External reviews are not included in the AWS star rating for the product.
My main use case for Datadog is application monitoring.
Specifically for application monitoring, we monitor our production Laravel instances using APM spans and tracing.
In addition to application monitoring, I also use Datadog to monitor our log management for our applications that are both on-prem and in the cloud, as using the AWS integration.
In my experience, the best features that Datadog offers us include unprecedented visibility and the ability to dive deep on application debugging.
Datadog's visibility and debugging features help me day-to-day; specifically, we had an application that was throwing a bunch of errors causing an issue in our production database. Using Datadog, we were able to immediately isolate the error and plan around it.
Datadog has positively impacted my organization. I think it has given us not only the specific debug and error codes that we're looking for, but it has changed the entire company's mindset in how to extract value from data that's been lying around in our internal systems for years now and given everybody a new perspective on monitoring and debugging.
Since adopting Datadog, I've noticed specific outcomes. We've begun to handle our log management internally in a more efficient manner, so we've actually reduced our disk space as simplified our backup procedures and process chains using Datadog. Now that we have extracted the value from the logs and the traces and the debug logs, we no longer have to rely so much on traditional text-based logs or even digging into the code and the error files themselves.
The only improvement I would to see with Datadog is that the graphical user interface sometimes takes a little bit to load, especially when diving deep on a subject, and just a little bit more caching would help.
The largest pain point we've had with Datadog to this point was onboarding. This was partly our fault because our logs weren't really set up to be used in a modern observability platform Datadog, but I definitely would have liked to have seen more comprehensive onboarding. We had a few appointments, but the more help we get up front, the easier it is for us to get more familiar and do more things with Datadog.
At this time, I do not think there are any other improvements Datadog needs that would make my experience even better.
I have been using Datadog for approximately four months now.
Datadog is very stable.
We have not yet hit the use case to evaluate Datadog's scalability, but based off of everything else we've used with the infrastructure, I don't think there are going to be any issues with it. We did, as a trial, engage the AWS integration, and immediately it found all of our AWS resources and presented them to us. In fact, it was talking about costing and billing which we had not anticipated, but we were pleasantly surprised with.
Customer support is excellent; I have opened and closed probably five tickets in the past few days, specifically within the past seven days. Very responsive, and the support techs are knowledgeable and responsive.
I would rate customer support an eight out of ten. The only issues that we had were really needing more educational resources to begin with to truly understand the specifics of log management and APM tracing setup, simply because those are very complicated procedures. Walking through that a couple more times with the support engineer probably would have been helpful. It was not a deal breaker or a significant pain point, but the quicker we get up with Datadog, the happier, the quicker and deeper we get with Datadog, the happier people seem to be at our organization.
Overall, the entire Datadog comprehensive experience of support, onboarding, getting everything in there, and having a good line of feedback has been exceptional. I've been in the industry over 20 years, and part of my roles has always been customer-facing. I find that Datadog's client support is very engaging, comprehensive, and thorough.
Positive
For on-prem infrastructure monitoring, we're currently using Nagios, but that's beginning to fade as we rely more on Datadog for our infrastructure monitoring. We had used New Relic for application performance monitoring, but because of the cost associated with that and not seeing the value from it, we stopped using that about two years ago.
We did not purchase Datadog through the AWS Marketplace; we were contacted independently by a Datadog sales agent.
My experience with pricing, setup cost, and licensing has been overall fairly positive. The on-demand/reserved pricing, we were not as cognizant as to how big the on-demand could get, especially when we were getting everything set up, but Datadog proactively took a strong hand in guiding us to getting our costs under control. I'm proud to say that we are within 1% of our projected cost budget, so that was very handy and that's happened in the last month. Very efficient and very effective working with Datadog to control cost.
In terms of time saved, I've noticed that when we're responding to potential errors or during our software deployments, it's saving us minutes at a time that quickly add up to hours, that quickly add up to days in terms of retrieving debug and application error information.
