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 are using a mixture of on-prem and cloud solutions to bridge the gap with healthcare entities in the service of providing patients with the medication they need to live healthy lives.

Since we're a heavily regulated company, a lot of our solutions grew from on-premises monoliths. However, as we scaled out, it became harder and harder to move forward with that architecture. Today, we're investing heavily in transforming our systems from monoliths into distributed systems.

With this change in mind, the ability for us to connect the dots using Datadog has been invaluable.

We have an API that serves as a critical aspect of our system for generating new requests for us to process in service of a patient. This service has many tentacles, and it was always hard to track down how issues from this API are affecting things downstream. Since we've added more instrumentation in this API, Datadog has changed our status from a reactive posture to a proactive one.

It has also served as a prime example to other applications on what the benefit of a well-instrumented system is for that application and other applications around it. Due to this, more and more people are using Datadog.

We like the distributed tracing and flame graphs for debugging. This has been invaluable for us during periods of high traffic or red alert conditions. It has also informed our developers on how our various systems are interconnected and the downstream effects of the problems we might encounter for certain services.

We're still working on getting widespread adoption of these products. Still, we're already seeing a shift in the developer's perspective from application-specific and starting to look at things from a more holistic systems perspective.

While this is not part of the question, this is relevant: Now that I've learned more about RUM, this will be something that we will heavily leverage moving forward to give us a whole complete view of our system from the front and back end perspective.

Once Datadog has gained wide adoption, it can often be overwhelming to both know and understand where to go to find answers to questions. Currently, we use a combination of documentation and COPs to ensure that folks know how to leverage what we have in Datadog properly.

While the guides for Datadog go a long way, a way to customize the user experience from "advanced" to "novice" mode would go a long way.

I've been using the solution for two years.

It has never failed us and therefore I consider it to be very stable.

It's magic. For the most part, we just installed the product and a lot of it just worked out of the box.

Technical support is excellent.

Positive

We have used Splunk, Sentry, and a suite of hand-made solutions. We switched since the Datadog solution was both comprehensive and cohesive. It was also easier to onboard people since the solution was well-documented and standardized.

For the most part, it was really painless to set up.

We implemented the solution in-house.

We're still early on in our transformation process. That said, we are gaining a lot of steam in terms of adoption. Both the engineering team and the product team are seeing tremendous value from this solution.

Adding more tooltips and links to documentation or how-tos within the application would really go a long way for those trying to get their feet wet with Datadog.

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. 

I have been using Datadog products and capabilities increasingly over the last 4 years, from POC to widespread adoption. 

The capabilities we use are unique for each use case and can be combined in various ways to provide the full observability coverage needed to maintain stable operations and shift from becoming more reactive to proactive. 

Our organization uses both site/service reliability for the range of backend and frontend services, custom monitoring, and dashboards that can be dynamic and reused for multiple teams.

The capabilities we use are unique for each use case. They can be combined in various ways to provide the full observability coverage needed to maintain stable operations in order to become more proactive. 

Our organization uses both site/service reliability for backend and frontend services. Custom monitoring and dashboards that can be dynamic and reused for multiple teams. 

We continue to increase the size of our footprint as we get more and more positive experiences.

The APM, RUM, synthetics, and infrastructure host maps have been some of the most popular and commonly used features. 

Overall, the Data UI and the usability of customer features continue to improve. 

The RUM session data and replays are much more convenient and applicable than other tools I have worked with in the past, and by combining multiple capabilities or features together, there is full visibility across the technology stacks and can identify specific bottlenecks or areas for risk and vulnerabilities to be likely to exist. 

Watchdog insights take the work out of the hardest part, helping us identify the issues before our customers.

It is very difficult to make the solutions fit perfectly for large organizations, especially in terms of high cardinality objects and multi-tenancy, where the data needs to be rolled up to a summarized level while maintaining its individual data granularity and identifiers. Tagging is imperative. However, the solutions could be improved for these needs in the future.

I've used the solution for over four years now.

The stability is excellent.

You can work with engineering to make it work for your needs. They are excellent at supporting their customers.

Technical support is excellent.

Neutral

I previously used New Relic, App Dynamics, Heap, Clicktale, and more. Datadog has incorporated many of the features we were looking for into a one-stop shop.

The initial setup is simple and straightforward.

We had an in-house team working directly with Datadog engineering support and technical enablement.

We looked into New Relic, App Dynamics, Heap, Clicktale, and more. Datadog has many of the features we were looking for in one place.

We use all versions of the solution.

The main use cases are to provide visibility to costs for each product in the company as well as to consolidate all the observability in one tool. We are moving the team from being an operational team that needs to keep the tool up and running (applying patches and resolving problems) to a team that is focused on providing meaningful visibility of the systems, applications, and services of the company. We want to add value where the developers and the systems administrators are not able to focus.

