Datadog Reviews

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.

Datadog serves as my primary tool for infrastructure monitoring and log analysis in a cloud environment. From a network and security perspective, I use it to monitor server health, track network metrics like latencies and traffic patterns, and analyze logs for troubleshooting issues such as VPN instability and unexpected spikes. The ability to correlate metrics and logs in one place makes it much faster to identify the root cause instead of checking multiple tools.

One example where Datadog proved invaluable was during a sudden spike in application response time. We received alerts on increased latencies, and instead of checking multiple tools, I used Datadog's dashboard to quickly correlate metrics. I noticed that while the application CPU was normal, there was a spike in database response times. Using the logs and metrics together, I was able to confirm that the issue was coming from the database, not the application. This helped us quickly involve the right team and resolve the issue faster.

The best features of Datadog are the correlation capabilities and unified visibility. The most useful aspect is that I can see metrics, logs, and service-level data in one place. During troubleshooting, I do not have to switch tools; I can directly correlate spikes in latencies with log error patterns, which saves considerable time. Another feature I find very useful is the dashboards, which are flexible, and I can create views based on what I actually need to monitor daily instead of relying on default setups. The integration with cloud services makes onboarding very easy, and once integrated, most of the data starts flowing automatically without much manual effort.

Datadog has had a positive impact, mainly by improving how quickly we detect and understand issues. Earlier, when something went wrong, considerable time went into figuring out where the problem actually was. Now, with better visibility across services and logs, we can quickly narrow down the source, whether it is application, infrastructure, or dependency-related. It has also helped in reducing the back and forth between teams because we can validate issues with the data before escalating, which has made incident handling smoother and more efficient overall.

One area where Datadog can be improved is around alert quality. In the beginning, it tends to generate many alerts, and without proper tuning, many of them are not actionable. It would help if there were more built-in guidance or smarter defaults to reduce noise. Another improvement area is cost visibility and control. As log and metric ingestion increases, it has not always been straightforward to track which data is driving the cost. More granular and real-time cost insights would make it easier to manage. Additionally, while the dashboards are flexible, navigating and organizing them at scale can become slightly difficult. Better structuring or management options would help in larger environments.

I have been using Datadog for nearly two years.

Datadog has been stable overall in my experience. We have not seen any major platform outages. Metrics collection and alerting have been consistent in day-to-day use. Most issues we have faced were related to configurations or alert tuning rather than the platform itself. The platform is stable with no major platform issues, only configuration-related challenges.

Datadog scales well as environments grow in my experience. As we add more servers and services, onboarding is straightforward with agents and integrations. We have not faced any major performance issues from the platform side; it handles increased metrics and monitoring loads smoothly. The primary consideration is managing log volume carefully because as the scale increases, data ingestion and costs also go up. Datadog is scalable technically, but the ingestion costs need to be managed as the environment grows.

We do not rely on Datadog support for day-to-day issues. Most of the time, we are able to resolve things using the dashboards, logs, and their documentation. We have only reached out in a few cases, mainly for configuration-related queries, and in those situations, support was helpful, though sometimes it required a few back and forth interactions to get to the exact solution. Overall, support is decent, but we mostly depend on self-troubleshooting.

Before Datadog, we were mainly using native cloud monitoring like Azure Monitor, along with a few basic tools. The main issue was that monitoring was fragmented. Metrics, logs, and alerts were spread across different places, and so during an incident, we had to switch between multiple tools to understand what was happening. We moved to Datadog to have everything in one place. The ability to correlate metrics and logs in a single platform made troubleshooting much faster and more efficient.

Setting up dashboards and integrations in Datadog is relatively straightforward in my experience, especially for standard cloud services. For integrations, once we connect our cloud account, most of the metrics start coming in automatically, so the initial setup is not very complex. The documentation also helps considerably during this phase. For dashboards, basic ones are easy to create using existing templates, but to make them truly useful, we have to spend time customizing them based on our actual use cases, like adding specific metrics and refining the layout. Overall, the initial setup is easy, but making it truly effective takes practical tuning.