Before choosing Datadog, we evaluated other options including New Relic and SolarWinds.
I would advise others looking into using Datadog to evaluate it against other competing properties and applications in the space, and really dig in. You will find that Datadog does what it's supposed to do very quickly, very efficiently, as does it more cost competitively than some of the other offerings.
Datadog is deployed in my organization in both on-prem and in public cloud scenarios.
On a scale of one to ten, I rate Datadog a nine overall.
I think the most important feature for me in Datadog is the RUM features.
I check the efficiency of the applications that I'm supporting in Datadog and also use it to view the sessions of users.
I have some trouble doing troubleshooting in our app currently, but RUM is my main use case in Datadog.
The personalized dashboards and alerting in Datadog stand out to me, so that way you can gear your use of the product towards what's important to you.
Datadog has allowed us to ensure that we can look at how our beta testers are using our new UIs and seeing where their frustration points are, which has been important to us.
We've been using the heat map feature in Datadog to measure those frustration points.
Some templates for certain roles and things that users care about could be auto-suggested for a dashboard or alerting in Datadog.
We had limitations around RUM and our feature flag provider in Datadog because it's a back-end forward feature flag usage in our Next.js application. We had trouble hooking up our feature flags due to RUM being client-side only. This issue arose because Next.js is a front-end and back-end focused application, and it would be beneficial to send the feature flag resolution from the back-end if needed. Our feature flag provider is GrowthBook, and the way we would have to get those feature flags into Datadog was time-consuming with a lot of boilerplate. We would have to mimic feature flag resolution on the client side, so we decided to forego that.
We have been using Datadog for about two or three months.
Datadog seems stable in my experience without any downtime or reliability issues.
Datadog is scalable and I don't think we'll have problems with scalability in terms of our use case. We might face limitations with logs, but I feel we would not be reaching any of Datadog's limits.
The customer support has been one of the best parts of Datadog.
I would rate the customer support from Datadog a 10 on a scale of 1 to 10.
I would suggest staying in close contact with your customer support representative to get the most out of Datadog.
Positive
We did not have a different solution before Datadog.
Setup with Datadog was pretty easy.
It is too early to tell if we've seen a return on investment so far with Datadog.
I'm not clear on pricing, but it's not a huge concern for us at the moment in terms of RUM. For the other pieces, I know that there may be some pricing that they've been looking at for APM and logs.
I did not evaluate other options before choosing Datadog.
I personally don't use the personalized dashboards and alerting, but I've seen some nice use cases from others on my team. On a scale of 1-10, I rate Datadog an 8.
My main use case for Datadog is application monitoring and alerting.
A specific example of how I use Datadog for application monitoring and alerting is monitoring for storage filling up.
I also monitor services to ensure that they're running when they should be, and then I schedule downtimes for whenever they shouldn't be.
In my experience, the best features Datadog offers are integrations with ServiceNow and PagerDuty and the large variety of other third-party integrations.
The integrations with ServiceNow and PagerDuty have helped my workflow because whenever there's an issue, we can get notified quickly, and whoever is on call, if it's after hours, can be notified that there's an issue going on.
Dashboards are nice for quick and easy access to important and useful information, and logs are a great place to review information quickly and easily without connecting to the application directly.
Datadog has positively impacted my organization by allowing for a more proactive response to issues whenever they occur.
Being more proactive has helped by reducing downtime and improving our response to resolution. It has helped us limit business impact whenever there are issues that arise.
I believe Datadog could be improved because sometimes it's not the most user-friendly, and when monitors have a new metric or a service that no longer needs to be monitored, it remains in the system. It could be user error, but it would be nice to remove a specific service or part of a monitor from continuing to be monitored if there's no data being collected anymore.
Documentation sometimes is a little misleading or confusing, and there are multiple versions available, so having more up-to-date or clearer documentation regarding which version it applies to would be good.
I have been using Datadog for two, two and a half years.
