Datadog Reviews

We deploy agents on-premise to collect data on on-premise VM instances. We don't use Datadog in our cloud network. We do have some Cloud apps that we have it on and we also have Containers. We have it on their headquarters, the main software for them is on their own Cloud.

Eventually, we're building out the process now and using it better. We plan to use Datadog for root cause analysis relating to any kinds of issues we have with software, with applications going down, latency issues, connection issues, etc. Eventually, we're going to use Datadog for application performance, monitoring, and management. To be proactive around thresholds, alerts, bottlenecks, etc. 

Our developers and QA teams use this solution. They use it to analyze network traffic, load, CPU load, CPU usage, and then Tracey NPM, API calls for their application. There are roughly 100 users right now. Maybe there's 200 total, but on a given day, maybe 13 people using this solution.

It hasn't improved the way our organization functions yet, because there's a lot of red tape to cut through with cultural challenges and changes. I don't think it's changed the way we do things yet, but I think it will — absolutely it will. It's just going to take some time.

I like that you can build out a dashboard pretty quickly. There are some things that come out of the box that you don't really need to do, which is great because they're default settings. Once you install the agent on the machine, they pick up a lot of metrics for you that are going to be 70 or more percent of what you need. Out of the box, it's pretty good.

I have been using Datadog every day since September 2020. I also used it at a previous company that I worked for.

Stability-wise, it's great.

It seems like it'll scale well. We're automating it with Ansible scripts and service now so that when we build a new virtual machine it will automatically install Datadog on that box.

The tool itself is pretty good and the customer service is good, but I think they're a growing company. I think better access to their engineers when we have a problem could be better. For example, if I asked the question, "Hey, how do I install it on this type of component?" We'll try to get an engineer on the phone with us to step us through everything, but that's a challenge because they're so busy.

Technically-wise, everything's fine. We don't need any support, everything that I need to do, I can do right out of the box. But as far as, in the knowledge of their engineers on how to configure it on given systems that we have, that's maybe at six because they're just not as available as I would've hoped.

We were using AppDynamics. Technically, we still have it in-house because it's tightly wound into certain systems, but we'll probably pull that off slowly over time. The reason we added Datadog and eventually we'll fully switch over is due to cost. It's more cost-friendly to do it with Datadog.

Yes, we looked at Dynatrace, AppDynamics, and New Relic. Personally, I wouldn't have chosen Datadog for the POC if it were up to me. Datadog was a leader, but New Relic was looking really good. In the end, the people above me decided to go with Datadog — it's a big company, so they wanted to move fast, which makes sense.

If you're interested in using Datadog, just do your homework, as we did. We're happy so far I think; time will tell as we are still rolling things out. It's a very good company. It's going to be a year before we really can tell anything. If you do your homework, you'll find that if you're really concerned with cost, it's good.

There are some strengths that AppDynamics and Dynatrace have that Datadog I don't think will have down the road, but they're not things we necessarily need — they're outliers. It would be nice to have them, but we can manage without them.

Know what you want. There is no need to pay for solutions like Dynatrace or AppDynamics that are more expensive or things that are just nice to have if you don't absolutely need to have them. That's something people need to understand. You just have to make sure you understand what it is that you need out of the tool — they are all a little different, those three. I would say to anybody that's going with Datadog: you just have to be patient at the beginning. It's a very busy company right now. They're very hot in the market.

Overall, on a scale from one to ten, I would give Datadog a rating of eight. It does what we need it to do, and it seems to be pretty user-friendly in terms of setting things up.

Features-wise, I'd give them a rating of ten out of ten. The better access we get to assistance from the engineers on how to configure dashboards and pulling metrics that we need, that would bring it up a little bit. So overall it would be harder and it would have to be perfect for it. I would say maybe they could bring it to a nine.

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. 

We're managing a hybrid multi-cloud solution across hundreds of applications, which is always a challenge. There are Datadog agents on each web host, and native integrations with GitHub, AWS, and Azure and that gets 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. 

