Datadog Enterprise (Container Agent)

The primary purposes for which Datadog  is used include infrastructure monitoring and application monitoring.

The main use case for Datadog  integration capabilities is to monitor workloads in public cloud, and those public cloud integrations that reached the public cloud metric natively were helpful or critical for us. We are not using Datadog for AI-driven data analysis tasks, but more cloud-native and vendor-native tools at the moment, and at the time when I was still in my last employer, we didn't use Datadog for the AI piece at all.

I find alerting and metrics to be the most effective features of Datadog for system monitoring. It was still cheaper to run Datadog than other alternatives, so the running costs were cheaper because it was SaaS and quite easy to use.

Datadog is only available in SaaS.

The pricing nowadays is quite complex.

In future updates, I would like to see AI features included in Datadog for monitoring AI spend and usage to make the product more versatile and appealing for the customer.

I have been using Datadog since 2014.

There were no problems with the deployment of Datadog.

The deployment of Datadog just took a few hours.

The challenges I encountered while using Datadog were in the early days when the product was missing the ability to monitor Kubernetes  and similar features, but they have since added those features. At the moment, I don't think there are too many challenges that I am worrying about.

One person is enough to do the installation.

I am not working with any of these solutions currently because I'm on sabbatical, but I used to work with Datadog six months ago, and now at the moment I'm on sabbatical.

We were using the tools that AWS  and Azure  came with natively to monitor the AI workflows on their platforms.

I used to work as the CTO at Northcloud, but I no longer work there.

On a scale of one to ten, I rate Datadog an eight out of ten.

The 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 use Datadog  for monitoring and observing all of our systems, which range in complexity from lightweight, user-facing serverless lambda functions with millions of daily calls to huge, monolithic internal applications that are essential to our core operations. The value we derive from Datadog  stems from its ability to handle and parse a massive volume of incoming data from many different sources and tie it together into a single, informative view of reliability and performance across our architecture.

Adopting Datadog has been fantastic for our observability strategy. Where previously we were grepping through gigabytes of plaintext logs, now we're able to quickly sort, filter, and search millions of log entries with ease. When an issue arises, Datadog makes it easy to track down the malfunctioning service, diagnose the problem, and push a fix.

Consequently, our team efficiency has skyrocketed. No longer does it take hours to find the root cause of an issue across multiple services. Shortened debugging time, in turn, leads to more time for impactful, user-facing work.

Our services have many moving parts, all of which need to talk to each other. The Service Map makes visualizing this complex architecture - and locating problems - an absolute breeze. When I reflect on the ways we used to track down issues, I can't imagine how we ever managed before Datadog.

Additionally, our architecture is written in several languages, and one area where Datadog particularly shines is in providing first-class support for a
multitude of programming languages. We haven't found a case yet where we
needed to roll out our own solution for communicating with our instance.

A tool as powerful as Datadog is, understandably, going to have a bit of a learning curve, especially for new team members who are unfamiliar with the bevy of features it offers. Bringing new team members up to speed on its abilities can be challenging and sometimes requires too much hand-holding. The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data. This would give them the confidence to navigate the tool and make the most of all it offers.

The company was using it before I arrived; I'm unsure of how long before.

We use Datadog for monitoring the performance of our infrastructure across multiple types of hosts in multiple environments. We also use APM  to monitor our applications in production. 

We have some Kubernetes clusters and multi-cloud hosts with Datadog agents installed. We have recently added RUM to monitoring our application from the user side, including replay sessions, and are hoping to use those to replace existing monitoring for errors and session replay for debugging issues in the application.

We have been using Datadog  since I started working at the company ten years ago and it has been used for many reasons over the years. Datadog  across our services has helped debug slow performance on specific parts of our application, which, in turn, allows us to provide a snappier and more performant application for our customers. 

The monitoring and alerting system has allowed our team to be aware of the issues that have come up in our production system and react faster with more tools to debug and view to keep the system online for our customers.

Datadog infrastructure monitoring has helped us identify health issues with our virtual machines, such as high load, CPU, and disk usage, as well as monitoring uptime and alerting when Kubernetes containers have a bad time staying up. Our use of Datadog's Application Monitoring, APM  over the last six years or so has been crucial to identifying performance and bottleneck issues as well as alerting us when services are seeing high error rates, which have made it easier to debug when specific services may be going down.

