Datadog Reviews

We are using Datadog to improve our monitoring and observability so we can hopefully improve our customer experience and reliability.  

I have been using Datadog to build better actionable alerts to help teams across the enterprise. Also by using Datadog we are hoping to have improved observability into our apps and we are also taking advantage of this process to improve our tagging strategy so teams can hopefully troubleshoot incidents faster and a much reduced mean time to resolve. 

We have a lot of different resources we use like Kubernetes, App Gateway and Cosmos DB just to name a few.

As soon as we started implementing Datadog into our cloud environment people really like how it looked and how easy it was to navigate. We could see the most data in our Kubernetes environments than we ever could. 

Some people liked how the logs were color coded so it was easy to see what kind of log you were looking at. The ease of making dashboards has also been greatly received as a benefit. 

People have commented that there is so much information that it takes a time to digest and get used to what you are looking at and finding what you are looking for. 

The selection of monitors is a big feature I have been working with. Previously with Azure Monitor we couldn't do a whole lot with their alerts. The log alerts can sometimes take a while to ingest. Also, we couldn't do any math with the metrics we received from logs to make better alerts from logs.  

The metric alerts are ok but are still very limited. With Datadog, we can make a wide range of different monitors that we can tweak in real time because there is a graph of data as you are creating the alert which is very beneficial. The ease of making dashboards has saved a lot of people a lot of time. No KQL queries to put together the information you are looking for and the ability to pin any info you see into a dashboard is very convenient. 

RUM is another feature we are looking forward to using this upcoming tax season, as we will have a front-row view into what frustrates customers or where things go wrong in their process of using our site. 

The PagerDuty integration could be a little bit better. If there was a way to format the monitors to different incident management software that would be awesome. As of right now, it takes a lot of manipulating of PagerDuty to get the monitors from Datadog to populate all the fields we want in PagerDuty.  

I love the fact you can query data without using something like KQL. However, it would also be helpful if there was a way to convert a complex KQL query into Datadog to be able to retrieve the same data - especially for very specific scenarios that some app teams may want to look for.

I've used the solution for about two years.

We previously used Azure Monitor, App Insights, and Log Analytics.  We switched because it was a lot for developers and SREs to switch between three screens to try troubleshoot and when you add in the slow load times from Azure it can take a while to get things done.

I would advise taking a close look at logging costs, man-hours needed, and the amount of time it takes for people to get comfortable navigating Datadog because there is so much information that it can be overwhelming to narrow down what you need.

We did evaluate DynaTrace and looked into New Relic before settling on Datadog.

The product monitors multiple systems, from customer interactions on our web applications down to the database and all layers in between. RUM, APM, logging, and infrastructure monitoring are all surfaced into single dashboards.

We initially started with application logs and generated long-term business metrics out of critical logs. We have turned those metrics and logs into a collection of alerts integrated into our pager system. As we have evolved, we have also used APM and RUM data to trigger additional alerts. 

The solution has surfaced how integrated our applications really are and helps us track calls from the top down, identifying slowness and errors all through the call stack.

The biggest improvement we have seen is our time to discovery and resolution. As Datadog has improved, and we add new features, the depth and clarity we get from top to bottom has been excellent. Our engineering teams have quickly adopted many features within Datadog, and are quick to build out their own dashboards and alerts. This has also led to a rapid sprawl when left unchecked.

We started with application logs and have expanded over the years to include infrastructure, APM, and now RUM. All of these tools have been incredibly valuable in their own sphere. The huge value is tying all of the data points together.

Logging was the first tool we started with years ago, replacing our ELK stack. It was the easiest to get in place, and our engineers quickly embraced the tools. Several critical dashboards were created years ago and are still in use today. Over time, we have shifted from verbose logs and matured into APM and RUM. That has helped us focus on fine-tuning the performance of our applications.

We need better visibility into our consumption rate, which is tied to our commit levels. We would love to see a % consumed and alert us if we are over budget before getting an overage charge 20 days into the month.

The biggest complaint we hear comes from the cost of the tool. It is pretty easy to accidentally consume a lot of extra data. Unless you watch everything come in almost daily, you could be in for a big surprise. 

