We have several teams and several different projects, all working in tandem, so there are a lot of logs and monitoring that need to be done. We use Datadog mostly for alerting when things go down.

We also have several dashboards to keep track of critical operations and to make sure things are running without issues. The Slack messaging is essential in our workflow in letting us know when an alert is triggered. I also appreciate all the graphs you can make, as it gives our team a good overview of how our services are doing.

It has improved our reliability and our time to get back up from an outage. By creating an alert and then messaging a Slack channel, we know when something goes down fairly fast. This, in turn, improves our response time to swarm on an issue without it affecting customers. The graphs have also been useful to demonstrate to higher-ups how our services are performing, allowing them to make more informed decisions when it comes to the team.

The alerts are the most valuable. Having alerts have saved us countless times in the past and is essentially what we use data dog for.

I like how we can customize alerts, and when alerts have become too noisy, we turn their threshold down fairly easily. This is also the case when alerts should be notifying us more often.

I also like the graphs and how customizable they are. It allows us to create a nice-looking dashboard with all sorts of information relating to our project. This gives us a quick overview of how things are going.

It's not that straightforward when creating an alert. The syntax is a little confusing. I guess that the trade-off is customizability. But it would be nice to have a click-and-drag kind of way when creating an alert. So, if someone who isn't so familiar with Datadog or tech in general wanted to create an alert, they wouldn't need to know the syntax.

It would also be great if AI could be used to generate alerts and graphs. I could write a short prompt, and then the AI could auto-generate alerts and graphs for me.

I've used the solution for more than two years.

We currently have an error monitor to monitor errors on our prod environment. Once we hit a certain threshold, we get an alert on Slack. This helps address issues the moment they happen before our users notice.

We also utilize synthetic tests on many pages on our site. They're easy to set up and are great for pinpointing when a bug is shipped, but they may take down a less visited page that we aren't immediately aware of. It's a great extra check to make sure the code we ship is free of bugs.

The synthetic tests have been invaluable. We use them to check various pages and ensure functionality across multiple areas. Furthermore, our error monitoring alerts have been crucial in letting us know of problems the moment they pop up.

Datadog has been a great tool, and all of our teams utilize many of its features. We have regular mob sessions where we look at our Datadog error logs and see what we can address as a team. It's been great at providing more insight into our users and logging errors that can be fixed.

The error logs have been super helpful in breaking down issues affecting our users. Our monitors let us know once we hit a certain threshold as well, which is good for momentary blips and issues with third-party providers or rollouts that we have in the works. Just last week, we had a roll-out where various features were broken due to a change in our backend API. Our Datadog logs instantly notified us of the issues, and we could troubleshoot everything much more easily than just testing blind. This was crucial to a successful rollout.

I honestly can't think of anything that can be improved. We've started using more and more features from our Datadog account and are really grateful for all of the different ways we can track and monitor our site.

We did have an issue where a synthetic test was set up before the holiday break, and we were quickly charged a great amount. Our team worked with Datadog, and they were able to help us out since it was inadvertent on our end and was a user error. That was greatly appreciated and something that helped start our relationship with the Datadog team.

We've been using Datadog for several months. We started with the synthetic tests and now use It for error handling and in many other ways.

Stability has been great. We've had no issues so far.

The solution is very easy to scale. We've used it on multiple clients.

We had a dev who had set up a synthetic test that was running every five minutes in every single region over the holiday break last year. The Datadog team was great and very understanding and we were able to work this out with them.

We didn't have any previous solution. At a previous company, I've used Sentry. However, I also find Datadog to be much easier, plus the inclusion of synthetic tests is awesome.

The documentation was great and our setup was easy.

We implemented the solution in-house.

This has had a great ROI as we've been able to address critical bugs that have been found via our Datadog tools.

The setup cost was minimal. The documentation is great and the product is very easy to set up.

We also looked at other providers and settled on Datadog. It's been great to use across all our clients.

