Datadog Reviews

Our primary use case for Datadog involves utilizing its dashboards, monitors, and alerts to monitor several key components of our infrastructure. 

We track the performance of AWS-managed Airflow pipelines, focusing on metrics like data freshness, data volume, pipeline success rates, and overall performance. 

In addition, we monitor Looker dashboard performance to ensure data is processed efficiently. Database performance is also closely tracked, allowing us to address any potential issues proactively. This setup provides comprehensive observability and ensures that our systems operate smoothly.

Datadog has significantly improved our organization by providing a centralized platform to monitor all our key metrics across various systems. This unified observability has streamlined our ability to oversee infrastructure, applications, and databases from a single location. 

Furthermore, the ability to set custom alerts has been invaluable, allowing us to receive real-time notifications when any system degradation occurs. This proactive monitoring has enhanced our ability to respond swiftly to issues, reducing downtime and improving overall system reliability. As a result, Datadog has contributed to increased operational efficiency and minimized potential risks to our services.

The most valuable features we’ve found in Datadog are its custom metrics, dashboards, and alerts. The ability to create custom metrics allows us to track specific performance indicators that are critical to our operations, giving us greater control and insights into system behavior. 

The dashboards provide a comprehensive and visually intuitive way to monitor all our key data points in real-time, making it easier to spot trends and potential issues. Additionally, the alerting system ensures we are promptly notified of any system anomalies or degradations, enabling us to take immediate action to prevent downtime. 

Beyond the product features, Datadog’s customer support has been incredibly timely and helpful, resolving any issues quickly and ensuring minimal disruption to our workflow. This combination of features and support has made Datadog an essential tool in our environment.

One key improvement we would like to see in a future Datadog release is the inclusion of certain metrics that are currently unavailable. Specifically, the ability to monitor CPU and memory utilization of AWS-managed Airflow workers, schedulers, and web servers would be highly beneficial for our organization. These metrics are critical for understanding the performance and resource usage of our Airflow infrastructure, and having them directly in Datadog would provide a more comprehensive view of our system’s health. This would enable us to diagnose issues faster, optimize resource allocation, and improve overall system performance. Including these metrics in Datadog would greatly enhance its utility for teams working with AWS-managed Airflow.

I've used the solution for four months.

The stability of Datadog has been excellent. We have not encountered any significant issues so far. 

The platform performs reliably, and we have experienced minimal disruptions or downtime. This stability has been crucial for maintaining consistent monitoring and ensuring that our observability needs are met without interruption.

Datadog is generally scalable, allowing us to handle and display thousands of custom metrics efficiently. However, we’ve encountered some limitations in the table visualization view, particularly when working with around 10,000 data points. In those cases, the search functionality doesn’t always return all valid results, which can hinder detailed analysis.

Datadog's customer support plays a crucial role in easing the initial setup process. Their team is proactive in assisting with metric configuration, providing valuable examples, and helping us navigate the setup challenges effectively. This support significantly mitigates the complexity of the initial setup.

We used New Relic before.

The initial setup of Datadog can be somewhat complex, primarily due to the learning curve associated with configuring each metric field correctly for optimal data visualization. It often requires careful attention to detail and a good understanding of each option to achieve the desired graphs and insights

We implemented the solution in-house.

Our primary use case for Datadog is to monitor, analyze, and optimize the performance and health of our applications and infrastructure. 

We leverage its logging, metrics, and tracing capabilities to pinpoint issues, track system performance, and improve overall reliability. 

Datadog’s ability to provide real-time insights and alerting on key metrics helps us quickly address issues, ensuring smooth operations. It’s integral for visibility across our microservices architecture and cloud environments.

Datadog has been incredibly valuable to our organization. Its ability to pinpoint warnings and errors in logs and provide detailed context is essential for troubleshooting. 

The platform's request tracing feature offers comprehensive insights into user flows, allowing us to quickly identify issues and optimize performance. 

Additionally, Datadog's real-time monitoring and alerting capabilities help us proactively manage system health, ensuring operational efficiency across our applications and infrastructure.

Being able to filter requests by latency is invaluable, as it provides immediate insight into which endpoints require further analysis and optimization. This feature helps us quickly identify performance bottlenecks and prioritize improvements. 

Additionally, the ability to filter requests by user email is extremely useful for tracking down user-specific issues faster. It streamlines the troubleshooting process and enables us to provide more targeted support to individual users, improving overall customer satisfaction.

The query performance could be improved, particularly when handling large datasets, as slower response times can hinder efficiency. 

Additionally, the interface can sometimes feel overwhelming, with so much happening at once, which may discourage users from exploring new features. 

