Datadog Enterprise

My main use case for Datadog  is that we offer the application performance management service within PwC as a global team.

A specific example of how my team uses Datadog  for performance management is that my team does not directly use Datadog for performance management; however, we work with approximately 300 teams that use it daily for monitoring their apps. One of the most used cases is to observe when services are up and down and if services are not degraded.

We use most of every product within Datadog across the 300 customers that we have internally.

Datadog has positively impacted my organization because before Datadog, we had multiple APM  tools and monitoring tools, which fragmented the service. The reason was that some tools offered benefits to certain teams, while other tools offered different benefits to other teams. With Datadog, we managed to get everyone on board into a single place and a single tool, providing teams with one spot where they can check everything related to monitoring, and enabling management and leadership to have an overview of all tools working together.

I measured the impact of bringing everything into one place through observation, and I can confirm that efficiency in reporting improved dramatically and it became much easier to observe changes. Standardization was a tremendous win for us. Having a set of standard alerts and monitoring in place allowed us to speed up onboarding for every app. Once the resources are in Datadog, the system provides alerting out of the box. Additionally, cost has decreased dramatically.

Datadog's best features include very high demand for logs management for alerting on indexed logs and a shift towards Flex Logs for storage and long-term storage. Most recently, BitsAI and the LLM part within Datadog has been in focus for us.

Flex Logs has helped my teams because we are migrating from other services to have a unique place to store all the logs, the non-security logs, and the app logs. This has benefited those teams because they also benefit from other services within Datadog such as APM  or other monitoring solutions. By bringing the logs into Datadog, they now have a single place where they can correlate everything.

The LLM integration within Datadog has helped my teams because LLM usage is at the beginning stage right now, and people are very excited. We have all these AI and LLM-based tools, and having the option of monitoring them is a great benefit for us. However, we are in the exploratory phase of this process and have just begun.

BitsAI is very interesting; we have done some testing and we are going to promote it and use it in our production environment. This is a very exciting new tool for us.

Datadog can be improved because sometimes it seems it has not been developed for enterprises. We work with over 300 customers, with each customer having multiple instances or apps within Datadog. We are facing difficulties in controlling access, in privacy settings, and splitting usage and costs for these customers.

We want to be able to customize the cost part, and we would appreciate more granular access control.

I have been using Datadog for four or five years.

Datadog is stable.

We have never had an issue with Datadog's scalability.

Datadog's customer support is good; it could be improved in terms of communication, but it is adequate.

We previously used Grafana , AppDynamics, New Relic , Splunk, and a couple of other smaller, more dedicated tools.

My experience with pricing, setup cost, and licensing is good; nothing out of the ordinary.

Before choosing Datadog, the biggest contender we evaluated was AppDynamics.

My advice for others looking into using Datadog is to test it out and see if it works for you. Try to become accustomed to the tagging part of things, and go through each product to understand what each product within Datadog is offering. I would rate this product an eight out of ten.