Datadog Reviews

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.

I have been using Datadog for two years.

Datadog is very stable, and the features are quickly updated because the research and development process moves swiftly, making it reliable for fixes and updates.

Datadog's scalability is very strong due to its cloud-native distributed architecture, massive data capability, extensive integration ecosystem, seamless expansion, and real-world scalability evidence.

Customer support is very good because there is extensive support from Datadog, including live chat, ticketing, and a very high SLA of 99.98%.

I was using Instana and Dynatrace as different solutions before Datadog.

I have seen a return on investment because Datadog helps save money and reduces the need for fewer employees while also saving time, which is very beneficial.

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.

I evaluated other options before choosing Datadog, specifically Dynatrace.

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.

Hybrid Cloud

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.

Hybrid Cloud

Amazon Web Services (AWS)

My main use case for Datadog involves working on projects related to our sales reps in terms of registering new clients, and I've been using Datadog to pull up instances of them while they're beta testing our product that we're rolling out just to see where their errors are occurring and what their behavior was leading up to that.

I can't think of all of the specific details, but there was a sales rep who was running into a particular error message through their sales registration process, and they weren't giving us a lot of specific screenshots or other error information to help us troubleshoot. I went into Datadog and looked at the timestamp and was able to look at the actual steps they took in our platform during their registration and was able to determine what the cause of that error was. I believe if I remember correctly, it was user error; they were clicking something incorrectly.

One thing I've seen in my main use case for Datadog is an option that our team can add on, and it's the ability to track behavior based on the user ID. I'm not sure at this time if our team has turned that on, but I do think that's a really valuable feature to have, especially with the real-time user management where you can watch the replay. Because we have so many users that are using our platform, the ability to filter those replay videos based on the user ID would be so much more helpful. Especially in terms where we're testing a specific product that we're rolling out, we start with smaller beta tests, so being able to filter those users by the user IDs of those using the beta test would be much more helpful than just looking at every interaction in Datadog as a whole.

The best features Datadog offers are the replay videos, which I really find super helpful as someone who works in QA. So much of testing is looking at the UI, and being able to look back at the actual visual steps that a user is taking is really valuable.

Datadog has impacted our organization positively in a major way because not even just as a QA engineer having access to the real-time replay, but just as a team, all of us being able to access this data and see what parts of our system are causing the most errors or resulting in the most frustration with users. I can't speak for everybody else because I don't know how each other segment of the business is using it, but I can imagine just in terms of how it's been beneficial to me; I can imagine that it's being beneficial to everybody else and they're able to see those areas of the system that are causing more frustration versus less.

I think Datadog can be improved, but it's a question that I'm not totally sure what the answer is. Being that my use case for it is pretty specific, I'm not sure that I have used or even really explored all of the different features that Datadog offers. So I'm not sure that I know where there are gaps in terms of features that should be there or aren't there.

I will go back to just the ability to filter based on user ID as an option that has to be set up by an organization, but I would maybe recommend that being something part of an organization's onboarding to present that as a first step. I think as an organization gets bigger or even if the organization starts using Datadog and is large, it's going to be potentially more difficult to troubleshoot specific scenarios if you're sorting through such a large amount of data.

I have been working in this role for a little over a year now.

As far as I can tell, Datadog has been stable.

I believe we have about 500 or so employees in our organization using our platform, and Datadog seems to be able to handle that load sufficiently, as far as I can tell. So I think scalability is good.

I haven't had an instance where I've reached out to customer support for Datadog, so I do not know.

I do not believe we used a different solution previously for this.

I cannot answer if I have seen a return on investment; I'm not part of the leadership in terms of making that decision. Regarding time saved, in my specific use case as a QA engineer, I would say that Datadog probably didn't save me a ton of time because there are so many replay videos that I had to sort through in order to find the particular sales reps that I'm looking for for our beta test group. That's why I think the ability to filter videos by the user ID would be so much more helpful. I believe features that would provide a lot of time savings, just enabling you to really narrow down and filter the type of frustration or user interaction that you're looking for. But in regards to your specific question, I don't think that's an answer that I'm totally qualified to answer.

I was not part of the decision-making process before choosing Datadog, so I cannot speak to whether we evaluated other options.

Right now our users are in the middle of the beta test. At the beginning of rolling the test out, I probably used the replay videos more just as the users were getting more familiar with the tool. They were probably running into more errors than they would be at this point now that they're more used to the tool. So it kind of ebbs and flows; at the beginning of a test, I'm probably using it pretty frequently and then as it goes on, probably less often.

It does help resolve issues faster, especially because our sales reps are used to working really quickly in terms of the sales registration, as they're racing through it. They're more likely to accidentally click something or click something incorrectly and not fully pay attention to what they're doing because they're just used to their flow. Being able to go back and watch the replay and see that a person clicked this button when they intended to click another button, or identifying the action that caused an error versus going off of their memory.

I have not noticed any measurable outcomes in terms of reduction in support tickets or faster resolution times since I started using Datadog. For myself, looking at the users in our beta test group, none of those came as a result of any sort of support ticket. It came from messages in Microsoft Teams with all the people in the beta group. We have resulted in fewer messages in relation to the beta test because they are more familiar with the tool. Now that they know there might be differences in terms of what their usual flow is versus how their flow is during the beta test group, they are resulting in fewer messages because they are probably being more careful or they've figured out those inflection points that would result in an error.

