Positive

I correlate those metrics, traces, and logs using the Service Map to visualize dependencies between our microservices, and for example, during a latency spike, I can instantly see if there is a bottleneck in a specific database query or a downstream API, which allows me to route the issues to the right team immediately.

Datadog has positively impacted our organization, as it has eliminated many negative issues, which I call tool sprawl, by replacing four or five separate monitoring tools with one unified platform. This has improved our MTTR and broken down silos between Dev and Ops teams.

Since Datadog has been introduced, the response time when seeing an alert has increased, so alerts have been taken care of within less time and routed to the other teams who have been taking the required actions. This has given us a very positive approach towards the entire working culture.

When it comes to the documentation, we do not have much available right now, so if Datadog can improve the documentation part, it would really help the engineers to work on this.

Datadog is the most comprehensive observability tool on the market, and it only loses two points because the pricing for log ingestion can grow quickly if we do not carefully manage our filters.

I intend to use Datadog for application performance monitoring, digital user experiences, and troubleshooting to find the root cause analysis of tickets that will be generated in my managed environment. Digital user experience happens to be the priority for me, as I am evaluating this feature across some competing products.

The best features Datadog offers are digital user experience, troubleshooting, and remediation capabilities, which help identify what is going wrong and where. I focused on the root cause analysis of incidents and tickets, as examining the RCAs makes it easier to find remediations and helps with shifting incidents left. Datadog will positively impact my organization by allowing me to handle ticket resolutions at a much faster pace and bring productivity by reducing the number of support engineers required at the monitoring level. If I integrate Datadog with my managed environment or cloud environment, the RCAs and all the left shift will be automated, and with automation, I will be able to reduce the number of support engineers.

Datadog could be improved with a simpler graphical user interface that can be extended to non-technical users, such as a CXO, if they want to review the dashboard overall for current tickets and the ticketing dashboard. It would be beneficial to have documentation auto-generated while examining remediations or integration with existing systems.

I have been working for more than fifteen years in data center, disaster recovery solutions, and cloud computing, which includes private, public, and hybrid environments.

Datadog seems to be more stable, and I really want to have a complete demo before making a call to decide on this.

I hope that Datadog will be able to extend to digital users, even if they are on a scale of thousands for an organization and connect to corporate bandwidth, and the server should be pretty much scalable on the server side.

I find the customer support impressive from what I have heard about Datadog, and I really want to onboard this solution for my customers.

Positive

As of now, we are using cloud-native monitoring with CloudWatch and Azure Monitor for our multi-cloud environment, and we really want to extend it to greater detail that will cover deliberations at greater depth. We have looked at ManageEngine and SolarWinds before choosing Datadog, but they were not very impressive, as the amount of Datadog functionality is not available in these two platforms.

I am looking to deploy Datadog on AWS and Azure for multi-cloud management support and really want to extend it at the server side and at the end-user side for digital user experience. I will start with AWS and extend it to Azure six months down the line. I plan to purchase Datadog through the AWS Marketplace once I have the demo.

I am looking at metrics that will help me decide whether I need to really deploy Datadog, and the metrics will primarily be centered around reducing the number of employees and cost optimization.

I did not get the complete information regarding the licenses and commercials associated with Datadog, and I would like to have some idea about the license.

I hope to have some literature on how I can leverage my managed support for cloud environments, plus how I can integrate this with my managed support at the end-user devices. Finding the root cause analysis at greater depth, reducing the number of employees to manage or monitor infrastructure incidents, and increasing satisfaction on the application performance monitoring part are the advice I would give to others looking into using Datadog. I give this review a rating of eight.

My main use case for Datadog is monitoring our servers.

A specific example of how I'm using Datadog to monitor my server is that we are maintaining request and latency and looking for errors.

I really enjoy the user interface of Datadog, and it makes it easy to find what I need. In my opinion, the best features Datadog offers are the customizable dashboards and the Watchdog.

The customizable dashboards and Watchdog help me in my daily work because they're easy to find and easy to look at to get the information I need. Datadog has positively impacted my organization by making finding and resolving issues a lot easier and efficient.

I think Datadog can be improved by continually finding errors and making things easy to see and customize.

