Datadog Reviews

We primarily use the solution for monitoring and log analysis.

Datadog shows all the logs for the services, and it is very useful for troubleshooting.

The most valuable aspect of the solution is the APM.

The logging capabilities are quite useful. 

The logging could be improved in the future. 

I've used the solution for four years.

We are providing managed services to our customers across multiple industries. Datadog is key to delivering these services by bringing the observability, monitoring, and alerting capabilities we need to operate at scale. 

We operate custom cloud native workloads as well as ISV products such as Atlassian Jira or Confluence. 

Integrating Synthetics, infrastructure, and application performance monitoring, as well as piping all logs through Datadog allows us to operate more with less with good alerting right in time.

We are providing managed services to our customers across multiple industries. 

Datadog is key to delivering these services. It brings in observability, monitoring, and alerting capabilities - all of which we need to operate at scale. 

We operate custom cloud native workloads as well as ISV products such as Atlassian Jira or Confluence. 

Integrating Synthetics, infrastructure, and application performance monitoring, as well as piping all logs through Datadog, help with getting alerts in real-time.

We are providing managed services to our customers across multiple industries. 

Datadog delivers observability, monitoring, and alerting capabilities we need to operate at scale. 

Operating custom cloud native workloads as well as ISV products such as Atlassian Jira or Confluence is also something we do. Integrating Synthetics, infrastructure, and application performance monitoring, as well as piping all logs through Datadog allows us to operate while grabbing alerts in real-time.

The current way accounts are billed could be vastly improved - especially when involving multiple organizations across multiple accounts in combination with reserved commitments. 

Being able to have an automatic materialized report on certain dashboards that could be exported as PDF to be shared with non-Datadog users could help a lot. 

Other than that, we are more than happy with the features we use regularly.

We have been using Datadog since 2015.

We use actual user monitoring and have set up thresholds for alerts to PagerDuty, Sentry, Slack, and so on. We also have dashboards set up for tracking latency and error rates. 

As an individual contributor, I also try to set up dashboards for the individual feature projects I work on. I'd like to learn more ways to use this, though, especially when it comes to more proactive approaches to issues. A starter pack of common-use types would be nice.

The solution it has improved our organization by expanding the awareness of issues and alerts beyond SRE and really empowering software engineers at a team level to make changes to monitoring and incident responses.

There could still be more training to bring this even further. A lot of the time I get into Datadog and it's already an incident and I am not in the right mindset to learn about the product or set alerts up.

The feature I have found most valuable is when I can reuse existing monitors and alerts for new dashboards. 

It is nice that things are so integrated, and any individual thing I build out can likely be re-used across the suite.

Other features I have found useful include the stack traces, especially when it links back to something specific I can look for in the code base. I also like that you can group similar alerts together to look for trends over time instead of one in isolation and see if there have been regressions.

The biggest improvement would be around educational content and helping new users get started. I've only been using Datadog for a few months, and at first, it was frankly overwhelming in terms of both the UI and the available capabilities. The workshops today have helped with this, however, more could be done.

As a user, I have about six or more months of experience with the solution. As a company, I'm not sure.

I was not in charge of evaluating any other solutions.

We use the solution for testing all of our application's endpoints. It is making sure that they work on a consistent basis.

We enjoy the multistep API tests.

They need to implement template variables into the message response body. They could be injected in the subsequent calls. However, they fail to be able to use those variables anywhere in the alert body message that is sent out.

We've been using the solution for a year.

I've never thought about stability and have no insights.

I find we are getting tossed from engineer to engineer. It is not fun when you have an ongoing problem. That is something from the customer resolution team that needs to be addressed.

Positive

We previously used Selenium. I was forced to use it. However, Datadog was a way simpler solution to setting up browser and API tests quickly.

The solution is very straightforward since it has a good GUI.

The initial setup was handled in-house.

It is not my job to track the ROI.

I have no details about the pricing. 

We did not evaluate other solutions. 

The solution is a SaaS.

We use this solution to monitor our Kubernetes clusters, nodes, deployments, daemon sets, replica sets, and pods.

I've been using this solution for a few months at my current company as a member of the Kubernetes team we use Datadog to provide monitoring and telemetry for our team and our customers.

This solution has improved our organization by giving us deeper insight into what's running in our clusters and their performance of it.