We have found that some of the different options for filtering for logs ingestion, APM traces and span ingestion, and RUM sessions vs replay settings can be hard to discover and tough to determine how to adjust and tweak for both optimal performance and monitoring as well as for billing within the console. 

It can sometimes be difficult to determine which information is documented, as we have found inconsistencies with deprecated information, such as environment variables within the documentation.

I've been using the solution for ten years.

The solution seems pretty stable, as we've been using it for more than a decade.

The solution seems quite scalable, especially within Kubernetes. Costs are a factor.

SUpport has been very helpful whenever we need it.

Positive

We had tried some other APM monitoring in the past, however, it was too expensive, and then we added it to Datadog since we were already using Datadog and it seemed like a good value add.

The solution is straightforward to set up. Sometimes, it is complex to find the correct documentation.

We handled the setup in-house.

 Our ROI is ease  of mind with alerts and monitoring, as well as the ability to review and debug issues for our customers.

Getting settled on pricing is something you want to keep an eye on, as things seem to change regularly.

We used New Relic previously.

Datadog is a great service that is continually growing its solution for monitoring and security. It is easy to set up and turn on and off its features once you have instrumented agents and tailored solutions to your needs.

We use Datadog  across the enterprise for observability of infrastructure, APM , RUM, SLO management, alert management and monitoring, and other features. We're also planning on using the upcoming cloud cost management features and product analytics.

For infrastructure, we integrate with our Kube systems to show all hosts and their data.

For APM , we use it with all of our API and worker services, as well as cronjobs and other Kube deployments.

We use serverless to monitor our Cloud Functions.

We use RUM for all of our user interfaces, including web and mobile.

It's given us the observability we need to see what's happening in our systems, end to end. We get full stack visibility from APM and RUM, through to logging and infrastructure/host visibility. It's also becoming the basis of our incident management process in conjunction with PagerDuty.

APM is probably the most prominent place where it has helped us. APM gives us detailed data on service performance, including latency and request count. This drives all of the work that we do on SLOs and SLAs.

RUM is also prominent and is becoming the basis of our product team's vision of how our software is actually used.

APM is a fundamental part of our service management, both for viewing problems and improving latency and uptime. The latency views drive our SLOs and help us identify problems.

We also use APM and metrics to view the status of our Pub/Sub topics and queues, especially when dealing with undelivered messages.

RUM has been critical in identifying what our users are actually doing, and we'll be using the new product analytics tools to research and drive new feature development.

All of this feeds into the PagerDuty integration, which we use to drive our incident management process.

Sometimes thesolution changes features so quickly that the UI keeps moving around. The cost is pretty high. Outside of that, we've been relatively happy.

The APM service catalog is evolving fast. That said, it is redundant with our other tools and doesn't allow us to manage software maturity. However, we do link it with our other tools using the APIs, so that's helpful.

Product analytics is relatively new and based on RUM, so it will be interesting to see how it evolves.

Sometimes some of the graphs take a while to load, based on the window of data.

Some stock dashboards don't allow customization. You need to clone them first, but this can lead to an abundance of dashboards. Also, there are some things that stock dashboards do that can't yet be duplicated with custom dashboards, especially around widget organization.

The "top users" widget on the product analytics page only groups by user email, which is unfortunate, since user ID is the field we use to identify our users.

I've used the solution for three and a half years.

The solution is pretty stable.

The solution is very scalable.

Support was excellent during the sales process, with a huge dropoff after we purchased the product. It has only recently (within the past year) they have begun to reach acceptable levels again.

Neutral

We did not have a global solution. Some teams were using New Relic.

The instructions aren't always clear, especially when dealing with multiple products across multiple languages. The tracer works very differently from one language to another.

We handled the setup in-house.

We have built our own set of installation instructions for our teams, to ensure consistent tagging and APM setup.

We did look at Dynatrace .

The service was great during the initial testing phase. However, once we bought the product, the quality of service dropped significantly. However, in the past year or so, it has improved and is now approaching the level we'd expect based on the cost.