Simplifying the layout or providing clearer guidance could enhance user experience. Any improvements related to query optimization would be highly beneficial, as it would further streamline workflows and boost productivity.

I've used the solution for five years.

We have several teams and several different projects, all working in tandem, so there are a lot of logs and monitoring that need to be done. We use Datadog mostly for alerting when things go down. 

We also have several dashboards to keep track of critical operations and to make sure things are running without issues. The Slack messaging is essential in our workflow in letting us know when an alert is triggered. I also appreciate all the graphs you can make, as it gives our team a good overview of how our services are doing.

It has improved our reliability and our time to get back up from an outage. By creating an alert and then messaging a Slack channel, we know when something goes down fairly fast. This, in turn, improves our response time to swarm on an issue without it affecting customers. The graphs have also been useful to demonstrate to higher-ups how our services are performing, allowing them to make more informed decisions when it comes to the team. 

The alerts are the most valuable. Having alerts have saved us countless times in the past and is essentially what we use data dog for. 

I like how we can customize alerts, and when alerts have become too noisy, we turn their threshold down fairly easily. This is also the case when alerts should be notifying us more often. 

I also like the graphs and how customizable they are. It allows us to create a nice-looking dashboard with all sorts of information relating to our project. This gives us a quick overview of how things are going.

It's not that straightforward when creating an alert. The syntax is a little confusing. I guess that the trade-off is customizability. But it would be nice to have a click-and-drag kind of way when creating an alert. So, if someone who isn't so familiar with Datadog or tech in general wanted to create an alert, they wouldn't need to know the syntax. 

It would also be great if AI could be used to generate alerts and graphs. I could write a short prompt, and then the AI could auto-generate alerts and graphs for me.

I've used the solution for more than two years.

We use this solution for enterprise monitoring across a large number of applications in multiple environments like production, development, and testing. It helps us track application performance, uptime, and resource usage in real time, providing alerts for issues like downtime or performance bottlenecks. 

Our hybrid environment includes cloud and on-premise infrastructure. The solution is crucial for ensuring reliability, compliance, and high availability across our diverse application landscape.

Datadog has greatly improved our organization by centralizing all monitoring into one platform, allowing us to consolidate data from a wide range of sources. 

From infrastructure metrics and application logs to end-user experience and device monitoring, everything is now collected and displayed in one place. This has simplified our monitoring processes, improved visibility, and allowed for faster issue detection and resolution. 

By streamlining these operations, Datadog has enhanced both efficiency and collaboration across teams.

Synthetic testing is by far the most valuable feature in our organization. It’s highly requested since the setup process is both quick and straightforward, allowing us to simulate user interactions across our applications with minimal effort. 

The ease of configuring tests and interpreting the results makes it accessible even to non-technical team members. This feature provides valuable insights into user experience, helps identify performance bottlenecks, and ensures that our critical workflows are functioning as expected, enhancing reliability and uptime.

One area where the product could be improved is Application Performance Monitoring (APM). While it's a powerful feature, many in our organization find it difficult to fully understand and utilize to its maximum potential. 

The data provided is comprehensive, yet it can sometimes be overwhelming, especially for those who are less familiar with the intricacies of application performance metrics. 

Simplifying the interface, offering clearer guidance, or providing more intuitive visualizations would make it easier for users to extract valuable insights quickly and efficiently.

I've used the solution for four years.

The solution is very stable. Issues happen once or twice a year and are usually solved before we have any real impact on the service.

Scalability has never been a bottleneck for us; we've never felt any issues here.

Support is slow at the beginning, however, they are much better and responsive now.

Positive

Datadog offered the most consolidated approach to our monitoring needs.

This was a migration project, so it was rather complex.

We implemented the solution with our in-house team.

I'd recommend new users look down the road and decide on at least a three-year plan.

We evaluated AppDynamics and Dynatrace.

Our primary use cases include:

• Alert on errors customers encounter in our product. We've set up logs that go to slack to tell us when a certain error threshold is hit.
• Investigate slow page load times. We have pages in our app that are loading slowly and the logs help us figure out which queries are taking the longest time.
• Metrics. We collect metrics on product usage.
• Session replays. We watch session replays to see what a user was doing when a page took a long time to load or hit an error. This is helpful.
  

It's helped us find bugs that customers are experiencing before they're reported to us. Sometimes, customers don't report errors, so being able to catch errors before they're reported helps us investigate before other users find errors

Datadog has helped us investigate slow page loading times and even see the specific queries that are taking a long time to load

Logging lets us see the context around an error. For example, see if a backend service had an error before it surfaced on the frontend.

Dashboards are helpful for reviewing occasionally to get a higher-level overview of what's happening.