Sometimes, the screenshots don't match the text as updates are made. I spent longer than I should have figured 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 product is very scalable and very customizable.

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

We implemented the solution in-house. 

ROI is reflected in in 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're excited to explore the new offerings around LLM further and continue to expand our presence in Datadog. 

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.

The most valuable features of Datadog are the flexibility and additional features when compared to other solutions, such as AppDynamics and Dynatrace. Some of the features include AI and ML capabilities and cloud and analysis monitoring 

Datadog could improve the flexibility with AI and ML concepts. This will allow customers to be more leveraged towards publishing.

I have been using Datadog for approximately one year.

Datadog is stable. We did not have a single outage.

I have found Datadog to be scalable.

We have approximately 2,000 users using the solution in my organization.

The support from Datadog is excellent.

I have previously used AppDynamics and Dynatrace. 

Datadog's initial setup is easy because they have helped us come up with the easiest way of instrumenting any of the features which need to be deployed.  We worked on it with their engineers and we were able to happily do it. We have done approximately 60 application monitoring through Datadog since our deployment.

We have a very tiny team of four members that do the maintenance of Datadog.

The price of Datadog is reasonable. Other solutions are more expensive, such as AppDynamics.

Datadog is far better than any other monitoring tool in introducing any of the new capabilities because they think before Amazon AWS and Microsoft Azure before they introduce the concepts. Datadog is a good tool to have for monitoring your own infrastructure.

I rate Datadog a ten out of ten. 

We use the product for instrumentation, observability, monitoring, and alerting of our system. 

We have multiple environments and a variety of pieces of infrastructure including servers, databases, load balancers, cache, etc. and we need to be able to monitor all of these pieces, while also retaining visibility into how the various pieces interact with each other. 

Tracing data between components and user interactions that trigger these data flows is particularly important for understanding where problems arise and how to resolve them quickly.

It provides a lot of options for integrations and tooling to observe what is happening within the system, making diagnosis and triage easier/faster. 

Each user can set up their own dashboards and share them with other users on the team. We can instrument monitors based on various patterns that we care about, then notify us when an event triggers an alert with platforms such as Slack or PagerDuty. 

Our ability to rapidly become aware of problems focused on the symptoms being observed and entry points into the tool to rapidly identify where to investigate further is important for our team and our users.

The most valuable aspects of the solution include log search to help triage specific problems that we get notified about (whether by alerts we have configured or users that have contacted us), APM traces (to view how user interactions trace through the various layers of our infrastructure and services to be able to reproduce and identify the source of problems), general performance/system dashboards (to regularly monitor for stability or deviation), and alerting (to be automatically informed when a problem occurs). We also use the incident tools for tracking production incidents.

In some ways, the tool has a pretty steep learning curve. Discovering the various capabilities available, then learning how to utilize them for particular use cases can be challenging. Thankfully, there is a good amount of documentation with some good examples (more are always welcome), and support is very helpful. 

While DataDog has started adding more correlation mapping between services and parts of our system, it is still tricky to understand what is the ultimate root cause when multiple views/components spike. Additionally, there are lots of views and insights that are available but hard to find or discover. Some of the best ways to discover is to just click around a lot and get familiar with views that are useful, but that takes time and isn't ideal when in the middle of fighting a fire.

I've used the solution for about four years.

It seems stable.

It seems to scale well. Performance for aggregating or searching is usually very fast.

Technical support is helpful and pretty responsive.

Positive

We did not use a different solution. 

It's hard to say what ROI would be as I have not managed our system without it to compare to.

I don't manage licensing.

We did not evaluate other options. 

It's a great tool with new features and improvements continuously being added. It is not simple to use or set up, however, if you have the right personnel, you can get a lot of value from what DataDog has to offer.

We use the solution for monitoring microservices in a complex AWS-based cloud service.  

The system is comprised of about a dozen services. This involves processing real-time data from tens of thousands of internet connected devices that are providing telemetry. Thousands of user interactions are processed along with real-time reporting of device date over transaction intervals that can last for hours or even days. The need to view and filter data over periods of several months is not uncommon.  

