Datadog Reviews

We use it mostly for logging log messages from our Kubernetes and EC2 instances, for example, system messages and errors. Also, we want log messages from our firewalls and other network infrastructure in case of network issues. We intend to use it for application logging, et cetera, to get insight into internal problems in the applications in Kubernetes pods. We want to use it for monitoring in case of system problems and hardware failures so that it can notify us.

It's good to have a single location for all the logs. If you have logs coming from a whole lot of sources, it makes it hard to find where the problem lies. 

We had to spend a lot of time logging into various systems and pursuing a billion different log files looking for something that stands out as a possible cause of the issue. That can take a lot of time and doesn't give much visibility into the possible interactions between systems.

Datadog has a lot of features to be able to drill down deep into the swath of logs that our platforms generate. 

It has a lot of ability to make fancy and deep searches using regular expressions and to graph them into useful and interesting dashboard graphs. 

The plethora of built-in/downloadable integrations make it much easier to set up for our platforms. Otherwise, we'd have to parse the log files ourselves, which would take a great deal of effort. Had to do it before when had to use an ELK stack for logging, which was painful.

Some of the interface is still confusing to use. It has many features, and it takes a lot of effort to figure out what they all mean. Maybe having tooltips or something would be helpful. Also, some of the integrations are better than others.

I've used the solution for a month.

The solution seems very stable.

Have used an ELK stack before. However, it took a lot of effort to maintain, and parsing the logs was difficult.

We implemented the solution in-house.

We are using Datadog for server metrics, log aggregation and searching, system monitoring, alerting the team about errors, and dashboards for our developers. It's used by the Site Reliability Engineering team and Management of all levels. 

It's assisting us in proving SOC II compliance. 

We're looking to improve our usage of Datadog's RUM and APM components to get better and more performance insights on our production environments. 

We're also looking to leverage more synthetic monitors and runbooks for anyone responding to incidents.

The ability to easily drill down into log queries quickly and efficiently has helped us to resolve several critical incidents so far this year, and we heavily rely on a series of dashboards showing us various queues and load on CPU and memory for servers. 

We also have a view of the information required when we begin the patch and/or upgrade processes. 

I've also set up several monitors to alert the Site Reliability Engineering team when various metrics show a server might be reaching capacity. We use it to send an email suggesting we increase the size of the cloud instance.

The ability to easily drill down into log queries quickly and efficiently has helped us to resolve several critical incidents. We heavily rely on dashboards that are showing us various queues and load on CPU and memory for servers. 

We also have a view of the information required when we begin the patch and/or upgrade processes. 

I've arranged several monitors to alert the Site Reliability Engineering team when various metrics show a server that might be reaching capacity. We use it to send an email suggesting we increase the size of the cloud instance.

Datadog could always lower the price! In general, more demos online and maybe more free hands-on tutorials for basic functionality would be good for less technical users. 

I would also prefer more chances to amend the contract more than twice a year. As a smaller but growing company, it can be difficult to adequately predict demand.

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

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.

Ingesting data from various sources to monitor the log metrics of the system and enabling an alert mechanism to notify the teammates if something goes wrong.

More specifically, having Datadog agents as integration to different services provides easy access and management.

The solution has helped out organization by allowing us to ingest data from various sources to monitor log metrics and enabling alert mechanisms to notify teams if something goes wrong.

Datadog agents act as an integration to different services, providing easy access and management.

The solution is useful for ingesting data from various sources. This helps to monitor the log metrics of the system. It has alert mechanisms that can be enabled to notify the teams if something goes wrong.

Datadog offers good integration to different services. It provides for easy access and management.

Ingesting data from various sources to monitor the log metrics of the system can always improve so that, if something goes wrong, the right teams are alerted. 

We've used the solution for close to 1.5 years.

We use a SaaS deployment.

We use Datadog incident management for our incident tooling. Whenever we run into an incident, we try to use it. It allows us to create a separate Slack channel for it.

