Datadog Reviews

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.

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 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.

We use Datadog to view and aggregate logs and monitor all of our services. We have a lot of running infrastructure and it is very convenient to have logs and metrics all aggregated somewhere we can view and chart them. 

I use Datadog to create dashboards and runbooks, and sharable graphs, which really help out my whole team. We mostly use logs and APM, yet have been starting to use other products. I would like to use more synthetic monitors.

It has democratized our logs and metrics, allowing all engineers to have insight into how our apps perform. It is also extremely helpful when debugging issues. 

It would be very difficult to debug issues without aggregated logs and APM traces. 

It has also definitely saved us some money since we can keep an eye on our running infrastructure in an easy-to-see way, rather than a less friendly CLI. It has been a very big help!

The log stream has been the most useful thing. Having so many logs on so many different running containers means it is very inconvenient to view them individually. Datadog's log aggregation is really helpful since it lets me and every other engineer on my team login, view, and share logs when we need to debug our application. 

APM has also been extremely helpful for debugging issues and profiling and optimizing our apps. Dashboards have also been really helpful for communicating needs and priorities to engineering leadership. 

It is very easy to get buy-in with graphs to back things up.

I recently saw the education, and it is amazing. Events like DASH are extremely helpful in understanding the deep set of features. Anything that helps to educate users is a huge win here. 

The menu on the left is pretty dense (and I know it has to be). I never knew about the cmd+k functionality until recently. It would be helpful to offer more tips/cheat sheets to see handy shortcuts like that.

I've used the solution for three years. 

We previously used AWS Cloudwatch logs. It was way less friendly and fully featured.

The solution is pretty straightforward to set up. It helps with logs and metrics, and the AWS integration is really great.

We handled the implementation in-house.

It is hard to educate an entire team. There is a big learning curve.

We are using the solution for migrating out of the data center. Old apps need to be re-architected. We are planning on moving to multi-cloud for disaster recovery and to avoid vendor lockouts. 

The migration is a mix between an MSP (Infosys) and in-house developers. The hard part is ensuring these apps run the same in the cloud as they do on-premises. Then we also need to ensure that we improve performance when possible. With deadlines approaching quickly it's important not to cut corners - which is why we needed observability

Using the product has caused a paradigm shift in how we deploy monitoring. Before, we had a one-to-one lookup in ServiceNow. This wouldn't scale, as teams wouldn't be able to create monitors on the fly and would have to wait on us to contact the ServiceNow team to create a custom lookup. Now, in real-time, as new instances are spun up and down, they are still guaranteed to be covered by monitoring. This used to require a change request, and now it is automatic.

For use, the most valuable features we have are infrastructure and APM metrics.

The seamless integration between Datadog and hundreds of apps makes onboarding new products and teams a breeze. 

We rely heavily on the API crawlers Datadog uses for cloud integrations. These allow us to pick up and leverage the tags teams have already deployed without having to also make them add it at the agent level. Then we use Datadog's conditionals in the monitor to dynamically alert hundreds of teams. 

With the ServiceNow integration, we can also assign tickets based on the environment. Now our top teams are using the APM/profiler to find bottlenecks and improve the speed of our apps

The real issue with this product is cost control. For example, when logs first came out they didn't have any index cuts. This caused runaway logs and exploding costs. 

It seems that admin cost control granularity is an afterthought. For example, synthetics have been out for over four years, yet there is no way to limit teams from creating tests that fire off every minute. If we could say you can't test more than once every five minutes, that would save us 5X on our bill.

I've used the solution for about three years. 

The solution is very stable. There are not too many outages, and they fix them fast.

It is easy to scale. That is why we adopted it.

Before premium support, I would avoid using them as it was so bad.

Neutral

We previously used AppDynamics. It isn't built for the cloud and is hard to deploy at scale.

The initial setup was not difficult. We just had to teach teams the concept of tags.

We did the implementation in-house. It was me. I am the SME for Datadog at the company.

The solution has saved months of time and reduced blindspots for all app teams.

I'd advise users to be careful with logs and the APM as those are the ones that can get expensive fast.

We looked into Dynatrace. However, we found the cost to be high.

We use this solution for our customer's IP and to support their cloud infrastructure.

Datadog is easy to use and easy to deploy. It's a better solution compared to others on the market in terms of being budget friendly for our customers.

Datadog could be improved if it could detect other software in a container or server. Datadog is better than other APM or observability tools, but it focuses mostly on telling the customer what they need to know about the software, database or applications that land on the server. We also need to know the version before setting up an agent with the APM modeling tool.

In some instances, the owner of a particular software changes to another person and this person did not originally transfer the knowledge or data to manage the server. The new person needs to monitor this server and they need to know what software or version of software was installed on this server before they used the APM agent for monitoring. If datadog could provide this insight, it would improve how we use the solution. 

In a future release, we would like to be able to complete a network traffic or network flow analysis to detect the errors or problems on the network.

I have been using this solution for two years. 

This is a stable solution. 

The initial setup was straightforward. We needed two engineers for the deployment.

This solution is budget friendly.

Overall, Datadog is a good product to use and is easy to deploy.

I would rate this solution a nine out of ten. 

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.