Datadog Reviews

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.

I think the most important feature for me in Datadog is the RUM features.

I check the efficiency of the applications that I'm supporting in Datadog and also use it to view the sessions of users.

I have some trouble doing troubleshooting in our app currently, but RUM is my main use case in Datadog.

The personalized dashboards and alerting in Datadog stand out to me, so that way you can gear your use of the product towards what's important to you.

Datadog has allowed us to ensure that we can look at how our beta testers are using our new UIs and seeing where their frustration points are, which has been important to us.

We've been using the heat map feature in Datadog to measure those frustration points.

Some templates for certain roles and things that users care about could be auto-suggested for a dashboard or alerting in Datadog.

We had limitations around RUM and our feature flag provider in Datadog because it's a back-end forward feature flag usage in our Next.js application. We had trouble hooking up our feature flags due to RUM being client-side only. This issue arose because Next.js is a front-end and back-end focused application, and it would be beneficial to send the feature flag resolution from the back-end if needed. Our feature flag provider is GrowthBook, and the way we would have to get those feature flags into Datadog was time-consuming with a lot of boilerplate. We would have to mimic feature flag resolution on the client side, so we decided to forego that.

We have been using Datadog for about two or three months.

Datadog seems stable in my experience without any downtime or reliability issues.

Datadog is scalable and I don't think we'll have problems with scalability in terms of our use case. We might face limitations with logs, but I feel we would not be reaching any of Datadog's limits.

The customer support has been one of the best parts of Datadog.

I would rate the customer support from Datadog a 10 on a scale of 1 to 10.

I would suggest staying in close contact with your customer support representative to get the most out of Datadog.

Positive

We did not have a different solution before Datadog.

Setup with Datadog was pretty easy.

It is too early to tell if we've seen a return on investment so far with Datadog.

I'm not clear on pricing, but it's not a huge concern for us at the moment in terms of RUM. For the other pieces, I know that there may be some pricing that they've been looking at for APM and logs.

I did not evaluate other options before choosing Datadog.

I personally don't use the personalized dashboards and alerting, but I've seen some nice use cases from others on my team. On a scale of 1-10, I rate Datadog an 8.

My main use case for Datadog is 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.

My main use case for Datadog is application monitoring and alerting.

A specific example of how I use Datadog for application monitoring and alerting is monitoring for storage filling up.

I also monitor services to ensure that they're running when they should be, and then I schedule downtimes for whenever they shouldn't be.

In my experience, the best features Datadog offers are integrations with ServiceNow and PagerDuty and the large variety of other third-party integrations.

The integrations with ServiceNow and PagerDuty have helped my workflow because whenever there's an issue, we can get notified quickly, and whoever is on call, if it's after hours, can be notified that there's an issue going on.

Dashboards are nice for quick and easy access to important and useful information, and logs are a great place to review information quickly and easily without connecting to the application directly.

Datadog has positively impacted my organization by allowing for a more proactive response to issues whenever they occur.

Being more proactive has helped by reducing downtime and improving our response to resolution. It has helped us limit business impact whenever there are issues that arise.

I believe Datadog could be improved because sometimes it's not the most user-friendly, and when monitors have a new metric or a service that no longer needs to be monitored, it remains in the system. It could be user error, but it would be nice to remove a specific service or part of a monitor from continuing to be monitored if there's no data being collected anymore.

Documentation sometimes is a little misleading or confusing, and there are multiple versions available, so having more up-to-date or clearer documentation regarding which version it applies to would be good.

I have been using Datadog for two, two and a half years.

Datadog is stable.

Datadog's scalability has been pretty scalable from what we've done in our organization.

The customer support is very good; it's easy to get support on pretty much any question that we have, including being able to chat in.

Positive

We previously used LogicMonitor, and I was not involved in the discussions on why we switched.

It's a pretty steep learning curve to start using Datadog; it takes time to really configure everything.

I would say we have seen a return on investment, but I don't have any relevant metrics.