We utilize the Datadog estimated usage metrics to build out alerts and dashboards. The usage and cost system page still doesn't tie into our committed spending - it would be wonderful to see the monthly burn rate on any given day.

I've used the solution for six years.

There have not been as many outages in the past year. We also haven't been jumping into the new features as quickly as they come out. We may be working on more stable products.

It has scaled up to meet our needs pretty well. Over the years, we have only managed to trigger internal DataDog alerts once or twice by misconfiguring a metric and spiralling out of control with costs.

Support has been lacking. Opening a chat with the tech support rep of the day is always a gamble. We are looking into working with third-party support because it has been so rough over the years.

Neutral

We used the ELK stack for logging and monitoring and AppDynamics for APM.

The initial setup for new teams has become easier over the years. We are increasing our adoption rate as we shift our technology to more cloud-native tools. Datadog has supported easy implementation by simply adding a package to the app. 

They have really focused on a lot of out-of-the-box functionality, but the real fun happens as you dive deeper into the configuration. We have also begun adapting open telemetry standards. This has kept us from going too deep into vendor-specific implementations.

We did the initial setup via an in-house team.

As long as we stay on top of our consumption mid-month, it has been worth it. However, the few engineers we have who are dedicated to playing whack-a-mole with the growing spending could be better utilized in teaching best practices to new users. I suppose our implementation of the rapidly changing tools over the years has led to a fair amount of technical debt.

It is quite easy to set up any specific tool, but to take advantage of the full visibility it offers, you need to instrument across the board—which can be time-consuming. Be careful about how each tool is billed, and watch your consumption like a hawk.

We evaluated AppDynamics and Dynatrace.

It's a very powerful tool, with lots of new features coming, but you certainly will pay for what you get.

We use the solution for APM, AWS, Lambda, logging, and infrastructure. We have many different things all over AWS, and having one place to look is great.

We have all sorts of different AWS things out there that are in C# and Node. Having a single place to log and APM into is very important to us.

Keeping track of the cloud infrastructure is also important. We have Lambda, containers, EC2, etc.

Having a super simple interface to filter the searching for APM and logging is great. It is super easy to show people how to use. This is super important to us.

Finding issues quickly is super important. Being able to create dashboards and alert on issues.

Having the ability to create dashboards has really taught us how to utilize the searching part of the system. We are able to share them, and build upon them so easily. Many iterations later people are putting some solid information out there.

Alerting is also important to us. We have set up many alerts that help us spot issues in the platform before they become bigger issues. This has enabled my teams to use incidents and address the issues so they are no longer problems.

Alerting on running systems is very helpful. Finding issues is quick. We have one place for logging, searching through. Being able to save these and reference them in the future and build upon them.

The logging in general is one of my favorite features. The search is so straight forward and easy to use. Just being able to click on a field and add it to search has taught me so much about the interface, It might not be as useful without a shortcut like that to teach me the system. We have Cloudflare logs in there, and I have no idea sometimes how to filter on such a buried piece of JSON. That is where the interface helps me by clicking on the add to search I get what I need.

The "Pager Duty" replacement is something we are very interested in. We only really use pager duty to call the team when things are down.

I love to have some DD guru come in and do a department training directly at our setup. We would love to have someone come in and show us the things we could do better within our current setup.

Also saving a bit of cash would also help if there are things we are doing that are costing us. It's a big enough tool that it is tough to have someone dedicated to manage. 

I've used the solution for a bit over a year at this point.

The stability seems good here too.

Scalability seems good to me. I have no complaints

I get answers from our contact, and one team member did reach out. It went well.

Positive

We used Loggly. 

We switched because we wanted an all-in-one tool

Some parts of our setup were tough. Some Windows container setups cost us a lot of time.

The AWS infrastructure was tough to fully turn on due to the large cost of everything being run.

We handled the setup ourselves in-house.

This cost us more overall. ROI is hard to sell. That said, I can find issues way faster and see what is going on in my entire platform. I pay back the cost every month with productivity. 

It is going to cost you more than you think to keep everything running. We saw value in the one-for-all solution, however, it came at a premium to what we were paying. 

We did evaluate Dynatrace.

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

We run a mix of AWS EC2, Azure serverless, and colocated VMWare servers to support higher education web applications. 

