Datadog Reviews

The solution is basically used for servers and applications.

The UI, basically, is the most valuable aspect of the solution. I really like the look and feel of the solution. It's not very distinctive now since other players have caught up, however, they were the first in the market to present such an effective UI. 

The many dozens of integrations that the solution brings out of the box are excellent.

It's easy to set up.

Deploying the agents is still very manual. 

Network monitoring could be better or rolled into this solution so that you do not have to buy a different product.

Customization of the tool itself should be taken into account. At the moment, although what they provide out of the box is good, they don't offer many customization possibilities. I know it's difficult, however, it's something that they would need to look at. When the customer gets some customization, they want customized requirements. We cannot do it. 

I've been dealing with the solution for five years. 

It's quite stable. I have never had an issue in regard to reliability, so it's very stable.

It's very scalable. I have not reached the limits at any time, never in the solution. I've never seen any performance degradation in large environments. I would say it's very scalable.

Each client has its own instance. We do not share instances with multiple customers. There's usually between 20 and 30, depending on the customer.

I never use technical support, to be honest.

The initial setup for the solution itself is quite straightforward. You just set it up and that's it. However, when it comes to, for instance, deploying the agents to the servers, or at least the target machines, it's still a manual task. They still do not have centralized management of the FD agents, which basically delays the deployment of the solution. It's very manual still.

How long it takes to deploy is difficult to pin down. It will vary based on the environment size. Obviously, if it's ten servers, it will basically take half an hour or one hour. If it's 5,000, obviously, besides the number of notes, other considerations will need to be taken into account. If t's a large environment, it will take much longer. We would need to basically develop a solution, or an effective process to deploy the agent and configure them in a standardized manner. This is something that the tool itself or the tool provider does not offer out of the box. You need to build it. That's a drawback.

How many people you need for the deployment and maintenance processes depends on the environment's size and geographical area. On average,  I would usually require for every 500 notes, one resource for implementation. Then for overall support, I usually put one resource per 1500.

Before, the ROI was much higher as you would not have to compete with any kind of tool since they were very good in the space. However, with time, other companies have picked up the slack. Now, you have other tools which provide a higher ROI. I cannot give a specific ROI percentage since I don't use it for personal use with deployment. We deploy it on behalf of customers. Obviously, depending on the deal, depending on the size, and the ROI will vary. If people are looking for a global monitoring solution in the same tool as Datadog network monitoring, they are always hindered as Datadog does not provide an adequate solution for it. That kind of decreases the ROI since you still need to get another tool to do the network monitoring.

The licensing is a bit complicated. When you pay for it on a note basis, that's perfectly fine. However, when you put log analytics on top of it, it's based on traffic. This is actually an issue. It gets complicated.

I'm providing Datadog. I'm a retailer.

I would recommend the solution. 

I would suggest if their environment is in the cloud, companies have their environments in the public cloud, such as GCP, Azure, or AWS. Datadog is a very good candidate to provide an overview of the monitoring. If you want to consider a hybrid solution where systems and servers and applications also provide a good solution and have a lot of APM capabilities, the only drawback will be network monitoring. When you grab a tool that you want to basically monitor the entire environment at a single point of contact, with Datadog, it's possible, however, there's not an effective tool to do network monitoring.

I'd rate the solution seven out of ten.

Our primary use case is log management and we also use the solution for monitoring the application and underlying infrastructure. I'm an IT test manager. 

I appreciate that they are constantly adding new features, some of which we haven't yet had a chance to implement. 

I'd like to see more flexibility in the customization and they have a few settings which need to be changed but we are unable to make those changes as users or as the administrator. The tagging to get the different parts of the monitoring interconnected is a bit tricky and takes time to work out. 

I've been using this solution for 18 months. 

The stability is good. 

I would say that the amount that we are monitoring is not that large and we've never had any scalability issues. We have around 50 users in our department. 

The availability or accessibility to customer service is not always good, although they generally provide solutions once you do manage to get hold of them. 

We have previously used different tools for different parts of the monitoring. We changed to AWS when we moved to the cloud. We also found that the effort in maintaining Grafana and Prometheus and keeping it up to date was taking too much time.

The initial setup was straightforward, we used a service provider and they also maintain our operation in general.