We have seen a clear return on investment with Datadog, mainly in terms of time saved and faster incident handling. For example, earlier when an issue occurred, it would take around thirty-five to forty-five minutes just to identify the root cause because we had to check multiple tools. With Datadog, we are usually able to narrow it down within ten to fifteen minutes using the centralized dashboard and logs. We have also reduced repeated troubleshooting efforts because we can identify patterns and fix the root cause instead of dealing with the same issues repeatedly. It has not reduced headcount, but it has definitely improved team efficiency and allowed us to handle more incidents with the same team.

My experience with pricing for Datadog has been mixed. The initial setup cost is relatively low since it is a SaaS model and does not require a heavy upfront investment. Getting started is quite quick with agent-based deployments. However, the ongoing cost is something that needs to be managed. Pricing is mainly based on data ingestion, such as logs, metrics, and traces, and it can increase quickly if everything is enabled by default. Licensing is flexible, but it requires continuous monitoring and optimization to keep costs under control.

One additional point I can add is that with Datadog, I focused considerably on making alerts actionable and reducing noise. In the initial phases, we had too many alerts that were not very useful, so we spent time tuning thresholds, adding conditions, and correlating alerts with real impact. After that, alerts became much more meaningful and helpful in faster response. I also use it regularly for trend analysis, checking for recurring spikes or patterns over time, which helps in identifying potential issues before they become incidents.

The features of Datadog become truly useful when you start combining them, not just using them separately. For example, just looking at the metrics alone does not always give the full picture, but when you combine metrics with logs and service-level data, it becomes much easier to understand what is actually happening during an incident. Features like tagging help considerably in filtering data across environments and services, especially when the setup grows. Without proper tagging, it can get difficult to navigate. Overall, the strength of Datadog is not just the individual features, but how well they work together in real scenarios.

We have seen noticeable improvements after using Datadog, mainly in terms of time saved and faster incident handling. Earlier when an issue occurred, it could take around twenty to forty minutes just to understand where the problem was. Now, with the centralized visibility and correlation of metrics and logs, we are often able to narrow it down within fifteen to twenty-five minutes. We have also seen fewer repeated incidents because we can identify patterns and fix the root cause instead of just resolving symptoms. Incidents are getting resolved faster, and the time spent on troubleshooting has reduced significantly.

My advice for anyone considering Datadog is to be selective about what you monitor from day one. It is tempting to enable everything, but that usually leads to too much data and noisy alerts. Instead, start with critical services and key metrics, and then expand gradually. Invest time in tagging and structuring your data properly because it makes a considerable difference later when you need to filter, troubleshoot, or build dashboards. Finally, review your setup regularly because what works in the beginning may not stay relevant as the environment grows. Start small, avoid collecting all data, use proper tagging, and keep refining your setup over time. This review reflects an overall rating of eight.

My main use case for Datadog is infrastructure and log monitoring in a cloud-based environment. From a network and security perspective, I mainly use it to monitor server health, track network-level metrics, and analyze logs for troubleshooting issues such as VPN instabilities, traffic spiking, or unexpected behavior.

One recent example where I used Datadog was during a VPN-related issue where users were reporting intermittent disconnections. I checked our Datadog dashboard and noticed spiking in network latencies and a sudden increase in connections dropped around the same time users reported the issues. I then correlated this with the logs and found that one of the back-end servers handling the connection was hitting high CPU utilization. Because everything was centralized, I did not have to jump between multiple tools. I was able to quickly identify the impacted servers and escalate it to the infrastructure team. Once the load was balanced, the issue got resolved.

With Datadog, I mainly focus on creating meaningful dashboards and tuning alerts properly. In the beginning, we saw a lot of alert noise, so we had to refine thresholds and conditions to make sure alerts are actually actionable. Once that was done, it became much more effective for proactive monitoring instead of just reactive troubleshooting.

One of the best features of Datadog, in my opinion, is its unified visibility across the metrics, logs, and traces in a single platform. The dashboards are very flexible and customizable, which helps a lot in creating meaningful monitoring views based on different use cases. I also find the log management quite useful because it allows quick correlation with metrics during troubleshooting. Another strong feature is its integration, especially with cloud platforms such as AWS or Azure, which makes onboarding and monitoring much easier without heavy manual work.