Our company has a microservice architecture, with different teams in charge of different services. Also, it is a start, which means that we have to build fast and move very fast as well. So before we were properly using DD, we often had issues of things breaking, but without much information on where in our system the breaking happened. This was quite a big-time sync as teams were unfamiliar with other teams' codes, so they needed the help of other teams to debug. This slowed our building down a lot. So implementing dd traces fixed this

DataDog has many features, but the most valuable have become our primary uses.

Also, thanks to frequent concurrent deployments, the DataDog alerts monitors allow us quickly detect issues if anything occurs.

The monitors can be improved. The chart in the monitors only goes back a couple of hours, clunky. Also, it can provide more info, like traces within the monitors. We have many alerts connected to different notification systems, such as Slack and Opsgenie.

When the on-caller receives notifications fired by the alerts, we are taken to the monitors. Yet often, we have to open up many different tabs to see logs, traces and info that is not accessible on the monitors. I think it would make all of the on callers' lives easier if the monitor had more data

We've used the solution for three years.

Our primary use case for this solution is comprehensive cloud monitoring across our entire infrastructure and application stack.

We operate in a multi-cloud environment, utilizing services from AWS, Azure, and Google Cloud Platform.

Our applications are predominantly containerized and run on Kubernetes clusters. We have a microservices architecture with dozens of services communicating via REST APIs and message queues.

The solution helps us monitor the performance, availability, and resource utilization of our cloud resources, databases, application servers, and front-end applications.

It's essential for maintaining high availability, optimizing costs, and ensuring a smooth user experience for our global customer base. We particularly rely on it for real-time monitoring, alerting, and troubleshooting of production issues.

Datadog has significantly improved our organization by providing us with great visibility across the entire application stack. This enhanced observability has allowed us to detect and resolve issues faster, often before they impact our end-users.

The unified platform has streamlined our monitoring processes, replacing several disparate tools we previously used. This consolidation has improved team collaboration and reduced context-switching for our DevOps engineers.

The customizable dashboards have made it easier to share relevant metrics with different stakeholders, from developers to C-level executives. We've seen a marked decrease in our mean time to resolution (MTTR) for incidents, and the historical data has been invaluable for capacity planning and performance optimization.

Additionally, the AI-driven insights have helped us proactively identify potential issues and optimize our infrastructure costs.

We've found the Application Performance Monitoring (APM) feature to be the most valuable, as it provides great visibility on trace-level data. This granular insight allows us to pinpoint performance bottlenecks and optimize our code more effectively.

The distributed tracing capability has been particularly useful in our microservices environment, helping us understand the flow of requests across different services and identify latency issues.

Additionally, the log management and analytics features have greatly improved our ability to troubleshoot issues by correlating logs with metrics and traces.

The infrastructure monitoring capabilities, especially for our Kubernetes clusters, have helped us optimize resource allocation and reduce costs.

While Datadog is an excellent monitoring solution, it could be improved by building more features to replace alerting apps like OpsGenie and PagerDuty. Specifically, we'd like to see more advanced incident management capabilities integrated directly into the platform. This could include features like sophisticated on-call scheduling, escalation policies, and incident response workflows.

Additionally, we'd appreciate more customizable machine learning-driven anomaly detection to help us identify unusual patterns more accurately. Improved support for serverless architectures, particularly for monitoring and tracing AWS Lambda functions, would be beneficial.

Enhanced security monitoring and threat detection capabilities would also be valuable, potentially reducing our reliance on separate security information and event management (SIEM) tools.

I've used the solution for two years.

We utilize Datadog mainly to monitor our API integrations and all of the inventory that comes in from our API partners. Each event has its own ID, so we can trace all activity related to each event and troubleshoot where needed.

Datadog gives non-dev teams insights as to what all is happening with a particular event as well as flags any errors so that we can troubleshoot more efficiently.

The dashboards are super convenient to us for a more zoomed out view of what is going on with each integration that we utilize.

There could be more easily identifiable documentation on how to find different things on the platform. It can be overwhelming at first glance, and it's hard to find appropriate documentation on the site to lead you to where you need to be.

I've used the solution for about 1.5 years.