We have a four-year contract with Datadog, and the solution is pay-as-you-use. 

I would suggest using the documentation, which is quite good. It's best to start with existing integrations, and then do the customization step-by-step.

I rate this solution eight out of 10. 

My customers were using Datadog for monitoring purposes. They were using it only because the solution is running on AWS and it's a microservices-based solution. They were using an application called Dynatrace for their log.

Most of the features in the way Datadog does monitoring are commendable and that is the reason we choose it. We did some comparisons before picking Datadog. Datadog was recommended based on the features provided.

Most of the monitoring tools nowadays are have or are going to have embedded artificial intelligence and machine learning to make monitoring and logging more proactive and intelligent. Datadog has incorporated some artificial intelligence.

The solution does not require a lot of maintenance.

The solution had all the features we were looking for and we were able to create a central dashboard as per our requirements.

All solutions have some area to improve, and in Datadog they can improve their overall technology moving forward.

I have been using Datadog for approximately four months.

Datadog is a stable solution.

Datadog is a highly scalable solution because it is a SaaS solution. Having this solution be a SaaS is one of its most appealing attributes. When the vendor is going to manage data scaling and everything for you, you are only going to use the solution as per your requirements. Autoscaling is a great feature that they have.

The support from Datadog is exellent. If you're stuck on something or you are facing any issue, support from the vendor itself is available. You will receive a response instantly from the vendor on anything related to the requirement,  issues, or feature you are looking for. The responses have always been in a timely manner.

I rate the technical support from Datadog a five out of five.

I have used other similar solutions to Datadog and when I do a comparison between the other tools Datadog is on top, it is great.

Since Datadog is a SaaS solution we had not deployed the Datadog on-premise or in any Cloud. We were using the SaaS solution from the vendor itself. From the provisioning perspective or from the monitoring and dashboard perspective, we were using Terraform to create the typical monitoring as code. Everything was basically automated, we were not doing anything manually.

If someone wants to set up Datadog on-premise or in any of the Cloud machines, they have to consider a lot of things from the auto-scaling perspective.

My recommendation is Datadog is very good. Your team can mainly focus on the development rather than the solution itself. The vendor is going to take care of auto-scaling and maintenance and everything for you.

I rate Datadog a nine out of ten.

We are evaluating Datadog for observability and monitoring requirements that we have in our company. In our use case, our intention is to provide some kind of framework for multiple app teams to use the tool for our cyber ability and engineering practices.

They have a very good foundation in capturing metrics, logs, and traces. It's a very nice tool for that and it allows you to apply these monitoring tools in almost any technology.

Even if you have several layers, containers, EC2 instances, build machines or whatever you need in your infrastructure, Datadog can integrate with all of them across multiple cloud providers. It's a great product.

One of the improvement opportunities that we have identified in my project concerns how hard it is to manage an organizational structure when you have multiple things in one organization, and you want to provide some kind of isolation between them. At the same time, from the management perspective, you want to see an overall overview of what is happening in your business unit, or as a whole division. This is the kind of limitation we're facing.

I'm not sure what kind of features are in the roadmap right now, but I encourage the development of features for defining your organization, and allowing the visibility of what kind of metrics you can get. Those features would be really useful for us. 

I have been using Datadog for about six months.

It's a very scalable product. Right now we are using the SaaS version, so we don't need to worry about the infrastructure or whatever is needed for the platform it is running on. All the capturing of data is sent to the SaaS product and that can be as scaled as needed.

So far their support is pretty nice. They have established many meetings and training sessions, and they are supporting our requirements very well. I don't have any complaints with Datadog support.

While it is an expensive product, I would rate the pricing level at four out of five. 

Normally, the primary reason why people use these kind of tools is observability, but right from the beginning you have to understand what observability is, what it means for your company, and how the tool is going to help you to capture the proper metrics for making your applications observable.

We have used this solution primarily for application performance monitoring. To do this, we needed to make sure we had the right data in the system so that people could be able to monitor their applications end-to-end.

The infrastructure monitoring capabilities are really valuable. You can just log on and see everything that is happening within an IT environment.

Their security features could be improved. We looked at their Security Monitoring feature but it was early in its development. Datadog are just getting into the security space so I'm sure this will improve in the future.

This is a stable solution. 