I have found the logging and tracing features the most valuable.

The documentation could be improved regarding setting up the agent properly and debugging.

I've been using this solution for a few months at my current company.

Our use case is mainly deploying into our applications for monitoring/logging observability. We currently have our microservices feed into an actuator that exists in each instance of our application that extends to a local and central Grafana for client and internal visibility. The application we use is Grafana.

Logging captures application and system logs that are ported to each application instance for querying.

Whenever anything occurs that is considered unhealthy from a range of health checks, we have notification rules configured internally and externally for a prompt response time.

We have been able to be a more confident, knowledgeable, and capable team when everything is being ported into a centralized format. Beforehand, knowledge was isolated to individuals. Knowledge in terms of what information represented and where it was led to a lack of confidence. By having everything in one place, rules out that confusion and allows us to respond better to issues.

It also allows for personal growth as our team is learning the application from the ground up, and each person is enhancing their own skills.

The valuable features include the following: 

• We are currently utilizing a decentralized distributed framework for our deployment, including our monitoring/logging observability capabilities. Centralizing them, if contingent on our company privacy guidelines, will be a big help in tracking and responding to issues that come up and have the means to understand the origin of the log management tools that were demonstrated.
• The ability to fiddle around and manipulate how logs are outputted.
• The ability to track AWS Lambda functions, Cloudformation, and Cloudwatch allow someone that is not savvy to dip their toe into understanding their own product.
• The integration into AWS is key as well as our software is currently bound to AWS.

The ability to find what you are looking for when starting out could be improved. It was a bit overwhelming trying to figure out what is the best solution. It led to many prototypes or time spent just perusing documentation. If we were able to select bundles or template use cases, we would hit the ground running quicker.

I've used the solution for one year.

We are using the solution for scaling up the website for market data applications. EC2 and Datadog have enabled high-level monitoring of underlying infra and services.

The Datadog profiler comes in handy to pinpoint issues with resource utilization during peak hours, and traces/log management helps narrow down the root cause.

The network map is crucial in identifying bottlenecks and determining what needs more attention.

Host map helps identify problematic hardware and devise ways to counter issues that arise during scaling, and deploying solutions on the cloud.

While my team is relatively new to Datadog, I already see immense value in switching over to Datadog as the primary APM and NPM tool.

The arsenal of features it offers is bound to come in a clutch when facing production issues, and when finding out what went wrong is crucial.

The network map has helped to figure out the golden signals and optimize the infrastructure.

The synthetics have helped ensure the high availability of arch functions as intended.

The network map is useful. With it, we have the ability to see the data flow across the entire network path across all the applications is highly valuable as the data from this service helps identify network bottlenecks, non-performant applications, and bad endpoints.

This is especially crucial for a high-availability website aimed at market data applications where low latency is crucial.

The host map gives a clear picture of the entire infrastructure, and the ability to switch between logs, metrics, and traces is very handy when it comes to debugging issues on the fly.

I love the ability to install the integrations and agents quickly. This is a well-made product.

To be very fair, I haven't had enough experience with Datadog to pick out improvements.

My involvement with Datadog has largely been positive. I love the simplicity and intuitiveness it offers - even for nontechnical folks who just might be starting out with developing technical chops in their domain.

I've used the solution for three years.

We use the application for our application monitoring, data security monitoring, and log management. What we like about the application is that it helps us to track issues more proactively instead of reactively.

There are other improvements we would like to see.

1. Being able to restrict users from seeing or viewing specific dashboards once they log in

2. They can cut down the prices for Cloud SIEM. It seems very useful, however, the prices are high. Some organizations are finding it difficult to make decisions in terms of getting the tool.

We use the application for our application monitoring, data security monitoring, and log management. It helps us to track issues proactively instead of reactively.

It helps us better manage our logs.

We can effectively track down issues.

We have dashboards that give us an overview of our environment.

The tools I have found useful include the Datadog cloud security posture manager and cloud workload security.

Datadog is a great tool, and we value the services they offer. They should continue expanding and integrating with more third-party apps.

I've used the solution for three years. 

I love its stability.

It is very scalable.

Technical support has been great.

Positive

We previously used AWS.

The initial setup is not too complex.

We've seen an ROI of 50%.

It's a little pricy yet worth it.

We did not previously evaluate another solution.