My experience with pricing, setup cost, and licensing is that it was good; I wasn't too involved with it, but as far as I know, it was smooth.

Before choosing Datadog, we did evaluate other options, but I'm not sure what those options were.

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

My main use case for Datadog is monitoring and collecting metrics. I use it to collect metrics from Kubernetes pod CPU and memory usage, and also logging, basically all our middleware platforms.

The best features Datadog offers are the ability to collaborate between different metrics such as logging, metrics, and APM, which helps me to pinpoint when I'm troubleshooting issues. The dashboard is very useful; I can use it to get a glance on how the system performs, and alerting is what I'm using right now to send notifications to either email or PagerDuty.

Datadog has positively impacted my organization by shortening our time to resolve incidents because it's a central place for getting all the data that we need for troubleshooting.

I think Datadog can be improved by adding anomaly detection, that would be nice. The user interface is okay, but sometimes cost is the issue because for logging, I had to actually trim down my logs because the cost is too much.

I have been using Datadog for several years.

Datadog is stable.

Datadog's scalability is quite good since it's a SaaS solution, and there are no scalability issues for me. I simply install an agent for whatever new component, server, or host I want to monitor, and then I'm good.

The customer support is hit and miss. Sometimes they respond fairly quickly, but it depends on the person, and it may take a couple of communications for them to actually understand what I need.

Positive

I previously used some open-source solutions from other vendors before Datadog. The switch was made to get a better observability stack.

My experience with pricing, setup cost, and licensing indicates that the pricing is based on usage. When we adopt more, we get more, so everything is based on our desire to improve adoptability for the entire studio, then cost becomes a main issue.

Before choosing Datadog, I evaluated other options, including Dynatrace, which was approximately 10 years ago.

My advice to others looking into using Datadog is that if cost is not a concern, I would recommend them to use it. However, if they are sensitive or concerned about how much money they want to spend, then maybe Datadog is not the solution for them.

I would rate Datadog overall as eight out of ten, though I find it too costly.

My main use case for Datadog is to analyze data in regards to instant funding.

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

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

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

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

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

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

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

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

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

I have been using Datadog for roughly six months.

Datadog is stable.

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

The customer support of Datadog is amazing.

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

Positive

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

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

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

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

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

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

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

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

My main use case for Datadog is monitoring 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.

My main use case for Datadog is performance monitoring, SLOs, and SLIs.

For performance monitoring, SLOs, and SLIs, we create objectives and indicators around user feedback and stakeholder feedback. We have weekly meetings to create backlog items to work on if things have elapsed and gone into the red based on our SLO definitions.

The best features Datadog offers are the analytics that are all associated with each other. RUM data associated with APM, trace data, and all of that, including information around inferred requests, has been super useful. Machine health data gives a complete picture of performance, which has been extremely useful for troubleshooting difficult problems.

Having all that associated analytics helps me in troubleshooting by not having to bounce around to other tools, which saves me a lot of time. I know that the quality of Datadog metrics gathered is enough to where I can rule things in and out. This basically goes for any web app; when asking why a web app is slow, first you look at the code. If the code looks good, then you look at the hardware or the database. Being able to rule all of those out with one tool with one set of requests is useful.

Datadog has positively impacted my organization by allowing us to gather complete data instead of looking all over the place at incomplete data and actually make pointed determinations for fixing issues. It has helped increase efficiency and saved time.

I don't know how Datadog can be improved as they are doing a pretty good job.

I have been using Datadog for three years.

Datadog is stable.

Datadog's scalability is good.

The customer support is good.

My experience with pricing, setup cost, and licensing is that it is really expensive.

I have not seen a return on investment.

My experience with pricing, setup cost, and licensing is that it is really expensive.

My advice to others looking into using Datadog is that it is good and they should use it.

I don't know if my company has a business relationship with this vendor other than being a customer.

On a scale of 1-10, I rate Datadog a 9.