The most valuable aspects include: 

• Logging. Being able to view detailed logs helps debug issues.
• Session replays. They are helpful for seeing what a customer was doing before they saw an error or had a slow page load
• Alerting. This is an important part of our on-call process to send alerts to slack when an error threshold is crossed. Alerts/monitors are easy to configure to only alert when we want them to alert.
• Dashboards. It's helpful to pull up dashboards that show our most common errors or page performance. It's a good way to see how the app is performing from a birds-eye-view.

The UI has a lot going on. It should be simpler and have a better way to onboard someone new to using Datadog.

The log querying syntax can be confusing. Usually, I filter by finding a facet in a log and selecting to filter by that facet - but I'm not sure how to write the filter myself

The monitor/alert syntax is also somewhat hard to understand.

Overall, it should be easier to learn how to use the product while you're using the product. Perhaps tooltips or a link to learn more about whatever section you're using.

I've used the solution for two years.

We did not previously use a different solution.

We did not evaluate other options. 

We use Datadog for monitoring to get the traces and logs of all our applications. Datadog provides dashboard and alert capabilities to identify if something is wrong with various teams. More than 200 users, mostly software engineers, work with Datadog. 

Datadog provides tracing and logging, whereas Dynatrace focuses on tracing, and Splunk is more of a logging tool. Datadog's advantage is that we don't need two tools. 

Datadog isn't as mature as some of the established players like Dynatrace or Splunk. It's a new product, so they are constantly releasing new features, and I don't have much to complain about.

We have used Datadog for seven months.

We haven't issued any issues so far, so it's a highly stable platform. 

We are a unit within a much larger entity that is using Datadog. It can scale up to meet your needs. 

We have regular calls with the Datadog team. They take feedback and bring in the product managers to quickly answer questions and fix issues. They help you deal with some of the issues you have with any new product, but Datadog is one of the fastest-growing products in the monitoring space.

You don't need to install anything because it's a SaaS product with a web-based UI. They provide you the credentials to give you admin access. You only need to install the agents where you need monitoring. The time required to deploy the agent depends on what you're monitoring, but the solution itself works like Office 365 or any other SaaS product. 

The primary purposes for which Datadog is used include infrastructure monitoring and application monitoring.

The main use case for Datadog integration capabilities is to monitor workloads in public cloud, and those public cloud integrations that reached the public cloud metric natively were helpful or critical for us. We are not using Datadog for AI-driven data analysis tasks, but more cloud-native and vendor-native tools at the moment, and at the time when I was still in my last employer, we didn't use Datadog for the AI piece at all.

I find alerting and metrics to be the most effective features of Datadog for system monitoring. It was still cheaper to run Datadog than other alternatives, so the running costs were cheaper because it was SaaS and quite easy to use.

Datadog is only available in SaaS.

The pricing nowadays is quite complex.

In future updates, I would like to see AI features included in Datadog for monitoring AI spend and usage to make the product more versatile and appealing for the customer.

I have been using Datadog since 2014.

There were no problems with the deployment of Datadog.

The deployment of Datadog just took a few hours.

The challenges I encountered while using Datadog were in the early days when the product was missing the ability to monitor Kubernetes and similar features, but they have since added those features. At the moment, I don't think there are too many challenges that I am worrying about.

One person is enough to do the installation.

I am not working with any of these solutions currently because I'm on sabbatical, but I used to work with Datadog six months ago, and now at the moment I'm on sabbatical.

We were using the tools that AWS and Azure came with natively to monitor the AI workflows on their platforms.

I used to work as the CTO at Northcloud, but I no longer work there.

On a scale of one to ten, I rate Datadog an eight out of ten.

The soluton is used for full stack enterprise performance monitoring for our primarily cloud-based stack on AWS. We have implemented monitoring coverage using RUM for critical apps and websites and utilize APM (integrated with RUM) for full stack traceability.  

We use Datadog as our primary log repository for all apps and platforms, and the advanced log analytics enable accurate log-based monitoring/alerting and investigations. 

Additionally, we some advanced RUM capabilities and metrics to track and optimize client-side user experience. We track SLO's for our critical apps and platforms using Datadog.

We now have full-stack observability, which allows us to better understand application behavior, quickly alert users about issues, and proactively manage application performance.  

We've seen value by implementing observability coordinated across multiple applications, allowing us to track things like customer shopping and orders across multiple applications and services.  

For critical application launches, we've built dashboards that can track user activity and confirm users are able to successfully utilize new features, tracking user activities in real-time in a war-room situation.  

Datadog is our go-to tool for analyzing, understanding, and investigating application performance and behavior.