Datadog is used for daily monitoring and R&D research as well as during incident response.

The query filtering and improved search abilities offered by Datadog are by far superior to other solutions we were using, such as AWS CloudWatch. We find that we can simply get at the data we need quicker and easier than before. This has made responding to incidents or investigating issues a much more productive endeavour. We simply have less roadblocks in the way when we need to "get at the data". It is also used occasionally to extract data while researching requirements for new features.

Datadog dashboards are used to provide a holistic view of the system across many services. Customizable views as well as the ability to "dive in" when we see someting anomalous has improved the workflow for handling incidents.    

Log filtering, pattern detection and grouping, and extracting values from logs for plotting on graphs all help to improve our ability to visualize what is going on in the system. The custom facets allow us to tailor the solution to fit our specific needs.

There are some areas on log filtering screens where the user interface can take some getting used to. Perhaps having the option for a simple vs advanced user interface would be helpful in making new or less experienced users comfortable with making their own custom queries.

Maybe it is just how our system is configured, yet finding the valid values for a key/value pair is not always intuitively obvious to me. While there is a pop-up window with historical or previously used values and saved views from previous query runs, I don't see a simple list or enumeration of the set of valid values for keys that have such a restriction.

I've used the solution for one year.

The solution is very stable.

The product is reasonably scalable, although costs can get out of hand if you aren't careful.

I have not had the need to contact support.

Neutral

We did use AWS CloudWatch. It was to awkward to use effectively and simply didn't have the features.

We had someone experienced do the initial setup.  However, with a little training, it wasn't too bad for the rest of us.

We handled the setup in-house.

Take care of how you extract custom values from logs. You can do things without thought to make your life easier and not realize how expensive it can be from where you started.

I'm not aware of evaluating other solutions.

Overall I recommend the solution. Just be mindful of costs.

From day one, we have seamlessly integrated our new product into Datadog, a comprehensive monitoring and analytics platform. By doing so, we are continuously collecting essential data such as host information, system logs, and key performance metrics. This enables us to gain deep insights into product adoption, monitor usage patterns, and ensure optimal performance. Additionally, we use Datadog to capture and analyze errors in real-time, allowing us to troubleshoot, replay, and resolve production issues efficiently.

It has proven invaluable in helping us identify early issues within the product as soon as they occur, allowing us to take immediate action before they escalate into more significant problems. This proactive approach ensures that potential challenges are addressed in real-time, minimizing any impact on users. Furthermore, the system allows us to measure product adoption and usage trends effectively, providing insights into how customers are interacting with the product and identifying areas for improvement or enhancement.

There isn't any single aspect that stands out in particular; rather, everything is interconnected and works together harmoniously. Each component complements the other, creating a cohesive system where data, logs, and metrics are seamlessly integrated. This interconnectedness ensures that no part operates in isolation, allowing for a more holistic view of the product's performance and health. The way everything binds together strengthens our ability to monitor, analyze, and improve the product efficiently.

At the moment, nothing specific comes to mind. Everything seems to be functioning well, and there are no immediate concerns or issues that I can think of. 

The system is operating as expected, and any challenges we've faced so far have been successfully addressed. If anything does come up in the future, we will continue to monitor and assess it accordingly, but right now, there’s nothing that stands out requiring attention or improvement. 

Datadog is too pricey when compared to its competitors, and this is something that its always on my mind during the decision-making process.

I've used the solution for nearly two years now.

I used Datadog typically for monitoring website statistics and some of the cloud networking equipment.

The most valuable features are the dashboards and the reporting.

I have been using this solution for approximately three years.

I found the solution to be stable, I did not experience any bugs or glitches. However, some of the managing team did.

The scalability of the solution was good. Being a cloud solution, if there was an issue with the scalability it would be easily fixed with an update.

We have approximately 200 users using the solution in my organization.

I did not need to use the support.

I was previously using SolarWinds in the company I was working with before.

I rate Datadog nine out of ten.