Managing a hybrid multi-cloud solution across hundreds of applications is always a challenge. 

Datadog agents on each web host and native integrations with GitHub, AWS, and Azure get all of our instrumentation and error data in one place for easy analysis and monitoring.

Using 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. 

Whether the app is vendor-supplied or we built it ourselves, the depth of tracing, profiling, and hooking into logs is all obtainable and tunable. Both legacy .NET Framework and Windows Event Viewer and cutting-edge .NET Core with streaming logs all work. 

The breadth of coverage for any app type or situation is really incredible. It feels like there's nothing we can't monitor.

When it comes to Datadog, several features have proven particularly valuable. For example, 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 has been a game-changer, 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. 

Together, these features form a powerful toolkit that helps us maintain high performance and reliability across our applications and infrastructure, ultimately leading to better user satisfaction and more efficient operations.

They need an expansion of the Android and IOS apps to provide a simplified CI/CD pipeline history view. 

I like the idea of monitoring on the go. That said, it seems the options are still a bit limited out of the box. 

While the documentation is very good considering all the frameworks and technology Datadog covers, there are areas - specifically .NET Profiling and Tracing of IIS hosted apps - that need a lot of focus to pick up on the key details needed. 

In some cases the screenshots don't match the text as updates are made. I spent longer than I should figuring 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 and clean and light resource usage of the agents.

The solution has been very scalable and very customizable.

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

We used a mix of a custom error email system, SolarWinds, UptimeRobot, and GitHub actions. We switched to find one platform that could give deep app visibility regardless of Linux or Windows or Container, cloud or on-prem hosted.

The implementation is generally simple. That said, .NET Profiling of IIS and aligning logs to traces and profiles was a challenge.

The solution was implemented in-house. 

Our ROI has been significant time saved by the development team assessing bugs and performance issues.

Set up live trials to asses cost scaling. Small decisions around how monitors are used can impact cost scaling. 

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

We are excited to explore the new offerings around LLM further and continue to expand our presence in Datadog. 

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

We run a mix of AWS EC2, Azure serverless, and colocated VMWare servers to support higher education web applications. 

Managing a hybrid multi-cloud solution across hundreds of applications is always a challenge. Datadog agents on each web host and native integrations with GitHub, AWS, and Azure get all of our instrumentation and error data in one place for easy analysis and monitoring.

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. Whether the app is vendor-supplied or we built it ourselves, the depth of tracing, profiling, and hooking into logs is all obtainable and tunable. Both legacy .NET Framework and Windows Event Viewer and cutting-edge .NET Core with streaming logs all work. The breadth of coverage for any app type or situation is really incredible. It feels like there's nothing we can't monitor.

When it comes to Datadog, several features have proven particularly valuable. 

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 has been a game-changer, 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. 

Together, these features form a powerful toolkit that helps us maintain high performance and reliability across our applications and infrastructure, ultimately leading to better user satisfaction and more efficient operations.

I'd like to see an expansion of the Android and IOS apps to have a simplified CI/CD pipeline history view. I like the idea of monitoring on the go, however, it seems the options are still a bit limited out of the box. 

While the documentation is very good considering all the frameworks and technology Datadog covers, there are areas - specifically .NET Profiling and Tracing of IIS-hosted apps - that need a lot of focus to pick up on the key details needed. In some cases the screenshots don't match the text as updates are made. I feel I spent longer than I should figuring 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 and clean and light resource usage of the agents.

The solution was very scalable and very customizable.

Sales service is always helpful in tuning our committed costs and alerting us when we start spending outside the on-demand budget.

We used a mix of a custom error email system, SolarWinds, UptimeRobot, and GitHub actions. We switched to find one platform that could give deep app visibility regardless of Linux, Windows, Container, cloud or on-prem hosted.

The setup is generally simple. That said, .NET Profiling of IIS and aligning logs to traces and profiles was a challenge.

The solution was iImplemented in-house. 

I'd count our ROI as significant time saved by the development team assessing bugs and performance issues.

It's a good idea 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 have a tech stack including all backend services written in TS/Node (mostly) and as a full stack engineer, it is crucial to keep track of new and existing errors. Our logs have been consolidated in Datadog and are accessible for search and review, so the service has become a daily tool for my work. 