This is a scalable solution. 

To get started with this solution, I would recommend front-loading it with some sort of data or process and filter out to view only the information you need.

I would rate this solution a nine out of ten. 

We implement these solutions for our clients. We have implemented Datadog as an SIEM solution.

Because of our client focus, it is easy for us to sell. This is because it is easy to use and easy to set up.

While I like the ease of use, when compared with Tenable Nessus they could still improve their usability. They are okay, but there is room to be better.

They could have more integration.

They could be more intuitive as well. For example, the intuitivity of the user interfaces, and how long it takes for users to learn how to use Datadog.

It is not impossible to use, or impossible to do the administration with it but when you put these two next to each other, meaning Nessus and Datadog, Nessus comes out as the winner.

I have been using Datadog for two years.

We are not using the latest version, we have missed at least one update.

We have no issues with the stability of Datadog.

Datadog is a scalable product.

We have two customers who are using this solution.

Technical support runs 24/7. The technical support is absolutely fine.

We are also using Nessus. My experience using Tenable Nessus is better.

It is easier to install than to use it.

I was not the one doing the handling the installation. I'm a senior consultant, and I was coordinating, planning, and interacting with clients. But the actual installation, I was not involved with. 

The installation could be done in an hour or so.

It is not complex, two professionals are enough to complete the installation and maintenance of Datadog.

With Datadog, it's a monthly fee. They prefer monthly subscriptions.

I would recommend this solution for medium enterprises with 100 to 1,000 employees. 

Small business is too small for the way that Datadog operates. It is not the best for very large enterprises for a company with more than 1,000 employees.

I would rate DataDog an eight out of ten.

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.

We primarily use Datadog for logs, APM, infrastructure monitoring, and lambda visibility.

We have built a number of critical dashboards that we display within our office for engineers to have a good understanding of the application performance, as well as business partners to understand at a high level the traffic flowing through the app.

We started with logging, as our primary monitor, and have shifted to APM to get a deeper understanding of what our system is doing, and how the changes we are making impact the apps.

Datadog has given us near-live visibility across our entire cloud platform. We are finally in a state where we are alerting our users about degraded performance well before the helpdesk tickets start rolling in.

We are making major architectural decisions based on the data we are getting from Datadog. It also gives us an idea of where the complexity really lies in some older, monolithic apps. 

We have used the APM endpoint monitoring to prioritize work on slower endpoints because we can see the total count, as well as the latency. That has been a big driver in our refactor work prioritization.

We have struggled to get more business-centric measures in our code to surface actual business values in our reports, but that is our next initiative.

We started with Log analytics in the beginning stages of our monitoring journey. Those were very insightful, but obviously only as useful as we made them with good logging practices.

The dashboards we created are core indicators of the health of our system, and it is one of the most reliable sources we have turned to, especially as we have seen APM metrics impacted several times lately. We can usually rely on logs to tell us what the apps are doing.

APM and Traces have been crucial to understanding how users are actually using the app. That drives a lot of our decisions around refactoring and focusing our limited engineering resources.

Continued improvement around cost and pricing model is needed. It is pretty complex and takes a fair amount of intimate knowledge to know exactly how turning on a single function is going to impact your bill, especially when you don't see the metrics for a day or two. 

We have recently had a number of issues with stability and delays on logging, monitoring, metric evaluation, and alerts. More often than not in the past month, it seems that we get the banner across the to of our dashboards that some service is impacted. They don't always show up on the incident page, either.

We have been using Datadog for two years.

Overall, it has been fairly stable for us. There are the occasional issues with importing data, that has usually been resolved in a short time. We have never had an issue where that data was lost, just delayed, and eventually backfilled. 

It seems (anecdotally, of course) that there have been a few more stability issues lately. We have noticed several days that we are getting in-app alert banners indicating that some metric or log ingestion was delayed, or the web app itself was experiencing severe slowness. 

Overall, these issues are resolved rather quickly - kudos to their engineering teams. I hear that they actually use Datadog to monitor Datadog. 

Datadog is very scalable but just watch the cost.

Technical support is hit and miss; there are a number of nuances to how this tool should be implemented, and it is difficult to re-explain how our infrastructure and applications are set up every time we need an in-depth investigation to understand what is broken.

