Datadog Reviews

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).

Our primary use case is custom and vendor-supplied web application log aggregation, performance tracing and alerting. 

Through the use of Datadog across all of our apps, we were able to consolidate a number of alerting and error-tracking apps, and Datadog ties them all together in cohesive dashboards. 

The centralized pipeline tracking and error logging provide a comprehensive view of our development and deployment processes, making it much easier to identify and resolve issues quickly. 

Synthetic testing is great, allowing us to catch potential problems before they impact real users. Real user monitoring gives us invaluable insights into actual user experiences, helping us prioritize improvements where they matter most. And the ability to create custom dashboards has been incredibly useful, allowing us to visualize key metrics and KPIs in a way that makes sense for different teams and stakeholders. 

While the documentation is very good, there are areas that need a lot of focus to pick up on the key details. In some cases the screenshots don't match the text when updates are made. 

I spent longer than I should trying to figure out how to correlate logs to traces, mostly related to environmental variables.

I've used the solution for about three years.

We have been impressed with the uptime.

It's scalable and customizable. 

Support is helpful. They help us tune our committed costs and alert us when we start spending out of the on-demand budget.

We used a mix of SolarWinds, UptimeRobot, and GitHub actions. We switched to find one platform that could give deep app visibility.

Setup is generally simple. .NET Profiling of IIS and aligning logs to traces and profiles was a challenge.

We implemented the solution in-house.

There has been significant time saved by the development team in terms of assessing bugs and performance issues.

I'd advise others to set up live trials to asses cost scaling. Small decisions around how monitors are used can have big impacts on cost scaling. 

NewRelic was considered. LogicMonitor was chosen over Datadog for our network and campus server management use cases.

We are excited to dig further into the new offerings around LLM and continue to grow our footprint in Datadog. 

We utilize Datadog mainly to monitor our API integrations and all of the inventory that comes in from our API partners. Each event has its own ID, so we can trace all activity related to each event and troubleshoot where needed.

Datadog gives non-dev teams insights as to what all is happening with a particular event as well as flags any errors so that we can troubleshoot more efficiently.

The dashboards are super convenient to us for a more zoomed out view of what is going on with each integration that we utilize.

There could be more easily identifiable documentation on how to find different things on the platform. It can be overwhelming at first glance, and it's hard to find appropriate documentation on the site to lead you to where you need to be. 

I've used the solution for about 1.5 years.

Datadog is mainly used to set up alerts and thresholds to monitor real-time metrics and checks.

Integrating Datadog with other platforms has made our monitoring processes a bit easier. It's not super simple, but it's manageable.

For three to four months, we have been experiencing real-time delays. For example, if we're monitoring incoming traffic, the real-time status should be displayed up to a certain point. However, due to delays or issues with Datadog, the real-time data might only be updated at an earlier time. We are experiencing consistent delays in data updates from Datadog, with the most recent data often being delayed by about an hour. This issue has been ongoing for the past four months.

I have been using the product for a year. 

My company has 50 users for Datadog. 

The tool's deployment is difficult and time-consuming. 

The tool is open-source. 

If you're thinking about using Datadog for the first time, I suggest getting some basic training in data operations. It'll help you navigate Datadog more easily. 
Learning it for the first time is not overly difficult, but it's also not very easy.

I would rate the tool a seven out of ten. While it's a useful tool, we've experienced some issues that haven't been resolved yet. Additionally, setting up dashboards and utilizing all the features requires some training. 

Datadog has flexibility.

The product needs to have more enterprise approach to configuration.

We use the tool to monitor our whole infrastructure. CPU, memory, and disk space are the types of things we use it for.

It is a stable solution.

It is a scalable solution.

The technical support team is good and responsive.

Positive

The initial setup is not very easy and the deployment took eight months.It took quite a few teams to get it all accomplished. I rate it a six out of ten.

I rate the solution eight out of ten.

We primarily use the solution for centralized dashboarding and telemetry viewing for teams across the organization. 

We're focused on ensuring that both development teams and leadership can reasonably gain insights into the status of various systems. 

At the end of the day, managing various dashboards and metrics aggregators like Prometheus, Kubernetes server, AWS Cloudwatch, and Grafana have lead to some confusion, and we've had issues with teams not knowing where their data exists and where they can view their system metrics. 

Datadog has proven to be easy to set up and legible for both development and operational teams.

The solution has been useful in generally ensuring that teams are able to better visualize and think about their application's impact on data centers/cloud performance. Having centralized tooling for observability means that each team can be on the same page when discussing monitoring. 

There have been some issues where teams have been unable to find metrics within the tool properly and some behaviors with the tagging and grouping functionality that seem not to be as easy to understand as one may expect. That said, overall, the experience has been one that is positive.

The dashboards have proven most helpful in ensuring that teams can track the performance of their apps. On a more practical scale, the alerts have proved invaluable for triaging and bringing services back online.

Being able to tie the alerts generated through Datadog monitors has allowed us to quickly and effectively respond to infrastructure and software issues that would have otherwise hamstrung the organization and prevented us from accomplishing our day-to-day tasks. This is naturally invaluable.

I'm sure that this is said all the time, however, the pricing model has led us to restrict the usage of the service. If there were a more cost-effective manner of deploying the tool, we'd be more likely to adopt it more widely. 

Aside from the cost, the nature of the tagging and grouping features within the monitoring dashboards have often caused headaches when creating new dashboards for aggregate services and infrastructure stacks. It would be nice to ensure that this feature is supported long-term and brought with easier accessibility.

I've been using the solution for three years.

Datadog is easy to use and generally looks great from a customer standpoint. The ability to export metrics all into a central location was crucial.

Datadog is very expensive for smaller organizations. The pricing model might be restrictive until the organization reaches a certain size.

Primarily we did an evaluation of other providers, such as AWS and GCP, outside of in-house solutions.

We primarily use the solution for application monitoring (APM, logs, metrics, alerts).

It's useful for active monitoring (static monitors, threshold monitors). We get a lot of value out of anomaly detection as well. SLOs and monitoring of SLOs have been another value add.

In terms of metrics, the out-of-the-box infrastructure metrics that come with the Datadog agent installation are great. We have made use of both the custom metrics implementation as well as the log-based metrics which are extremely convenient.

We also leverage Datadog for use of RUM and want to explore session replay.

It is easy to implement and scale applications with standardized visibility, monitoring and alerting

We get a lot of value out of passive and active monitoring. While different teams across our organization have used different services (metrics, logs, APM, RUM), almost all teams have been able to use the dashboards to report and track high-level metrics and active monitoring. 

Active monitoring (static monitors, threshold monitors) is great. We get a lot of value out of anomaly detection as well. SLOs and monitoring of SLOs have been another value add for our organization.

The APM and tracing provide visibility and the ability to get right to root cause issues while being able to deploy new services without much need for custom instrumentation quickly

The active monitoring (static monitors, threshold monitors) has been very helpful. We get a lot of value out of anomaly detection. SLOs and monitoring of SLOs have been extremely valuable.

The metrics and out-of-the-box infrastructure metrics that come with the Datadog agent installation are quite helpful to the organization. We have made use of both the custom metric implementation as well as the log-based metrics which are extremely convenient.

Datadog is so feature-rich that it is often hard to onboard new folks and tough to decide where to invest time. 

The APM is a perfect example of this. This feature alone has so much (profiling, tracing, span summary, flame graphs). I would love to see more of the insight and automation-focused features, such as the log patterns, where I can spend time more efficiently.

The cost of Datadog at scale can get very expensive very quickly. I would like to see a better usage/cost dashboard with breakdowns like the AWS cost explorer.

I've used the solution for three years.