Datadog is stable.
Datadog's scalability has been pretty scalable from what we've done in our organization.
The customer support is very good; it's easy to get support on pretty much any question that we have, including being able to chat in.
Positive
We previously used LogicMonitor, and I was not involved in the discussions on why we switched.
It's a pretty steep learning curve to start using Datadog; it takes time to really configure everything.
I would say we have seen a return on investment, but I don't have any relevant metrics.
My experience with pricing, setup cost, and licensing is that it was good; I wasn't too involved with it, but as far as I know, it was smooth.
Before choosing Datadog, we did evaluate other options, but I'm not sure what those options were.
On a scale of 1-10, I rate Datadog an 8.
My main use case for Datadog is monitoring and collecting metrics. I use it to collect metrics from Kubernetes pod CPU and memory usage, and also logging, basically all our middleware platforms.
The best features Datadog offers are the ability to collaborate between different metrics such as logging, metrics, and APM, which helps me to pinpoint when I'm troubleshooting issues. The dashboard is very useful; I can use it to get a glance on how the system performs, and alerting is what I'm using right now to send notifications to either email or PagerDuty.
Datadog has positively impacted my organization by shortening our time to resolve incidents because it's a central place for getting all the data that we need for troubleshooting.
I think Datadog can be improved by adding anomaly detection, that would be nice. The user interface is okay, but sometimes cost is the issue because for logging, I had to actually trim down my logs because the cost is too much.
I have been using Datadog for several years.
Datadog is stable.
Datadog's scalability is quite good since it's a SaaS solution, and there are no scalability issues for me. I simply install an agent for whatever new component, server, or host I want to monitor, and then I'm good.
The customer support is hit and miss. Sometimes they respond fairly quickly, but it depends on the person, and it may take a couple of communications for them to actually understand what I need.
Positive
I previously used some open-source solutions from other vendors before Datadog. The switch was made to get a better observability stack.
My experience with pricing, setup cost, and licensing indicates that the pricing is based on usage. When we adopt more, we get more, so everything is based on our desire to improve adoptability for the entire studio, then cost becomes a main issue.
Before choosing Datadog, I evaluated other options, including Dynatrace, which was approximately 10 years ago.
My advice to others looking into using Datadog is that if cost is not a concern, I would recommend them to use it. However, if they are sensitive or concerned about how much money they want to spend, then maybe Datadog is not the solution for them.
I would rate Datadog overall as eight out of ten, though I find it too costly.
Our main use case for Datadog is that we heavily rely on it for our infrastructure monitoring and application monitoring, including some of the browser-based application monitoring, which is RUM.
A specific example of how we use Datadog for monitoring is that we monitor our infrastructure CPU and memory utilization. Sometimes we see slowness and figure out CPU utilization was near the threshold, around 90-95%, which helped us to resolve the issue, underlying SQL problem, and that helped us to troubleshoot the issue.
In addition to our main use case, we also use RUM monitoring and synthetic monitoring, which really help us to look at our end-user sessions and proactively solve any slowness or errors spiking up.
The best feature that Datadog offers is infrastructure monitoring, where it can look at the CPU utilization, different process utilization, all the processes which are running, and alert us in advance if things are going beyond normal threshold.
I think everything about the features of Datadog is amazing. Datadog provides details up to the transactions. We can look at the transaction log too for the application, which is really helpful.
Datadog has impacted our organization positively since we were previously using AppDynamics and then we switched to Datadog. It has improved a lot in our alerting and monitoring in the infrastructure space and application space. We can monitor business transactions and take proactive action. It is really great to take actions on the issues before an end user reports it, which is a great advantage for us.
The world is moving toward artificial intelligence, so maybe we can have an inbuilt AI agent within Datadog, or maybe it exists and I have not used it.
The AI aspect would be great where we would not need to go and look at different transactions or different modules of Datadog, as AI can actually provide the data to us on Datadog UI. If we need more details, it could have a link to go to that specific module to look at more details for the application and infrastructure monitoring and alerts.