Integration with cloud platforms such as Amazon Web Services or Microsoft Azure has really made daily monitoring much easier. Once the integration is set up, Datadog automatically pulls metrics from services such as virtual machines, load balancers, and databases without needing manual configuration on each resource. In one case, I was monitoring a cloud-based application where we started seeing performance issues through Datadog's Azure integrations. I could quickly view metrics from the application server and the back-end database in the same dashboard. It helped me identify that the issue was not network-related but due to the increased load on the backend services. Instead of checking multiple portals, everything was available in one place, which saved time and made troubleshooting faster.

Datadog has had a positive impact mainly by improving visibility and reducing troubleshooting times. Earlier, we had to rely on multiple tools to check metrics and logs, which delayed root cause analysis. With Datadog, everything is centralized, so it is much faster to identify issues and take actions. It has also helped in proactive monitoring with properly tuned alerts. We are able to detect unusual behaviors such as spiking in traffic or resource usage before it turns into a major incident. Overall, it has improved operational efficiency and reduced downtime by enabling quicker responses during incidents.

If you are asking for improvements, I feel some small areas where Datadog can improve. One area is alert management. In a dynamic environment, it can generate a lot of alert noise if not tuned properly. More intelligent alerting or built-in recommendations would help. Another aspect is cost visibility. As log ingestion increases, pricing can scale quickly. Having more transparent and granular cost control features would make it easier to manage usage. Also, the initial setup and configuration can feel a bit complex for new users.

I have been using Datadog for ten months.

In my experience, it has been quite stable; we have not faced any major outages or reliability issues from the platform side. Data collection and dashboards have been consistent, and alerts are delivered on time as long as they are properly configured. Most of the issues we have seen were related to configuration or alert tuning rather than the platform itself.

It has scaled well for our needs. As we added more servers and services, Datadog was able to handle the increased load without any major issues. Since it is a SaaS platform, we did not have to worry about backend scaling. New hosts and services get onboarded easily with the agents, and metric collection continues smoothly even as the environment grows. The only thing we monitor closely is log volume because as scale increases, ingestion and costs also go up, but from a performance and handling perspective, it has been quite good.

In my experience, the customer support from Datadog has been quite reliable. For standard issues and queries, the response time is generally good, and the documentation is also very helpful for resolving common problems. For more complex cases, support may take some time for investigations, but they usually provide proper guidance and follow-up. Overall, I would say support is responsive and helpful, especially when combined with their strong documentation.

This is the first time I am using Datadog. Before that, there was not any solution in place.

The initial setup cost is relatively low since it is a SaaS model and getting started is straightforward with agent-based deployments. However, the main challenge is the ongoing cost, which depends on data ingestion such as logs, metrics, and traces. As usage grows, especially with log collection, the costs can increase quickly, which requires proper planning around what data to collect, retention policies, and filtering to keep control. Overall, I think it is flexible, but cost optimization needs continuous monitoring.

We have seen a return on investment with Datadog, mainly in terms of saving operational efficiency. For example, earlier our troubleshooting process involved checking multiple tools, which used to take around forty to forty-five minutes just to identify the root cause. With Datadog, since metrics and logs are centralized, we are usually able to reduce the time to around ten to twenty minutes in many cases. This has improved our response time and reduced the duration of incidents. While it may not directly reduce headcount, it definitely improves team productivity and helps handle more issues efficiently with the same team.

While we do not track exact numbers in all cases, with Datadog we have definitely seen a noticeable improvement in incident response time. For example, earlier it could take around thirty to forty-five minutes to identify the root cause analysis because we had to check multiple tools. With Datadog's centralized dashboards and logs, we are usually able to narrow it down within ten to fifteen minutes in most cases. We have also seen fewer escalations for minor issues because alerts help us catch problems earlier, which indirectly reduces downtime and improves overall efficiency.

We did consider a few alternatives, but they each have their own standards. We considered solutions such as Splunk, New Relic, and Prometheus. Everything is more costly, but I prefer Datadog. I have just heard about Datadog and other monitoring tools from some colleagues. As per their comparisons, I feel Datadog is much better.

