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.

We have a tech stack including all backend services written in TS/Node (mostly) and as a full stack engineer, it is crucial to keep track of new and existing errors. Our logs have been consolidated in Datadog and are accessible for search and review, so the service has become a daily tool for my work.

More recently, session replay has been adopted at my company, but I do not like it so much because the UI elements are not in their place, so it is very hard to see what the users on the web app are actually clicking on.

The best way it has helped us is by consolidating all our logs into a single place and making it easier to find errors. Previously using AWS Cloudwatch was cumbersome and time-consuming. One issue I do have with logs is the length of time they are on the platform. Some issues happen sporadically, so it would be good to have logs for longer than one month by default or make it a configuration.

Another issue that I have is with the search syntax, it could be simpler and it feels like there are too many ways to do the same things.

Logs search is the most valuable feature because it has consolidated all of our backend services logs into one place. Now we can see the relationship between them as requests are going from one service to other dependencies.

One issue I do have with logs is the length of time they are on the platform. Some issues happen sporadically, so it would be good to have logs for longer than one month by default or make it a configuration. I have yet to try rehydrating logs, so this might be an option I need to try. Another issue I have is with the search syntax, it could be simpler. The syntax is a bit cumbersome and there is not an intuitive to save them to look for similar searches in the future.

Finally, while my company replaced a different tool for session replay with DataDog's version, I find it clunky and in need of further improvements. For example, when troubleshooting a web portal issue, it is super important to know what the user clicked, but the elements are not where they should be in the replay.

It is also hard to find details about the sessions, and metadata such as user email, account, etc. that exist on other services with replay features.

I have been using Datadof for approximately five years.

So far we haven't had any issues with uptime and Datadog has been available when needed.

It seems to scale well as we continue to add services that need monitoring.

I haven't had to contact support.

Cloudwatch was not a great tool for what we need to do to troubleshoot issues.

We deployed it in-house with intermediate expertise.

I am not sure how much we are paying, but I use the app often enough to feel like we are getting a good ROI.

I was not involved in the choosing process as a software engineer

Datadog is mainly used to set up alerts and thresholds to monitor real-time metrics and checks.

Integrating Datadog with other platforms has made our monitoring processes a bit easier. It's not super simple, but it's manageable.

For three to four months, we have been experiencing real-time delays. For example, if we're monitoring incoming traffic, the real-time status should be displayed up to a certain point. However, due to delays or issues with Datadog, the real-time data might only be updated at an earlier time. We are experiencing consistent delays in data updates from Datadog, with the most recent data often being delayed by about an hour. This issue has been ongoing for the past four months.

I have been using the product for a year.

My company has 50 users for Datadog.

The tool's deployment is difficult and time-consuming.

The tool is open-source.

If you're thinking about using Datadog for the first time, I suggest getting some basic training in data operations. It'll help you navigate Datadog more easily.
Learning it for the first time is not overly difficult, but it's also not very easy.

I would rate the tool a seven out of ten. While it's a useful tool, we've experienced some issues that haven't been resolved yet. Additionally, setting up dashboards and utilizing all the features requires some training.

Datadog is expensive.

The tool's deployment is easy.

The solution's pricing depends on project volume.

I rate Datadog a seven out of ten.

Datadog has clear dashboards and good documentation.

The solution needs to integrate AI tools.

I avail support from our internal team.

I rate Datadog a nine out of ten.

Datadog has flexibility.

The product needs to have more enterprise approach to configuration.

We use the tool to monitor our whole infrastructure. CPU, memory, and disk space are the types of things we use it for.

It is a stable solution.

It is a scalable solution.

The technical support team is good and responsive.

The initial setup is not very easy and the deployment took eight months.It took quite a few teams to get it all accomplished. I rate it a six out of ten.

I rate the solution eight out of ten.

The solution is primarily used for better understanding the health of applications, modern environments, and many other solutions, which are the main focus of Datadog and many other monitoring tools.

With Datadog specifically, I can look at the health of the technology stack and services, and also integrate multiple metric sources, security, business data, and much more. This makes it a real software solution for centralizing data and unifying monitoring silos in one place. Datadog is like a hub - not just a monitoring software.

The solution primarily has helped the organization by helping us better understand the health of applications, modern environments, et cetera.

We can see the health of the technology stack and services. We can also integrate multiple metric sources, security, business data, and much more. It centralizes data and unifies monitoring in one place. It's Helping reduce costs with other solutions, and also reduces costs with teams that might waste time with manual troubleshooting.

Understanding better the health of applications, modern environments, and many other solutions, is the main focus of Datadog and many other monitoring tools.

With Datadog I can look at the health of the technology stack and services. I can also integrate multiple metric sources, security, business data, and much more. It's great for centralizing data and unifying monitoring silos.

Datadog is a hub, not just a monitoring software. The biggest value of Datadog is looking at the big picture, not only one part of it.

Datadog could have a better business analysis module.

Other vendors have specific business collections and analyses. With Datadog, I don't see much of it. It's possible to do this with custom metrics, dashboards, etc. However, none of those are business-focused - and that is what is lacking in Datadog.

I've used the solution for two years.

We use the SaaS version of the product.