I have been using Datadog for three years now.
Datadog is stable for our organization, and we have not seen any downtime or issues so far.
Datadog's scalability has been great as it has been able to grow with our needs. As per our need, we are able to utilize different modules and there was never any need where we needed to scale anything else. We have limited our transition recording to 45 days, which helps. That is what our need is. It is really helpful and nothing additional is needed.
We reached out to Datadog only once to find out our AMI images, which we needed for our infrastructure as a code component, and it was a great experience. We got the required information and that helped us.
Neutral
Before Datadog, we previously used OpsRamp and also AppDynamics, and both of the tools we retired and moved to Datadog due to our enterprise approach to consolidate overall monitoring to Datadog.
I gave Datadog a nine out of ten because it is amazing. All the features and functionalities are amazing. The ease of implementation was a bit difficult for us for the database servers where we have different kinds of databases. We needed different kinds of agents to be installed, and that was a bit tricky for us. I think it is not on Datadog but it is about our complex infrastructure where we have a different set of infrastructure in place, so that created a bit of trouble during the implementation.
Since using Datadog, we have seen a return on investment with a lot of savings around infrastructure monitoring, and also on the people needed to monitor overall application and infrastructure on both sides. Previously we had thirteen contractors doing the monitoring for us, which is now reduced to only five. That is a huge saving.
We did not evaluate other options before choosing Datadog, we went with Datadog directly.
My advice for others looking into using Datadog is to keep exploring the tool and utilize the different modules and the different functionalities of features Datadog offers. There are multiple features and functionalities available with the Datadog agents which are really helpful and powerful to troubleshoot, alert, and monitor both applications and infrastructure.
So far, all the features I have used in Datadog are amazing. It captures all the logging information which I have, and I can include the links of Datadog transactions on my Splunk logs. It is integrated with Splunk and other platforms, which is great.
On a scale of one to ten, I rate Datadog a nine.
My main use case for Datadog is dashboards and monitoring.
We use dashboards and monitoring with Datadog to monitor the performance of our Nexus Artifactory system and make sure the services are running.
The best features Datadog offers are the dashboarding tools as well as the monitoring tools.
What I find most valuable about the dashboarding and monitoring tools in Datadog is the ease of use and simplicity of the interface.
Datadog has positively impacted our organization by allowing us to look at things such as Cloud Spend and make sure our services are running at an optimal performance level.
We have seen specific outcomes such as cost savings by utilizing the cost utilization dashboards to identify areas where we could trim our spend.
To improve Datadog, I suggest they keep doing what they're doing.
Newer features using AI to create monitors and dashboards would be helpful.
I have been using Datadog for six years.
Datadog is stable.
I am not sure about Datadog's scalability.
Customer support with Datadog has been great when we needed it.
I rate the customer support a nine on a scale of 1 to 10.
Positive
We did not previously use a different solution.
In terms of return on investment, there is a lot of time saved from using the platform.
I was not directly involved in the pricing, setup cost, and licensing details.
Before choosing Datadog, we evaluated other options such as Splunk and Grafana.
I rate Datadog an eight out of ten because the expense of using it keeps it from being a nine or ten.
My advice to others looking into using Datadog is to brush up on their API programming skills.
My overall rating for Datadog is eight out of ten.
My main use case for Datadog is performance monitoring, SLOs, and SLIs.
For performance monitoring, SLOs, and SLIs, we create objectives and indicators around user feedback and stakeholder feedback. We have weekly meetings to create backlog items to work on if things have elapsed and gone into the red based on our SLO definitions.
The best features Datadog offers are the analytics that are all associated with each other. RUM data associated with APM, trace data, and all of that, including information around inferred requests, has been super useful. Machine health data gives a complete picture of performance, which has been extremely useful for troubleshooting difficult problems.
