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 to analyze data in regards to instant funding.

A specific example of how I use Datadog for instant funding data is understanding how long it takes for an application to be processed, approved, and then instantly funded, how many applications there are, and if there's any holdups on the applications as well.

We are identifying the reason behind a hold-up for instant funding and possibly why some applications do not get instantly funded. Datadog helps us identify those weak areas.

Datadog has significantly improved our organization’s visibility into system performance and application health. The real-time dashboards and alerting capabilities have helped our teams detect issues faster, reduce downtime, and improve response times. It’s also made collaboration between engineering and operations smoother by providing a shared view of metrics and logs in one place.

In my experience, the best features Datadog offers include the layout of the reporting, which is user-friendly, and for those who are not familiar with data, this helps the visual impact.

The layout and reporting are user-friendly because there is a dashboard that I use the most.

Datadog has positively impacted my organization by allowing cross-functional teams who do not necessarily work directly with data to understand, simplify, and take in the data points.

Those cross-functional teams are using the data now by reviewing these reports and they are able to identify weak spots as well to improve cross-functionally the application process.

Areas for improvement:
Datadog could improve in dashboard usability and data correlation across products. While it’s powerful, the interface can feel cluttered and overwhelming for new users. Streamlining navigation and offering simpler default dashboards would help teams ramp up faster.

Additional features for next release:
It would be great to see stronger AI-driven anomaly detection and predictive analytics to help identify potential issues before they impact performance. Improved cost management insights or forecasting tools would also help teams monitor usage and control expenses more effectively.

I have been using Datadog for roughly six months.

Datadog is stable.

Regarding Datadog's scalability, we have not scaled yet, but we are in the process of continuously scaling up, so we will find out in the near future.

The customer support of Datadog is amazing.

I would rate the customer support a definite 10, as friendliness is top tier.

Positive

I previously used a different solution, and we switched due to inconsistencies. The previous solution was also inaccurate and unreliable.

I have seen a return on investment in terms of time saved. I don't have metrics on hand for that answer, but there has been time saved due to the Datadog output.

My experience with pricing, setup cost, and licensing has been that all were fair.

Before choosing Datadog, I evaluated other options, but I don't want to identify other ones.

I don't have anything else to mention about the features, including integrations, alerts, or ease of setup.

I am unsure what advice I would give to others looking into using Datadog.

I found this interview impressive for AI, and I do not think there is anything I would change for the future.

On a scale of one to ten, I rate Datadog a 10.

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.

My main use case for Datadog is the security aspect of it, utilizing the SIEM and the cloud security features. I use it every day monitoring different types of logs and reports that come through, managing most of the alerts that populate from our different applications and software, and it's been a good ride.

Datadog has impacted my organization positively because it tracks all the logs and helps us utilize our features through security. We use Datadog in basically all of our other teams, including engineering, code, APIs, and many other features available, and my peers always say something good about it.

Datadog has helped my organization improve a lot of response time because we get alerts the minute it happens, which is our only means to reduce incident response time. I also appreciate how it provides remediation efforts, allowing us to implement different playbooks while constantly updating with new threats and vulnerabilities, keeping us safe.

One of the best features I appreciate is the Cloud SIEM, and I've used many SIEMs in my experience, but until I got to this company, I never had the chance to really see how Datadog works. With this organization, they were able to show me how easy it was, and Datadog has a really good UI that's easily navigable, helping us teach new team members quickly.

My experience with the Cloud SIEM specifically is that it works 24/7 and stands out due to the easy UI it provides, which helps onboard new members who enjoy it. They are able to pick it up quickly without any prior knowledge.

Datadog helped us out with cloud security features and alerts during situations where we get numerous account lockouts or accounts being jeopardized. Datadog was able to find the alerts and trigger to notify our team in a very prompt manner before it got worse, allowing us to promptly adjust and remediate the situation in time.

Something I would appreciate seeing from Datadog is more events focused on the networking aspect, which allows me to see what others are using. I enjoy showing up to those events and exploring new features they are releasing as well.

I think Datadog has been performing excellently with no areas that need improvement, as they've been doing great and I want them to keep striving to do better.

I'm fairly new with Datadog, having used it for the past year and a half, almost two years now, and it's been going really well.

Datadog is very stable, as there hasn't been any downtime or issues since I've been here, and it's always on time. I would appreciate seeing it as an app or mobile app for quicker issue tracking.