More recently, session replay has been adopted at my company, but I do not like it so much because the UI elements are not in their place, so it is very hard to see what the users on the web app are actually clicking on.

The best way it has helped us is by consolidating all our logs into a single place and making it easier to find errors. Previously using AWS Cloudwatch was cumbersome and time-consuming. One issue I do have with logs is the length of time they are on the platform. Some issues happen sporadically, so it would be good to have logs for longer than one month by default or make it a configuration. 

Another issue that I have is with the search syntax, it could be simpler and it feels like there are too many ways to do the same things.

Logs search is the most valuable feature because it has consolidated all of our backend services logs into one place. Now we can see the relationship between them as requests are going from one service to other dependencies. 

One issue I do have with logs is the length of time they are on the platform. Some issues happen sporadically, so it would be good to have logs for longer than one month by default or make it a configuration. I have yet to try rehydrating logs, so this might be an option I need to try. Another issue I have is with the search syntax, it could be simpler. The syntax is a bit cumbersome and there is not an intuitive to save them to look for similar searches in the future. 

Finally, while my company replaced a different tool for session replay with DataDog's version, I find it clunky and in need of further improvements. For example, when troubleshooting a web portal issue, it is super important to know what the user clicked, but the elements are not where they should be in the replay.

It is also hard to find details about the sessions, and metadata such as user email, account, etc. that exist on other services with replay features.

I have been using Datadof for approximately five years.

So far we haven't had any issues with uptime and Datadog has been available when needed.

It seems to scale well as we continue to add services that need monitoring.

I haven't had to contact support.

Cloudwatch was not a great tool for what we need to do to troubleshoot issues.

We deployed it in-house with intermediate expertise.

I am not sure how much we are paying, but I use the app often enough to feel like we are getting a good ROI.

I was not involved in the choosing process as a software engineer

The technology itself is generally very useful and the interface it great.

There should be a clearer view of the expenses.

I have used the solution for four years.

The solution is stable.

I have not personally interacted with customer service. I am satisfied with tech support.

Neutral

I am using ThousandEyes and Datadog. Datadog supports AI-driven data analysis, with some AI elements to analyze, like data processing tools and so on. AI helps in Datadog primarily for resolving application issues.

It was not difficult to set up for me. There was no problem.

I can confirm there is a return on investment.

I find the setup cost to be too expensive. The setup cost for Datadog is more than $100. I am evaluating the usage of this solution, however, it is too expensive.

I would rate this solution eight out of ten. 

We were looking for an all-in-one observability platform that could handle a number of different environments and products. At a basic level, we have a variety of on-premises servers (Windows/Mac/Linux) as well as a number of commercial, cloud-hosted products. 

While it's often possible to let each team rely on its own means for monitoring, we wanted something that the entire company could rally around - a unified platform that is developed and supported by the very same people, not others just slapping their name on some open source products they have no control over.

Datadog has effortlessly dropped in to nearly every stage of observability for us. We appreciate how it has robust cross-platform support for our IT assets, and for integrating hosted products, enabling integrations often couldn't be easier, with many of them including native dashboards and even other types of content packs. 

Over the last couple of years, we have onboarded a number of engineering teams, and each of them feels comfortable using Datadog. This gives us the ability to build organizational knowledge.

Datadog's learning platform is second to none. It's the gold standard of training resources in my mind; not only are these self-paced courses available at no charge, but you can spin up an actual Datadog environment to try out its various features. 

I just hate when other vendors try to upsell you on training beyond their (often poorly-written) documentation. Apart from that, we appreciate the variety of content that comes from Datadog's built-in integrations - for common sources, we don't have to worry about parsing, creating dashboards, or otherwise reinventing the wheel.

Datadog's roadmap can be a bit unpredictable at times. For instance, a few years ago, our rep at the time stated that Datadog had dropped its plans to develop an incident on-call platform. However, this year, they released a platform that does exactly that.

They also decided to drop chat-based support just recently. While I understand that it's often easier to work with support tickets, I do miss the easy availability of live support. 

It would be nice if Datadog continued to broaden its variety of available integrations to include even more commercial platforms because that is central to its appeal. If we're looking at a new product and there isn't a native integration, then that's more work on our part.