Having all that associated analytics helps me in troubleshooting by not having to bounce around to other tools, which saves me a lot of time. I know that the quality of Datadog metrics gathered is enough to where I can rule things in and out. This basically goes for any web app; when asking why a web app is slow, first you look at the code. If the code looks good, then you look at the hardware or the database. Being able to rule all of those out with one tool with one set of requests is useful.
Datadog has positively impacted my organization by allowing us to gather complete data instead of looking all over the place at incomplete data and actually make pointed determinations for fixing issues. It has helped increase efficiency and saved time.
I don't know how Datadog can be improved as they are doing a pretty good job.
I have been using Datadog for three years.
Datadog is stable.
Datadog's scalability is good.
The customer support is good.
My experience with pricing, setup cost, and licensing is that it is really expensive.
I have not seen a return on investment.
My experience with pricing, setup cost, and licensing is that it is really expensive.
My advice to others looking into using Datadog is that it is good and they should use it.
I don't know if my company has a business relationship with this vendor other than being a customer.
On a scale of 1-10, I rate Datadog a 9.
My main use case for Datadog is application and portal monitoring.
For application or portal monitoring, we have several monitors set up that give us a heads up early when we believe there's a problem with end users getting to the applications that are available to them on the portal. Just yesterday, we were able to identify an error in code that was throwing thousands of errors a day, and it was very simple for us to actually find it using Datadog analytics on the error and the Watchdog alerts.
I don't have anything else to add about my main use case, other than the ease with which we were able to identify an issue that we previously, when we didn't have Datadog, might not even be aware of, but was consuming resources that it didn't need to.
In my opinion, the best features Datadog offers are flexibility and extensive support. It can be a little overwhelming since there are so many features that come with Datadog, and I'm just scratching the surface of that. I also appreciate the support that our representative has provided to us, coming on-prem, providing training, being available to answer questions, and the extensive knowledge base documentation that I have been referred to, which has been extremely helpful also.
The flexibility I mentioned shows up in my day-to-day work because traditionally, I was using SolarWinds to monitor infrastructure health, but the polling period is lengthier than we would like to see. Datadog specifically has real-time monitoring, and the alerts that we have configured are coming to us much quicker. We're able to address an issue sooner rather than later, and when it comes to reviewing .NET code or application configuration, I only had limited visibility, but with Datadog doing the analysis of the IIS logs and any other application logs, it's also opened up visibility to me so that I can assist a developer in identifying the area of concern or where a code could be more efficiently written.
Datadog has positively impacted my organization by helping us make our web portals more efficient. Our portals and integrations are extremely complex, and as we get the agent installed on more devices, it's really provided us visibility that we haven't had in my entire career with Ace Hardware.
I cannot provide specific numbers for the improved performance, but Datadog has identified issues that we have in our data source area. We have implemented additional indexes and have plans for breaking out complex queries that are pulling data across multiple data sources. We're in the crawl, walk, run phase, so right now we're identifying and prioritizing the things that need to be fixed. A few of the things that we've already addressed include adding additional resources to servers, and we have noticed improved performance. I know someone has the statistics; I just don't have them available to me at the moment.
At this point, I'm not sure how Datadog can be improved, but maybe some initial intense training from the vendor before setting us loose with the application is the only thing I can think of.
I think it would be helpful to have an administrative page right from the portal that gives us links to the application documentation. I have separate URLs to get to the various locations that I need to go to, but unless I'm just not seeing them, I have to go to separate URLs. I cannot get to some of the documentation and various other components from my company-specific portal.
Other than being restricted by cost, Datadog's scalability has been a little bit of a challenge to do the initial installation of the agent. We have upgraded all of our agents so that we can do the upgrades remotely, but the initial install is still a little time-consuming and a little clunky.
I think the customer support is great. I love the ability to send flares directly from the machine or device that's having an issue, and my tickets are always opened promptly. I usually get links to documentation about the specific feature or function that I'm trying to implement, and when I have additional questions, the ticket is updated with actual recommendations or suggestions pointing me in the correct direction.