APM accurately tracks our service performance across our ecosystem. RUM gives us client-side performance and user experience visibility, and the rate of new features implemented in the Digital Experience area recently has been high. Log analytics give us a powerful mechanism for error tracking, research, and analysis.  

Custom metrics that we've created allow us to track KPIs in real-time on dashboards. All of these have proven valuable in our organization.  Additionally, Datadog product support teams are responsive and have provided timely support when needed.

Agent remote configuration should be provided/improved and streamlined, allowing for config changes/upgrades to be performed via the portal instead of at the host.   

Cost tracking via the admin portal is a bit lacking, even though it has gotten better.  I'm looking for usage trends (that drive cost) across time and better visibility or notifications about on-demand charges.  

Network device and performance monitoring could be improved, as we've faced some limitations in this area.  

The Datadog usage-based cost model, while giving us better transparency, is difficult to follow at times and is constantly evolving.  

I've used the solution for three years.

Support has been responsive and helpful.  

Positive

Pricing is straightforward. That said, it's sometimes difficult to estimate usage volumes.

We evaluated Datadog and New Relic in detail and chose Datadog due to their straightforward and competitive pricing model, and their full coverage of monitoring features that we desired, and an easy-to-use UI.  

I work in product design, and although we use Datadog for monitoring, etc, my use case is different as I mostly review and watch session recordings from users to gain insight into user feedback.

We watch multiple sessions per week to understand how users are using our product. From this data, we are able to hone in on specific problems that come up during the sessions. We then reach out to specific users to follow up with them via moderated testing sessions, which is very valuable for us.

Using Datadog has allowed us to review detailed interactions of users at a scale that leads us to make informed data-driven UX improvements as mentioned above.

Being able to pinpoint specific users via filtering is also very useful as it means when we have direct feedback from a specific user, we can follow up by watching their session back. 

The engineering team's use case for Datadog is for alerting, which is also very useful for us as it gives us visibility of how stable our platform is in various different lenses.

Session recordings have been the most valuable to me as it helps me gain insights into user behaviour at scale. By capturing real-time interactions, such as clicks, scrolls, and navigation paths, we can identify patterns and trends across a large user base. This helps us pinpoint usability issues, optimize the user experience, and improve the overall experience for our users. Analyzing these recordings enables us to make data-driven decisions that enhance both functionality and user satisfaction.

I'd like the ability to see more in-depth actions on user sessions, such as where there are specific problems and rather than having to watch numerous session recordings to understand where this happens to get alerts/notifications of specific areas that users are struggling with - such as rage clicks, etc.

In terms of UI, everything is very small, which makes it quite difficult to navigate at times, especially in terms of accessibility, so I'd love for there to be more attention on this.

I've used the solution for over one year.

We did not evaluate other options. 

I wasn't part of the decision-making process during licensing.

I wasn't part of the decision-making process during the evaluation stage.

We use Datadog as our main log ingestion source, and Datadog is one of the first places we go to for analyzing logs. 

This is especially true for cases of debugging, monitoring, and alerting on errors and incidents, as we use traffic logs from K8s, Amazon Web Services, and many other services at our company to Datadog. In addition, many products and teams at our company have dashboards for monitoring statistics (sometimes based on these logs directly, other times we set queries for these metrics) to alert us if there are any errors or health issues.

Overall, at my company, Datadog has made it easy to search for and look up logs at an impressively quick search rate over a large amount of logs. 

It seamlessly allows you to set up monitoring and alerting directly from log queries which is convenient and helps for a good user experience, and while there is a bit of a learning curve, given enough time a majority of my company now uses Datadog as the first place to check when there are errors or bugs. 

However, the cost aspect of Datadog is tricky to gauge because it's related to usage, and thus, it is hard to tell the relative value of Datadog year to year.

The feature I've found most valuable is the log search feature. It's set up with our ingestion to be a quick one-stop shop, is reliable and quick, and seamlessly integrates into building custom monitors and alerts based on log volume and timeframes. 

As a result, it's easy to leverage this to triage bugs and errors, since we can pinpoint the logs around the time that they occur and get metadata/context around the issue. This is the main feature that I use the most in my workflow with Datadog to help debug and triage issues.

More helpful log search keywords/tips would be helpful in improving Datadog's log dashboard. I recently struggled a lot to parse text from raw line logs that didn't seem to match directly with facets. There should be smart searching capabilities. However, it's not intuitive to learn how to leverage them, and instead had to resort to a Python script to do some simple regex parsing (I was trying to parse "file:folder/*/*" from the logs and yet didn't seem to be able to do this in Datadog, maybe I'm just not familiar enough with the logs but didn't seem to easily find resources on how to do this either). 

I've used the solution for 10 months.

Beware that the cost will fluctuate (and it often only gets more expensive very quickly).