I have been using Datadog for one month.

Datadog is stable.

Datadog's scalability has been easy to put on each server that we want to monitor.

I have not had to contact customer support yet, but I've heard they are great.

Neutral

We previously used our own custom solution, but Datadog is a lot easier.

I'm not sure if I've seen a return on investment.

My experience with pricing, setup cost, and licensing is that it was easy to find and easy to purchase and easy to estimate.

I did not make the decision to evaluate other options before choosing Datadog.

I would rate Datadog a nine out of ten.

I give it this rating because I think just catching some of the data delays and latency live could be a little bit better, but overall, I think it's been great.

I would recommend Datadog and say that it's easy to customize and find what you're looking for.

We use Datadog for all of our observability needs and application performance monitoring. We recently transitioned our logs to Datadog. We also use it for incident management and on-call paging. We use Datadog for almost everything monitoring and observability related.

We use Datadog for figuring out the root cause of incidents. One of the more recent use cases was when we encountered a failure where one of our main microservices kept dying and couldn't give a response. Every request to it was getting a 500. We dug into some of the traces and logs, used the Kubernetes Explorer in Datadog, and found out that the application couldn't reach some metric due to its scaling. We were able to figure out the root cause because of the Kubernetes Event Explorer in Datadog. We pushed out a hotfix which restored the application to working condition.

Our incident response team leverages Datadog to page relevant on-calls for whatever service is down that's owned by that team, so they can get the appropriate SMEs and bring the service back up. That's the most common use case for our incident response. All of our teams appreciate using Datadog on-call for incident response because there are numerous notification settings to configure. The on-call schedules are very flexible with overrides and different paging rules, depending on urgency of the matter at stake.

As an administrator of Datadog, I really appreciate Fleet Automation. I also value the overall APM page for each service, including the default dashboards on the service page because they provide exactly what you need to see in terms of request errors and duration latency. These two are probably my favorite features because the service page gives a perfect look at everything you'd want to see for a service immediately, and then you can scroll down and see more infrastructure specific metrics. If it's a Java app, you can see JVM metrics. Fleet Automation really helps me as an administrator because I can see exactly what's going on with each of my agents.

My SRE team is responsible for upgrading and maintaining the agents, and with Fleet Automation, we've been able to leverage remote agent upgrades, which is fantastic because we no longer need to deploy to our servers individually, saving us considerable time. We can see all the integration errors on Fleet Automation, which is super helpful for our product teams to figure out why certain metrics aren't showing up when enabling certain integrations. On Fleet Automation, we can see each variant of the Datadog configuration we have on each host, which is very useful as we can try to synchronize all of them to the same version and configuration.

The Kubernetes Explorer in Datadog is particularly valuable. It gives us a look at each live pod YAML and we can see specific metrics related to each pod. I appreciate the ability to add custom Kubernetes objects to the Orchestration Explorer. It gives our team an easier time to see pods without having to kubectl because sometimes you have permission errors related to that. Sometimes it's just quicker than using kubectl.

Our teams use Datadog more than they used their old observability tool. They're more production-aware, conscious of how their changes are impacting customers, how the changes they make to their application speed up or slow down their app, and the overall request flow. It's a much more developer-friendly tool than other observability tools.

Datadog needs to introduce more hard limits to cost. If we see a huge log spike, administrators should have more control over what happens to save costs. If a service starts logging extensively, I want the ability to automatically direct that log into the cheapest log bucket. This should be the case with many offerings. If we're seeing too much APM, we need to be aware of it and able to stop it rather than having administrators reach out to specific teams.

Datadog has become significantly slower over the last year. They could improve performance at the risk of slowing down feature work. More resources need to go into Fleet Automation because we face many problems with things such as the Ansible role to install Datadog in non-containerized hosts.

We mainly want to see performance improvements, less time spent looking at costs, the ability to trust that costs will stay reasonable, and an easier way to manage our agents. It is such a powerful tool with much potential on the horizon, but cost control, performance, and agent management need improvement. The main issues are with the administrative side rather than the actual application.

I have been using Datadog for about a year and nine months.

We face a high amount of issues with niche-specific outages that appear to be quite common. AWS metrics being delayed is something that Datadog posts on their status page. We face a relatively high amount of Datadog issues, but they tend to be small and limited in scope.

