

Datadog requires pretty close supervision on the usage page to ensure you aren't going out of control. They have provided a bunch of new features to assist in retention percentage, but it can be a bit confusing on what is being retained, and what can be viewed again after triggering an alert. It's a difficult balance of making sure you are getting the right data for alerts, and still having the correct information still available for research after the fact.

We were in need of a cloud monitoring tool that was operationally focused on the AWS Platform. We wanted to be able to responsibly and effectively monitor, troubleshoot, and operate the AWS platform, including Server, Network, and key AWS Services.

Tooling that highlighted and detected problems, anomalies, and provided best practice recommendations. Tooling that expedites root-cause analysis and performance troubleshooting.

Datadog provided us the ability to monitor our cloud infrastructure (network, servers, storage), platform/middleware (database, web/applications servers, business process automation), and business applications across our cloud providers.

Datadog provided us the tooling to help us effectively monitor, troubleshoot, and operate the AWS platform, including Server, Network, Database, and key AWS Services. It highlights detected problems and anomalies and provides best practice recommendations, expedites root-cause analysis, and performance troubleshooting.

Datadog provides analytics and insights that are actionable through out-of-the-box visualizations, dashboards, aggregation, and intuitive searching that shortens the time to value and account for our limited time & resources we have to operate in production.

The most valuable feature is the dashboards that are provided out of the box, as well as ones we were able to configure. Specific Dashboards that were provided that made things easier were EC2, RDS, Kubernetes dashboards.

We also use the logging tool, which makes searching for specific error logs easier to do.

Datadog Logging provides the capability for us to use AWS logs such as VPC Flow Logs, ELB, EC2, RDS, and other logs that provide lots of relevant operational data but are not actionable. Datadog provides a tool that can provide us analytics and insights that are actionable for visualizations, dashboards, alerting, and intuitive searching.

More pre-configured "Monitor Alerts" would be helpful. Datadog's knowledge of its customers and what they are looking for in terms of monitoring and alerting could be taken advantage of with pre-canned alerts. They have started this with "Recommended Monitors".  That feature was very helpful when configuring our Kubernetes alerts. More would be even better. 

Datadog tech support is very good. One area that could be more helpful is actually talking to someone or sharing your screen to help troubleshoot issues that arise. For new cloud engineers just coming into the cloud monitoring field, there is a learning curve. There is a lot to learn and figure out. For example, we still ran into some issues configuring the private link and more videos of how to do things could be of use.

We have been using Datadog for one year.

We have not run into any issues with stability.

The scalability of Datadog is very good.

Customer service has been excellent.  I communicate weekly a Datadog Customer Success Manager.  He helps me followup on any open issues or questions that we may have.  Technical support has been very good. Opening tickets is easy.  Sometimes a Tech Engineer may take a bit of time to get back with you.  Communicating with Tech Engineer has to be done via ticket/email - no phone assistance is available.

we did not.

Procedures for setup seemed straightforward but once you got going, there were some issues. For us, getting our private link to work needed additional tech support. They were able to help us resolve the issue we were experiencing. I think the procedures could be done a bit better to help you with setup.

We deployed it ourselves.

Datadog helps us minimize downtime and helps us resolve issues quickly.  

Pricing seemed easy until the bill came in and some things were not accounted for. The issue may have been that we didn't realize what was being accounted for, such as the number of servers and the number of logs being ingested.

Datadog had really good pre-sale reps that work with us but need to make sure all the details are covered.

The solution we were looking for needed to provide out-of-the-box capabilities that shorten the time to value. We had limited time & limited resources. Datadog had high recommendations in these areas, so we decided to do a trial with them.

We are very pleased with Datadog overall.

Datadog has assigned an account rep to us that meets with us regularly to make sure all our needs are being met and help us get answers to any questions or issues we are running up against. They have been of great helping us standup monitoring of our Kubernetes environment.