Positive
We continue to use SolarWinds, although I can see the infrastructure monitoring component of SolarWinds being replaced with Datadog. We also used Catchpoint to run synthetic scripts from various locations throughout the country, and we use Pingdom for our e-commerce solution. We're trying to phase out Pingdom at this time with the help of Datadog engineers, and we have ceased using Catchpoint because we have created those synthetic scripts within Datadog.
At this point, I'm leaving the return on investment metrics to my manager and director. I'm just focused on getting it up and running, installed, upgraded, and helping to train other folks to use it. I know they're trying to keep metrics on all of those questions, but I'm just not focusing on that at this time.
I was not included in the pricing, setup cost, and licensing decisions, but I have needed to gain more information about licensing and individual feature cost projections. Everybody wants the agent installed, but we only have so many dollars to spread across, so it's been difficult for me to prioritize who will benefit from Datadog at this time.
I'm excited to learn more about the application and can't wait as my knowledge expands, all the exciting things that we might be able to do with the tool.
I rate Datadog an 8 out of 10, only because I haven't had the ability to explore everything that I intend to explore, and some of the more complex monitors that I want to create I'm just not able to intuitively do. But that might be on me and not the product. The complexity and my lack of knowledge related to all the features and how I can use them keep it from being a 10 for me.
I would advise others looking into using Datadog to do more training and become much more familiar with the product before going live with it. There are so many wonderful things that can be done with it that it's a little overwhelming to only attempt to configure those or investigate them when the product's already live.
I'm excited to continue to learn and explore the tool. It's giving me some insight into systems that I have not had for the past 17 years, so it's exciting to be able to see that and put it to use almost immediately.
The primary purposes for which Datadog is used include infrastructure monitoring and application monitoring.
The main use case for Datadog integration capabilities is to monitor workloads in public cloud, and those public cloud integrations that reached the public cloud metric natively were helpful or critical for us. We are not using Datadog for AI-driven data analysis tasks, but more cloud-native and vendor-native tools at the moment, and at the time when I was still in my last employer, we didn't use Datadog for the AI piece at all.
I find alerting and metrics to be the most effective features of Datadog for system monitoring. It was still cheaper to run Datadog than other alternatives, so the running costs were cheaper because it was SaaS and quite easy to use.
Datadog is only available in SaaS.
The pricing nowadays is quite complex.
In future updates, I would like to see AI features included in Datadog for monitoring AI spend and usage to make the product more versatile and appealing for the customer.
I have been using Datadog since 2014.
There were no problems with the deployment of Datadog.
The deployment of Datadog just took a few hours.
The challenges I encountered while using Datadog were in the early days when the product was missing the ability to monitor Kubernetes and similar features, but they have since added those features. At the moment, I don't think there are too many challenges that I am worrying about.
One person is enough to do the installation.
I am not working with any of these solutions currently because I'm on sabbatical, but I used to work with Datadog six months ago, and now at the moment I'm on sabbatical.
We were using the tools that AWS and Azure came with natively to monitor the AI workflows on their platforms.
I used to work as the CTO at Northcloud, but I no longer work there.
On a scale of one to ten, I rate Datadog an eight out of ten.
The technology itself is generally very useful and the interface it great.
There should be a clearer view of the expenses.
I have used the solution for four years.
The solution is stable.
I have not personally interacted with customer service. I am satisfied with tech support.
Neutral
I am using ThousandEyes and Datadog. Datadog supports AI-driven data analysis, with some AI elements to analyze, like data processing tools and so on. AI helps in Datadog primarily for resolving application issues.
It was not difficult to set up for me. There was no problem.
I can confirm there is a return on investment.
I find the setup cost to be too expensive. The setup cost for Datadog is more than $100. I am evaluating the usage of this solution, however, it is too expensive.
I would rate this solution eight out of ten.