Datadog has definitely kept up with our growth.

I've had a couple instances where I reached out to Datadog's support team, and they have been really super helpful and very kind, even reaching back out after resolving my issues to check if everything's going well.

Positive

I was not here during the time they onboarded Datadog or looked for different solutions, so I'm not aware of which solution we used before.

I cannot share any metrics regarding return on investment.

Pricing is fairly affordable, and the setup cost has been good, while licensing has been well maintained, making it pretty great.

I'm certain they did their research and looked around at many different options, but I cannot speak on their behalf regarding which they chose or had competition with.

My advice for others looking into using Datadog is to honestly give yourself a week or two to explore all the features and software application, as there are quite a lot of amazing features to learn and utilize, making it not just a software to monitor threats but also a tool to enhance your knowledge in this industry. I rate Datadog 10 out of 10.

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.

Our main use case for Datadog is collecting metrics, specifically things such as latency metrics and error metrics for our services at Procore.

To give a specific example of how I use Datadog for those metrics in my daily work, I had to create a new service to solve a particular problem, which was an API. I used Datadog to get metrics around successful requests, failure requests, and 400 requests. I then created dashboards that showed those metrics along with some latency metrics from the API, and I also built a monitor that triggers and sends an alert whenever we're over a certain number of the failure metrics.

The single biggest improvement has been breaking down the silos between our teams. Before we adopted it, our developers, operations, and SRE teams all lived in separate tools. Ops had their infrastructure graphs, Devs had their log files, and no one had a complete picture.

Here’s where we’ve seen the most significant impact:

1. We Find and Fix Problems Drastically Faster: The "single pane of glass" is a real thing for us. When an alert fires, our on-call engineer can see the infrastructure metric spike (like CPU), pivot directly to the application traces (APM) running on that host, and see the exact, correlated logs from the services causing the problem—all in one place. We've cut our Mean Time to Resolution (MTTR) significantly because we're no longer "swivel-chairing" between three different tools trying to manually line up timestamps.
2. We Are More Proactive and Less Reactive: Features like Watchdog (its anomaly detection) have been crucial. We've been alerted to a slow-building memory leak and an abnormal spike in error rates on a specific API endpoint before they breached our static thresholds and caused a user-facing outage. It's helped us move from a "firefighting" culture to one where we can catch problems before they escalate.

The best features of Datadog include a great dashboard, a super simple and easy to use Python library, and an easy monitor, which together provide a really great UI experience.

What makes the dashboard and Python library stand out for me is that they save a lot of time, getting right to the point and being super intuitive.

Datadog has positively impacted my organization by allowing us to have a link to a dashboard for most services.

We have dashboards across the company, which can easily be passed around, making it super easy for everyone to understand the metrics they are looking at.

Oh, that's a great question. We actually have a running list of things we'd love to see. Even though we get a ton of value from it, no tool is perfect. Our feedback generally falls into two categories: making the current experience less painful and adding new capabilities we think are the logical next step.

Honestly, our biggest frustrations aren't about a lack of features, but about the management of the platform itself.

1. Cost Predictability and Governance: This is, without a doubt, our number one issue. It's not just that Datadog is expensive—it's that the cost is incredibly complex and hard to predict. Our bill can fluctuate wildly based on custom metrics, log ingestion, and traces from a new service. We've had to dedicate engineering time just to managing our Datadog costs, creating exclusion filters, and sampling aggressively, which feels like we're being punished for using the product more.
   • How to improve it: We need a "cost calculator" inside the platform. Before I enable monitoring on a new cluster or turn on a new integration, I want Datadog to give me a concrete estimate of what it will cost. We also need better built-in tools for attributing costs back to specific teams or services before the bill arrives.
2. The Steep Learning Curve and UI Density: The UI is incredibly powerful, but it's dense. For a senior SRE who lives in the tool all day, it's fine. For a new engineer or a developer who only jumps in during an incident, it's overwhelming. We've seen people "click in circles" trying to find a simple stack trace that's buried three layers deep. Building a "perfect" dashboard is still too much of an art form.

I have been using Datadog for about five years.

Datadog is stable.

I did not previously use a different solution.

I did not deal with any of the pricing, setup cost, or licensing.

I do not know if we purchased Datadog through the AWS Marketplace.

My advice to others looking into using Datadog is to just try using it and see how easy it is to use. I found this interview great. On a scale of 1-10, I rate Datadog a 10.

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.