Previously, we used AppDynamics. The pricing model didn't seem to fit with actual cloud spend. Now we may have swung the pendulum a little too far, and seem to be dealing with pricing on every facet of the application. 

The initial setup was pretty straightforward. Additional tweaks and configuration have been a bit more difficult as we get deeper and deeper into the guts of the integrations. Making sure we are keeping up with a rapid release schedule, and keeping our server clients in sync with our app packages has been troublesome. There have been some major changes in the APM that have introduced a number of bugs and broken some of our dashboards and alerts.

Our in-house team handled the deployment, with a lot of tickets created for the Datadog team.

ROI is difficult to measure completely. Our first year spend compared to our second and now going into the third year spend have been significantly different.

My advice is to really keep an eye on your overage costs, as they can spiral really fast. We turned on some additional span measures and didn't realize until it was too late that it had generated a ton.

Frankly, we love the visibility it gives us into our applications, but it is a bit cumbersome to ensure we are paying for the right stuff. Overall, the cost is worth it, as it helps us keep system-critical applications up and running, and reduces our detection and correction times significantly.

We evaluated Dynatrace and AppD before choosing this product.

Datadog requires pretty close supervision on the usage page to ensure you aren't going out of control. They have provided a bunch of new features to assist in retention percentage, but it can be a bit confusing on what is being retained, and what can be viewed again after triggering an alert. It's a difficult balance of making sure you are getting the right data for alerts, and still having the correct information still available for research after the fact.

We were in need of a cloud monitoring tool that was operationally focused on the AWS Platform. We wanted to be able to responsibly and effectively monitor, troubleshoot, and operate the AWS platform, including Server, Network, and key AWS Services.

Tooling that highlighted and detected problems, anomalies, and provided best practice recommendations. Tooling that expedites root-cause analysis and performance troubleshooting.

Datadog provided us the ability to monitor our cloud infrastructure (network, servers, storage), platform/middleware (database, web/applications servers, business process automation), and business applications across our cloud providers.

Datadog provided us the tooling to help us effectively monitor, troubleshoot, and operate the AWS platform, including Server, Network, Database, and key AWS Services. It highlights detected problems and anomalies and provides best practice recommendations, expedites root-cause analysis, and performance troubleshooting.

Datadog provides analytics and insights that are actionable through out-of-the-box visualizations, dashboards, aggregation, and intuitive searching that shortens the time to value and account for our limited time & resources we have to operate in production.

The most valuable feature is the dashboards that are provided out of the box, as well as ones we were able to configure. Specific Dashboards that were provided that made things easier were EC2, RDS, Kubernetes dashboards.

We also use the logging tool, which makes searching for specific error logs easier to do.

Datadog Logging provides the capability for us to use AWS logs such as VPC Flow Logs, ELB, EC2, RDS, and other logs that provide lots of relevant operational data but are not actionable. Datadog provides a tool that can provide us analytics and insights that are actionable for visualizations, dashboards, alerting, and intuitive searching.

More pre-configured "Monitor Alerts" would be helpful. Datadog's knowledge of its customers and what they are looking for in terms of monitoring and alerting could be taken advantage of with pre-canned alerts. They have started this with "Recommended Monitors".  That feature was very helpful when configuring our Kubernetes alerts. More would be even better. 

Datadog tech support is very good. One area that could be more helpful is actually talking to someone or sharing your screen to help troubleshoot issues that arise. For new cloud engineers just coming into the cloud monitoring field, there is a learning curve. There is a lot to learn and figure out. For example, we still ran into some issues configuring the private link and more videos of how to do things could be of use.

We have been using Datadog for one year.

We have not run into any issues with stability.

The scalability of Datadog is very good.

Customer service has been excellent.  I communicate weekly a Datadog Customer Success Manager.  He helps me followup on any open issues or questions that we may have.  Technical support has been very good. Opening tickets is easy.  Sometimes a Tech Engineer may take a bit of time to get back with you.  Communicating with Tech Engineer has to be done via ticket/email - no phone assistance is available.

we did not.

Procedures for setup seemed straightforward but once you got going, there were some issues. For us, getting our private link to work needed additional tech support. They were able to help us resolve the issue we were experiencing. I think the procedures could be done a bit better to help you with setup.