Keeping track of incidents makes it possible to share learnings and follow-up. It is also required for SOC2 compliance.

It is great that creating an incident is possible from Slack while having all the relevant data in Datadog. It allows us to not think too much about organizing incidents, which is a highly stressful situation.

I would like the tooling to have better integration in Slack, specifically sending out reminders to the relevant people to take breaks, do a retrospective, and specify with emojis which messages to log.

Learning about the tooling could also be improved. It is a bit clunky to use, especially in an incident. It could definitely be more streamlined.

I've used the solution for about a year.

We have deep integration with Datadog for observability and monitoring.

We use everything from APM, logs, and RUM to monitor and dashboards for tracking system health. 

We are trying to move from many different solutions for error tracking/observability to a single platform (Datadog).

We are currently in the process of setting up logging in Datadog in order to maintain our logs better. We are looking to create more insights into the real user flows by using real user monitoring (RUM) too.

We use Datadog quite extensively. I primarily work with APM traces and logs to debug issues and unblock myself in my day-to-day role. I have found the traces and spans most useful in providing details about why certain services are performing poorly.

Datadog provides a lot of value in terms of adding monitoring and observability to our app. There are so many different solutions, it is sometimes difficult to gauge where to start, and I sometimes miss a lot of functionality (such as the very useful error-tracking dashboard mentioned in my review above).

As I mentioned above, we use Datadog quite extensively. In my day-to-day role, I primarily work with APM traces and logs to debug issues and unblock myself. 

I have found the traces and spans most useful in providing details about why certain services are performing poorly.

Additionally, the flexibility to create notebooks and dashboards and fully customize them gives us a lot of power to track the exact services and endpoints we are working on.

Furthermore, we are also using monitoring to page us if things break, and the Slack integration provides us instantaneous feedback on how things are performing.

We need more visibility into the error tracking dashboard. I only learned about it during a demo at Dash Con. That said, it seems to be a very useful tool.

Additionally, we want to export our dashboards and monitors to source control, and there doesn't seem to be any easy way to do so.

I've used the solution for four years.

For logging, we are moving from LogDNA to Datadog to have access to everything in one place. Also, searching and traversing through logs seems easier in Datadog

I did not evaluate others, however, my team probably did.

Datadog provides a lot of value in terms of adding monitoring and observability to our app. There are so many different solutions; it is sometimes difficult to gauge where to start, and I sometimes miss a lot of functionality. For example, the very useful error-tracking dashboard that I just discovered.

We primarily use the solution for the RUM, security monitoring, and streams.

We need to monitor users and what they access. We also need to identify security loopholes and attack patterns and identify and quickly respond to issues.

We can identify pushbacks, and get insight into application components that stack up with each other. We can understand which components, libraries, and code to alert teams.

Using Datadog, we can raise incidents, track incidents to completion, and be able to gather data for reporting and post-mortem.

The solution allows us to track fixes and tracks their test coverage. With it, we get confidence in the fix/improvement phase and be able to provide a response.

The solution has improved the organization by providing good insights into app performance and offering good dashboards.

With it, our company can track fixes and track test coverage. We get confidence in the fix/improvement and are able to provide a response.

I've been able to present data to the team/ management based on the team's dashboards.

It's helped us when we've needed to monitor users and what they access or needed to identify security loopholes and attack patterns. It can help identify and quickly respond to issues.

Datadog allows us to identify pushbacks, and get insight into application components (how they stack up with each other). When we need to know which component, libraries, code, and teams to alert, we can raise and track incidents to completion and gather data for reporting and post-mortems.

The APM and container monitoring are excellent aspects of the solution.

It helps with monitoring users and what they access.

We can identify security loopholes and attack patterns while quickly responding to issues.

Our team can now identify pushbacks and get insights into application components. We can gather data for reporting and post-mortem, and we can track fixes and test coverage. Datadog allows us to gain confidence in the fix/improvement.