If anyone is looking to use Datadog, I would advise planning your monitoring strategy from the beginning. Focus on what metrics and logs are actually important because collecting everything can increase noise and costs. It is also important to spend some time on proper alert tuning; otherwise, you may end up with too many non-actionable alerts. I would also recommend starting with key integrations, especially with cloud platforms, and then gradually expanding use instead of enabling everything at once. I would rate this product an eight out of ten.

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 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.

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.

My main use case for Datadog is monitoring performance of Grainger.com and all the components that are involved within it.

A specific example of how I use Datadog to monitor performance is finding out an issue with an internal bot that we use. We had some issues with some of the commands and we looked into the logs which showed the events from that Slack bot. This was quite useful.

I use Datadog day-to-day to monitor the performance of key services, endpoints, and resources. Currently, we have a migration project for which I created a dashboard to help visualize the performance of key services and endpoints being migrated. At a high level, it helps to capture the performance and health of the services and endpoints.

Datadog has impacted my organization positively as this is our main observability tool when it comes to monitoring services, traces, and all resources within key services. This is our go-to tool and it has helped us to pinpoint issues. One aspect that needs improvement about Datadog is the Watchdog. If there are any escalated conditions or errors happening, it does not indicate which service is causing the issue or which line of code is responsible unless we recreate Watchdog monitors and add the dependency of the GitHub repo to that service.

When pinpointing issues, it helps us focus on where the problem is. Sometimes it's finding a needle in a haystack, especially when it comes to network issues. This has been our key concern lately. During network outages, we don't know exactly which device has the issue, but network observability is an area we're working towards improving. For regular issues within services, we can see the errors, but we must configure the GitHub repo associated with that service to see the key issue. Overall, it helps us to pinpoint issues. While I'm not certain about the exact timing of resolution, it does help overall.

In my opinion, the best features Datadog offers are their APM traces and ability to create dashboards with many customizable metrics, from CPU to thread count to host errors by host and errors by service. Having customized dashboards is really useful, and exploring traces is one of my favorite parts.

We have a list of dashboards primarily showing the key services and APIs related to orders, generating orders, customer direct, and main customer services. Within that list, we have RUM dashboard as well, which shows us the customer impact and the performance of key services which can directly impact customers. During code red or major escalations, I refer to these dashboards for quick analysis of any issues for the services or endpoints.

To make Datadog better, it should be able to pick up error codes automatically. Currently, you have to programmatically configure every single step. In our previous tool, Dynatrace, it could pick up error codes without developers having to explicitly code that into the configuration. Sometimes the APMs are missing the exact error code and error message which is frustrating.

Some minor improvements could include adjusting unit display on dashboards. When request counts go from 900,000 to 1.5 million or 2.2 million for endpoints, the graph keeps all units in thousands rather than converting to millions, which would be more useful and visually appealing.

Datadog Watchdog hasn't been as effective as Dynatrace Davis, which pinpoints key errors or latency within a specific service and drills down to the specific endpoint. This is an area where Datadog could improve.

We fully migrated to Datadog last year.

In my experience, Datadog is stable, though there's typically at least one or two incidents per week. This amounts to approximately four incidents per month that cause disruption. These incidents are related to log service, indexes, and metric capturing issues, which occur in the Datadog platform more frequently compared to other tools we have.

Datadog's scalability for my organization is pretty straightforward. When it comes to installation, we just have to install it on the respective service hosts and configure it. There's a new way of installing these agents, though I haven't worked on it in a while, but the process is straightforward for installing.

The customer support rates eight out of ten. They require all information upfront and there's still back and forth communication happening. Overall, they provide good service.

Positive

We switched from Dynatrace to Datadog after conducting a survey amongst team members from various service teams. We found that developers preferred using Datadog over Dynatrace. The user interface was more intuitive, modern, and more cloud-focused. Since everybody was moving to cloud, we determined that Datadog would be a suitable tool for us.