We deployed it ourselves.

Datadog helps us minimize downtime and helps us resolve issues quickly.  

Pricing seemed easy until the bill came in and some things were not accounted for. The issue may have been that we didn't realize what was being accounted for, such as the number of servers and the number of logs being ingested.

Datadog had really good pre-sale reps that work with us but need to make sure all the details are covered.

The solution we were looking for needed to provide out-of-the-box capabilities that shorten the time to value. We had limited time & limited resources. Datadog had high recommendations in these areas, so we decided to do a trial with them.

We are very pleased with Datadog overall.

Datadog has assigned an account rep to us that meets with us regularly to make sure all our needs are being met and help us get answers to any questions or issues we are running up against. They have been of great helping us standup monitoring of our Kubernetes environment.

We primarily use Datadog for the monitoring of EC2 and ECS containers running mostly Rails applications that host a SaaS product. We also monitor ElasticSearch and RDS, and we are working on adding their Application Performance Monitoring solution to monitor our applications directly.

We use DataDog to create dashboards, graphs, and alerts based on interesting metrics. DataDog is our first place to look to find the performance of our system.

We also use their logging platform and it works well. Especially useful is that the logs and metrics are tightly integrated so you can jump between them easily.

Developers are able to see how code is running in production, where this was mostly opaque previous to us implementing DataDog. We are able to emit custom metrics that are specific to our business, and the built-in metrics have also proven useful. Having a wealth of information has helped us investigate outages, and having historical data helps us tune our system.

DevOps engineers are able to put sensors around our system to proactively detect problems, whereas before, our engineers heard about problems from customers. Logs are easier to find for developers.

Metric graphing and Dashboards are the most valuable features because they give us good observability into our system and work well to alert us when interesting things happen. We use this functionality daily.

We value the monitoring capability since it allows us to be pushed alerts, rather than have to observe graphs continually. The integrations with Slack and PagerDuty enable us to be interrupted appropriately and keep a running tab on the system without bothering us unnecessarily.

The online process monitoring has been extremely helpful, as it gives engineers the ability to see the live status of all the processes running our systems without them having to log in.

Their logging solution is expensive for our use case. They do have the capability to rehydrate old or incomplete logs, and it works, but I would rather not have to think about that operation.

Datadog has a lot of documentation, but a lot of that documentation assumes you know how the service works, which can lead to confusion. Positive note is that they do have lots of documentation, it just needs better curation.

Their APM solution still needs some work, but they are actively developing it. I would also like to see more database-specific application monitoring.

I have been using Datadog for five years across two companies.

Any issues are addressed and communicated very quickly. I have not had any issues with uptime.

If you do not need 100% of data such as logs, APM traces, etc., this scales well. It does not scale as well if you want 100% of your logs indexed. You should understand any other usage-based bills before using any part of their service as it is very easy to run up a large bill.

The performance of the system scales very well, and host monitoring and APM are relatively cheap.

Account support is excellent.

Customer support is good if you get them to go beyond pointing out the right documentation.

Previously, I used homebuilt solutions with Nagios and Cacti but found that there was far too much work to understand them and keep them up and fed compared to the value that I got. They also did not integrate well with existing data sources without a lot of effort.

I also previously used StackDriver and found it too opinionated. I like that DataDog gives you tools to work with certain types of data and make your own graphs, monitors, etc., whereas, with StackDriver, I felt like there were a limited number of ways you could accomplish goals.

The basic setup is easy. A more advanced setup can be tricky because the documentation assumes you know how the system works already. Support is somewhat helpful, but mostly points out the documentation you should already have found.

We implemented in-house.

My advice is to understand what number of hosts and data you want to commit to. Beware that usage-based billing is both a blessing and a curse. It is easy to run up a large bill, so become familiar with the cost of each piece of your bill and use the metrics they supply to estimate and monitor your bill.

I have had good luck with their support team helping us to figure out the correct commit levels. Their account support is excellent in this regard. I have heard their sales team can be aggressive, but I have not experienced it personally.

I originally chose Datadog because of my previous experience. We recently considered moving over to New Relic because we liked their APM solution better. However, the pricing of New Relic and our familiarity with Datadog won over. New Relic is a good product but it didn't fit our overall needs as well as Datadog.