The organization changed from having a team to operate different tools and providers to being a team worried about enabling and creating different dashboards, alerts, and automations in order to reduce downtime and increase the visibility of all the products, systems, and applications used. 

We moved from a full operation team to a team that adds value to IT, finance, product, back office, and any other team that requires correct information about the services provided while providing the possibility for them to create their own views and dashboards.

The tags are quite useful. They are providing the capability to give meaning to on-premises hardware (since it was not possible outside of cloud solutions and containers) as well to tag traces and logs. 

The full stack of integrations made it easier to monitor the different technologies and platform providers, including Software as a Service providers, that otherwise would need a lot of work and customization to be able to see what is happening. We'd also need to use several other separate tools that would require an increase in the required staff to operate them. Datadog gave us the opportunity to have a single platform for observability.

The product could be improved by providing remote control to agents, enabling them to execute automation and collections without requiring another automation tool or integration. 

Also, there is a lot of space for the FinOps discipline. For example, it could potentially provide better and richer information for the teams to check the costs and optimize the product.

I've used the solution for one year.

The stability is very good even though we have had some minor problems recently.

The scalability is very good. We've had no problems until now.

Technical support is good. That said, we had some cases that needed to be escalated to get to a faster resolution.

Positive

We previously used AppDynamics. The tool was not providing good system visibility as it was limited and had a very high cost.

The initial setup is somewhat complex. There is a need to create a new automation to install and deploy agents that needs to consider the required security for a financial company.

We handled the implementation in-house.

The ROI is still being calculated.

Users need to be aware of licensing control. With autodiscovery, the product can begin to come at a high cost.

We also looked into Splunk, ELK, and Dynatrace.

We use Datadog for three main use cases, including:

• Infrastructure and application monitoring. It is ensuring that our services are available and performant at all times. This allows us to proactively address incidents and outages without customers contacting us. This includes monitoring of cloud resources (databases, load balancers, CPU usage, etc.), high-level application monitoring (response times, failure rates, etc.), and low-level application monitoring (business-oriented metrics and functional exceptions to customer experience.
• Analyzing application behavior, especially around performance. We often use Datadog's application performance monitoring on non-production environments to evaluate the impact of newly introduced features and gain confidence in changes.
• End-to-end regression testing for APIs and browser-based experiences. Using Datadog's synthetic testing checks periodically that the system behaves in the exact correct way. This is often used as a canary to detect issues even before users reach them organically.

Since we integrated Datadog, we have had increased confidence in the quality of our service, and we had an easier time increasing our delivery velocity. 

We have seen time after time that the monitors we have carefully created based on all ingested data are detecting issues quickly and accurately. 

This means we allow ourselves to manually test things less frequently. We have also had an easier time investigating application errors and slowness using Datadog's APM and log explorer products which allow us to introspect any part of the system, in its execution context.

The most valuable features include:

• Integrated observability data ingestions: All data that Datadog collects is connected. This allows easily connected logs with failed requests, and slow database questions with services and requests.
• Broad integrations allow us to monitor our entire production environment in a single place, not just cloud resources. Since all parts stream metrics, logs, and events to Datadog, we can have unified dashboards and manage monitors and incidents all from the same page.
• A high level of configuration. We can configure and modify many parts, from how data is collected from our applications to how Datadog parses and visualizes it. This means that we always get the best experience, and we don't need to find ten different products that do small things well or settle on one product that does everything badly.

Since the Datadog platform has so many separate features, solving so many use cases, there are often inconsistencies in feature availability and interoperability between products. 

Older, more mature products tend to be complete (many features, customization, broad integrations, etc.), while newer products will often be at a "just above minimum viable product" phase for a long time, doing what's intended yet missing valuable customizations and integrations.

We've used the solution for 12 months.

The solution scales very well on technical aspects, being able to ingest large quantities of data from many services. However, the pricing often doesn't scale naturally, and effort has to be put in to keep ongoing costs at a reasonable amount.

Customer service and support are generally very high-quality. In most cases, they reply very quickly and offer well-researched and relevant responses. This is contrasted with many vendors who take a long time to reply and send links to documentation instead of understanding the problem.

However, we had cases where support took several weeks to reply to a complicated request and sometimes eventually responded that the issue cannot be resolved. These are rare edge-case occurrences.

Positive

A large part of the initial setup was straightforward. We were able to collect about 80% of the relevant and 90% of the meaningful insights from just a couple of hours of connecting the AWS integration and the Datadog APM agent. 

Getting it to 100% and configuring and customizing things to our unique situation, took about two weeks. Datadog's documentation and support team were extremely helpful during both phases.

We handled the setup in-house.

From the number of outages stopped or shortened (which lead to lost revenue from non-renewals) and the number of hours saved on investigations (which correlates to engineering salaries), I estimate that the ROI of the implementation time and monthly charges to be between 10x and 20x.

We use the solution as a SaaS deployment.