My biggest piece of advice for others looking into using Datadog would be to use the filters based on user ID; it will save so much time in terms of troubleshooting specific error interactions or occurrences. I would also suggest having a UI that's more simple for people that are less technical. For example, logging into Datadog, the dashboard is pretty overwhelming in terms of all of the bar charts and options; I think having a more simplified toggle for people that are not looking for all of the options in terms of data, and then having a more technical toggle for people that are looking for more granular data, would be helpful.

I rate Datadog 10 out of 10.

Public Cloud

Our main use case for Datadog includes monitoring and logs, custom metrics, as well as utilizing the APM feature and synthetic tests in our day-to-day operations.

A quick specific example of how Datadog helps with our monitoring and logs comes from all our applications sending logs into Datadog for troubleshooting purposes, with alerts built on top of the logs, and for custom metrics, we send our metrics from the applications via Prometheus to Datadog, building alerts on top of those as well, sometimes sending critical alerts directly to PagerDuty.

We generally have monitors and alerts set up for our applications and specifically rely on them to check our critical business units, such as databases; in GCP, we use Cloud SQL, in AWS, we use RDS, and we also monitor Scylla databases and EC2 instances running Kafka services, which we heavily depend upon. Recently, we migrated from US one to US five, which was a significant shift, requiring us to migrate all alerts and monitors to US five and validate their functionality in the new site.

The best feature Datadog offers is its user-intuitive interface, making it very easy to track logs and custom metrics. We also appreciate the APM feature, which has helped reduce our log volumes and custom metric volumes, allowing us to turn off some custom metrics.

We recently learned how tags contribute to custom metrics volume, which led us to exclude certain tags to further reduce that volume, and we implement log indexing and exclusion filters, leaving us with much to explore and optimize in our use of Datadog as our major observability platform.

Regarding metrics showing our improvements, the MTTR has been reduced by about 40% after integrating Datadog with PagerDuty, and we've seen our costs significantly drop in the most recent renewal after three years' contract.

Operationally, we spend about 30-40% less time correlating logs and metrics across services, while potential areas for improvement in Datadog include its integration depth and providing more flexible pricing models for large metric and log volumes.

I would suggest having an external Slack channel for urgent requests, which would enable quicker access to support or a dedicated support team for our needs.

I choose eight because, while we have used Datadog for three years and experienced growth in our business and services, the cost has also increased with the growth in metrics and log volumes, and proactive cost management feedback has not been provided to help manage or budget those rising costs. Thus, I'd like to see more proactive cost management in the future, as the pricing model seems to escalate quickly with increasing metrics ingestion and monitoring across clouds. Datadog is a powerful and reliable observability platform, but there is still room for improvement in cost efficiency and usability at scale.

Regarding pricing, setup costs, and licensing, I find Datadog's pricing model transparent but scaling quickly; the base licensing for host integration is straightforward, but costs can rapidly climb as we add custom metrics and log ingestion, especially in dynamic Kubernetes or multi-cloud environments, with the pricing being moderate to high, and while cost visibility is straightforward, it could become challenging with growing workloads. The upfront setup cost is minimal, mainly involving fine-tuning dashboards, tags, and alerts, making licensing very flexible to enable features as needed.

I have been working in my current field for roughly around 10 years, starting my AWS journey about 10 years ago, mainly focused on infrastructure and observability.

I believe Datadog is stable.

Datadog's scalability is impressive, as it has the necessary integrations, supports agent-based and cloud-native solutions, and accommodates multi-cloud, multi-region features, making overall performance very good.

Customer support has improved recently with online support available through a portal, allowing for quicker access to help.

Neutral

Previously, we used Splunk SignalFx for a couple of years, switching to Datadog because of Datadog's user-intuitive interface, which was lacking in SignalFx at the time.

Datadog has had a significant positive impact on our organization overall, particularly in visibility, reliability, and cost efficiency, allowing us to centralize metrics, logs, and traces across our cloud, moving from reactive to proactive monitoring, with improvements including faster incident detection and resolution, enhanced service reliability, better cost and resource optimization, and shared dashboards providing the engineering and product teams a single source of truth for system health and performance, thus enhancing our overall observability and operational efficiency.

I believe Datadog has delivered more than its value through reduced downtime, faster recovery, and infrastructure optimization; although we sometimes miss critical alerts, overall, it has improved our team's efficiency by maybe 30% less time spent troubleshooting logs and custom metrics while providing measurable ROI through enhanced system reliability, reduced incident costs, and infrastructure spending optimization.

We only evaluated SignalFx before choosing Datadog, as Datadog offered simpler scaling, better management, broader integrations, and dashboards, allowing for easier monitoring of our multi-cloud setup.

After reducing log and custom metric volumes, we notice a significant reduction in costs without any performance issues on our end, actually seeing a lot of cost reductions.

I strongly recommend using Datadog, but suggest being proactive about resource usage and tracking anomalies monthly.

I find the interview process okay, although it runs longer than I expected, exceeding the anticipated 10 minutes.

My rating for Datadog is 8 out of 10.

Public Cloud

Google

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.

Hybrid Cloud

Amazon Web Services (AWS)

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.

Private Cloud

Microsoft Azure

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.

On-premises

Microsoft Azure

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.

On-premises