I've been able to present data to the team/ management based on the team's dashboards.

We'd like Datadog to make the log storage cheaper. Right now, it makes our life difficult since logs are stored separately.

We should not have to rely on team members to educate themselves in Datadog features. There should be templates that anyone can select, and they should be able to create dashboards easily. This is really slowing us down. It takes time to to explore the full potential of Datadog.

I've used the solution for two years.

We use Datadog for application logs, error tracking, performance tracking, alerting, and overall production state surveillance. 

It helps us improve observability and ease of maintenance through better information for our support teams and their issue qualification. 

We also use dashboards to keep all the information at ready and easy to access. SLOs notably for our uptimes but also our feature usage. It also feeds our alerting for our on-call SREs into PagerDuty by launching alerts when specific parameters are exceeded.

Our usage of Datadog has allowed us to improve our observability at great lengths. We have been able to track pain points more easily with it, and be able to define custom metrics to track our user's usage of the features we roll out.

Being able to generate dashboards has given higher management a better view of our teams' work and has allowed for better client information by our sales team as they have a more transparent way ofdealing with our upcoming features.

Dashboards and their versatility are among the most valuable features. They allow us to have internal facing trackers of our application's issues, usages, and features. They also allow us to have a better understanding of how users react to new features, and to display more information to other teams or also clients through uptime SLOs, et cetera.

We also found the Synthetics Tests and especially the Browser Tests very helpful. It is a nicer way to create end-to-end tests in a more user-friendly way than through code. They are very valuable in saving time compared to code-based testing.

Documentation is also very clear and interesting.

We would like to see some versioning system for the Synthetic Tests so that we could have a backup of our tests since they are time-consuming to make and very easy to damage in a moment of error.

I look forward to seeing the next features that will be released.

I have been using the product for a year and a half. The company has been using it for longer. I don't know the exact details.

We have yet to have a large-scale problem with stability using Datadog. It's very satisfying.

The scalability is very good.

I've had only a few experiences with customer support, and it went well. They were fast!

Positive

We did not use a different solution previously.

I wasn't there for the initial setup.

I wasn't there for the initial setup.

I cna't speak to the ROI.

I don't give advice regarding that.

I wasn't part of the decision-making process.

It would be nicer if the pricing information was easier to find in the documentation. Sometimes it helps to get an overall idea of the cost of certain options.

We use the solution for monitoring, logging, and alerts. 

Thanks to Datadog, we report errors using the logger integrated into our services, which is crucial since we only do unit tests. The infrastructure team handles the monitoring part, so I can't give more insights about that. I am an API developer, so I use Datadog mainly for logging.

The alerts are connected to Microsoft Teams in a specific channel, and we pay a lot of attention to it, and we usually create tickets based on these alerts.

Thanks to the logs, we manage to make better reports through Jira and also to trace the request with more facility than we would be able to do otherwise. 

Since there are many teams in my company, the fact that we can share the trace of an error, for example, together with all the information about the log, we are able to save a lot of time when it comes to communication between everyone.

The most valuable feature for me so far is logging. We do not do integration tests, so we rely a lot on tracing all the requests and we report errors to different teams in the company together with logs that we take from Datadog.

Since I am an API developer, I do not use so much with the other features. Also, I have been in the company for only four months. I have only worked with monitors and alters.

I value tracing the request and being able to tell other teams which component, service, or line of code has an issue.

Since I have only been in the organization for four months, I only worked with the log, alerts, and monitoring. I do not have so many insights to share about what can be improved.

I am not an expert user, and not even an intermediate user yet. Rather, I am a beginner.

That said when the logs are too big, and Datadog splits them, the JSON format breaks and it is not so useful for us.

I've used the solution for four months.

I did not previously use a different solution.

I will get informed about this, I have no idea about costs as an API developer. But I get curious about it

I did not evaluate other options previously.