When comparing the setup between Dynatrace and Datadog, Datadog required more time and effort. Dynatrace was more straightforward - you simply install the agent and it picks up all the traffic with minimal configuration needed for capturing specific things. Overall, the setup for Datadog was more challenging compared to Dynatrace setup.

I would rate Datadog overall as eight out of ten.

My advice for others looking into using Datadog is to be ready to spend a lot of time setting it up and make sure you have a good plan in terms of analyzing the finances because it can easily cost a lot of money to install agents on your service hosts.

My main use case for Datadog is that we offer the application performance management service within PwC as a global team.

A specific example of how my team uses Datadog for performance management is that my team does not directly use Datadog for performance management; however, we work with approximately 300 teams that use it daily for monitoring their apps. One of the most used cases is to observe when services are up and down and if services are not degraded.

We use most of every product within Datadog across the 300 customers that we have internally.

Datadog has positively impacted my organization because before Datadog, we had multiple APM tools and monitoring tools, which fragmented the service. The reason was that some tools offered benefits to certain teams, while other tools offered different benefits to other teams. With Datadog, we managed to get everyone on board into a single place and a single tool, providing teams with one spot where they can check everything related to monitoring, and enabling management and leadership to have an overview of all tools working together.

I measured the impact of bringing everything into one place through observation, and I can confirm that efficiency in reporting improved dramatically and it became much easier to observe changes. Standardization was a tremendous win for us. Having a set of standard alerts and monitoring in place allowed us to speed up onboarding for every app. Once the resources are in Datadog, the system provides alerting out of the box. Additionally, cost has decreased dramatically.

Datadog's best features include very high demand for logs management for alerting on indexed logs and a shift towards Flex Logs for storage and long-term storage. Most recently, BitsAI and the LLM part within Datadog has been in focus for us.

Flex Logs has helped my teams because we are migrating from other services to have a unique place to store all the logs, the non-security logs, and the app logs. This has benefited those teams because they also benefit from other services within Datadog such as APM or other monitoring solutions. By bringing the logs into Datadog, they now have a single place where they can correlate everything.

The LLM integration within Datadog has helped my teams because LLM usage is at the beginning stage right now, and people are very excited. We have all these AI and LLM-based tools, and having the option of monitoring them is a great benefit for us. However, we are in the exploratory phase of this process and have just begun.

BitsAI is very interesting; we have done some testing and we are going to promote it and use it in our production environment. This is a very exciting new tool for us.

Datadog can be improved because sometimes it seems it has not been developed for enterprises. We work with over 300 customers, with each customer having multiple instances or apps within Datadog. We are facing difficulties in controlling access, in privacy settings, and splitting usage and costs for these customers.

We want to be able to customize the cost part, and we would appreciate more granular access control.

I have been using Datadog for four or five years.

Datadog is stable.

We have never had an issue with Datadog's scalability.

Datadog's customer support is good; it could be improved in terms of communication, but it is adequate.

We previously used Grafana, AppDynamics, New Relic, Splunk, and a couple of other smaller, more dedicated tools.

My experience with pricing, setup cost, and licensing is good; nothing out of the ordinary.

Before choosing Datadog, the biggest contender we evaluated was AppDynamics.

My advice for others looking into using Datadog is to test it out and see if it works for you. Try to become accustomed to the tagging part of things, and go through each product to understand what each product within Datadog is offering. I would rate this product an eight out of ten.

Datadog's main use case is end-to-end monitoring that helps check problems across infrastructure, application, database, security, and logs.

For example, when checking a problem with a mobile application such as an error from a user hitting a transaction, we check from the client-side mobile device and also from the back end for the API to see if there is latency or an error that triggers the problem. There may be an issue on the database, such as a locking query or high latency on query performance. For infrastructure, if the application is slow, it may be impacted on infrastructure monitoring by CPU and memory consumption.

Datadog is a powerful observability tool that allows us to correlate and see problems on the infrastructure or application side. In an incident war room, we can see the correlation and the detailed root cause of the problem across real user monitoring, application, database, and infrastructure.