We have not experienced any scalability issues.

I have interacted with support. Support quality varies significantly. Some support agents are fantastic, but some tickets take months to resolve.

Neutral

We used Dynatrace previously, and I believe the switch was due to cost, but that decision was outside my scope as I'm not a decision-maker in that situation.

The initial setup in Kubernetes is not particularly difficult.

I cannot definitively say MTTR has improved as I don't have access to those numbers and don't want to make misleading statements. Developers use it significantly more than our old observability tool. We've seen some cost savings, but we have to be significantly more cost-aware with Datadog than with our previous observability tool because there's more fluctuation and variation in the cost.

One pain point is that it has caused us to spend too much time thinking about the bill. Understand that while it is an administrative hassle, it is very rewarding to developers.

On a scale of 1-10, I rate Datadog an 8 out of 10.

Positive

My main use case for Datadog is for security SIEM, log management, and log archiving.

In my daily work, we send all our logs from different cloud services and SaaS products, including Okta, GCP, AWS, GitHub, as well as virtual machines, containers, and Kubernetes clusters. We send all this data to Datadog, and we have numerous different monitors configured. This allows us to create different security features, such as security monitoring and escalate items to a security team on call to create incident response. Archiving is significant because we can always restore logs from the archive and go back in time to see what happened on that exact day. It is very helpful for us to investigate security incidents and infrastructure incidents as well.

Regarding our main use case, we use the Terraform provider for Datadog, which is probably one of the biggest benefits of using Datadog over any other similar tool because Datadog has great Terraform support. We can create all our security monitoring infrastructure using Terraform. Even if something goes wrong and the Datadog tenant becomes completely compromised or if all our monitors were to get erased for whatever reason, we can always restore all our monitoring setup through Terraform, which provides peace of mind.

The best features Datadog offers are not necessarily about having the best individual features, but rather the sheer quantity of different features they offer. I appreciate how you can reuse a query across different indexes for logs or security monitoring. The syntax remains consistent for everything, so you do not have to learn multiple languages. Similarly, for different types of monitors, you can always reuse the same templating language, which makes things much more efficient.

Datadog positively impacted our organization by making us more cautious about how we manage our logs. Before Datadog, we would ingest substantial amounts of data without considering indexing priorities. We became more strategic about what we index, particularly for security and cloud audit logs. We improved our approach to indexing retention and determining which types of logs are important. Overall, we enhanced our internal log management practices.

After implementing Datadog, we observed specific improvements in outcomes and metrics. We started analyzing our logs more thoroughly than before, identifying different patterns, and determining log importance levels. We began looking for more signals from audit logs and distinguishing between critical and non-critical information. The most significant metric improvement has been reduced incident investigation time.

Datadog can be improved by addressing billing and spend calculation methods, as it would be better if these were more straightforward. Currently, these calculations can be complex. Additionally, while we use Terraform extensively, not everything is available in Terraform. It would be beneficial to have more features supported in Terraform, particularly some security features that have been available for a while but still lack Terraform support.

I have been using Datadog for about four years.

Datadog is very stable.

Datadog's scalability is excellent. We have never encountered any issues.

The customer support is good. I have never had any issues.

I would rate the customer support as nine out of ten.

Positive

We previously used New Relic and switched because it was not very effective.

My experience with pricing, setup cost, and licensing indicates that it was somewhat expensive.

I have seen a return on investment with Datadog, particularly in time saved responding to incidents. Regarding staffing requirements, that metric isn't applicable for our use case since log management and security monitoring inherently require personnel to respond. However, it has definitely improved our efficiency in terms of response time, though this isn't a hard metric but rather based on experience.

I do not remember evaluating other options before choosing Datadog as it was a long time ago.

I would rate Datadog an eight out of ten because while it is expensive, it offers numerous features, though sometimes it attempts to do too much.

My advice to others considering Datadog is to explore other products and calculate potential spending carefully. If Terraform support is important to your organization, then Datadog is an excellent choice. However, keep in mind that costs will increase significantly as you scale, and different features have varying pricing structures.

Overall rating: 8/10

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.

Neutral