Datadog has positively impacted our organization because our customers are very happy using it. With silo monitoring, where infrastructure has separate monitoring, application has another, and cloud has another, it becomes tricky and complex. We cannot correlate the silo monitoring, and pricing is complicated. With Datadog, we can centralize and use one observability tool for monitoring all components across all features or modules, unifying the monitoring process.

Regarding specific outcomes, I observe that tools with Datadog's capabilities enable us to quickly achieve mean time to detect problems. We can specifically check the root cause analysis of issues from the infrastructure, application, database, or security sides. Mean time to resolve is improved with Datadog since it provides many suggestions and actions to resolve problems, which heavily impacts the business for our application customers when issues arise.

Datadog's best feature is real user monitoring.

I prefer Datadog's real user monitoring most because of its analytics capabilities. First, Datadog is recognized in the Gartner Digital Experience for real user monitoring. Second, the analytics capability is very powerful, enabling us to check the experience of customers first. We can also correlate with the back-end side of the performance for real user monitoring and application monitoring. Finally, the capability of metrics within real user monitoring provides many helpful insights for mobile developers to improve their mobile application performance.

Datadog could improve its pricing because it is very tricky, and most of our customers notice many hidden costs. Additionally, if possible, Datadog should offer deployment options not only for SaaS but also for on-premises solutions, which would benefit banking transactions.

Regarding pricing, it remains tricky with many hidden costs. For technological enhancement, there could be an on-premises option alongside the SaaS version. I also find setting up and configuring Datadog to be very complex.

Positive

My experience with pricing, setup costs, and licensing is that it is very tricky due to many hidden costs, so we need to check repeatedly for allotments and commitments regarding what we receive from the license.

My advice for others looking into using Datadog is to initially simplify the technical setup and configuration. Secondly, regarding pricing mechanisms, it would be wise to commit to clear allotments to avoid hidden costs for customers, as it significantly impacts pricing.

I believe Datadog is the largest single observability platform, with correlation as a differentiation factor, enterprise readiness as a measure, and cost management now being a key topic with a very clear roadmap and direction. I would rate this product nine out of ten.

My main use case for Datadog is unified observability, as I use it to correlate metrics, traces, and logs in a single pane of glass to ensure the health and security of our cloud infrastructure and application.

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 is an incredibly powerful daily driver for any engineer, and the recent addition of LLM observability for AI apps and Cloud Security Management makes it feel like a platform that is truly keeping up with modern tech trends. The dashboarding and alert integrations are great features offered by Datadog, giving us all the required information on a single screen, and the alert integration performs its job in a very good manner.

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.

Datadog is a platform that can be improved by making its pricing more predictable, as sometimes it is difficult to forecast exactly how much a new project will cost until after we have started ingesting the data.

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 have been using Datadog for about three years to monitor our cloud-native application and infrastructure across multiple environments.

Datadog is extremely stable, as it is built for high scalable environments and consistently maintains high availability, which is why I trust it as our primary monitoring tool.

Datadog is built for hyperscale, as it automatically scales when we add new hosts or containers, and its Monitoring as Code approach via Terraform allows us to scale our monitoring setup instantly as our infrastructure grows.

Their technical documentation is some of the best in the industry, and their support engineers are very proactive, helping us optimize the ingestion cost.

I previously used a mix of open-source tools like Prometheus and Grafana, and I switched because manual upkeep was too high and I needed a platform that could handle logs and traces alongside metrics without having to manage the backend storage.

Buying Datadog through the AWS Marketplace was seamless and helped me meet AWS spending commitments, and while Datadog's custom metric pricing can be complex, the setup cost is very low because the agent is easy to deploy.

I have seen a strong ROI through a thirty percent reduction in downtime and significant cost savings by identifying under-utilized cloud resources, for example, the ideal EC2 instances through their cloud cost management.

I evaluated New Relic, Dynatrace, and Amazon CloudWatch before choosing Datadog, and I chose Datadog because of its massive library of over seven hundred integrations and its superior user interface, which is easier for our developers to use daily.

My biggest advice is to set up ingestion rules and filters early, as you should not send all your logs and metrics at once, and being selective about what you need to store can maximize your ROI from day one. I would rate this review as an eight.