Datadog Reviews

We use Datadog to monitor our Kubernetes clusters. 

We have 3 different clusters for different parts of the SDLC. We run the Datadog agent DaemonSet as well as the Datadog cluster agent. Our services have the APM installed by default. 

To create monitors, we use Terraform. This is provided out-of-the-box for our service owner. 

We run EKS on top of K8s, therefore, we also make use of some of the AWS monitoring capabilities that can be integrated into Datadog. 

We are hugely reliant on Datadog for all aspects of our system.

With Datadog, we were able to gain observability in our system. 

The installation step is pretty straightforward. 

It's easy to use by non-DevOps users. For instance, our engineers do not interact with K8s often; therefore, it is hard for them to debug. However, with Datadog, they are able to view their containers and deployments with a single click. 

We also heavily use the tags to help us identify who the service owners are. This is super useful when we need to track owners for patching or pick up new features we implemented.

The APM and K8s monitoring are the most valuable aspects of the solution. The K8s monitoring allows all customers to view their infra, even if they do not use K8s daily. They can just click on a few tabs to get all of the information they need. 

It is also very easy to install on our system. APM has helped debug applications on our system as well. We were able to view why a service has suddenly shut down.

We also use Datadog for SLOs/SLAs as well. We check the live endpoint of services to ensure they are still up and running.

There is not much that needs to be improved. 

The UI is super user-friendly. The deployment process is easy. We enjoy using the integrations with Slack and PagerDuty. 

Customer support is awesome from our experience. There is a lot of documentation for us to be able to use if we need to. 

I'm not sure if Datadog can monitor K8s deployments in real-time. For instance, being able to see a deployment step by step visually. This would be helpful if there were any incidents during the deployment. 

In general, Datadog is a great solution.

I've used Datadog since I joined my company about a year ago.

We haven't had issues with the stability.

The scalability is really great.

We've had no issues with the product or support. 

The initial setup is super simple, and the documentation was helpful.

We managed the initial setup process in-house.

We've witnessed ROI in our DevOps.

We primarily use Datadog for alerts. If we're running out of database connections or CPU credits we want to find out in Slack. Datadog provides nice features for that.

Secondarily, we use Datadog for analyzing historical trends and forecasting potential issues.

I'm trying to learn how to add in Continuous Profiler in our primary backend servers and set up Synthetic Tests for monitoring our front end.

Everything is mostly on AWS, and the Datadog integrations help a ton.

Datadog has helped us a ton by allowing us to set up a multitude of easily configurable alarms across our tech stack and infrastructure. It doesn't matter if it's in AWS Lambda or a Docker container in AWS EC2, Datadog's intuitive interface makes alarms incredibly easy to configure, reducing our resolution time for incidents.

A lot of the value comes from how frictionless the integrations are. Adding in a Datadog agent or flipping a switch on the Datadog UI to start streaming Lambda data makes the product so incredibly appealing for my company.

The monitoring feature has been the most valuable.

I really like the dashboard. Monitoring has a straightforward tie-in to business value at my company (i.e. declaring incidents, etc). Things like having a dashboard and APM make my job easier. That said DevX is a little bit of a harder sell to executives in my company.

The dashboard feature makes it so easy to inspect multiple metrics at once across services. It's truly been a lifesaver when I'm personally trying to understand why performance degradation is happening.

I found the documentation can sometimes be confusing. I tried configuring APM for some of our Python containers, and I had to cross-reference multiple blog posts and the official documentation to figure out which Datadog-agent to use. If I needed a ddtrace trace, what environment variables I should set, etc. 

Furthermore, to generate my own traces, I wasn't aware that ddtrace adds its own "monkey patching," which led to headaches with respect to configuring the service for RabbitMQ.

A more unified and up-to-date documentation suite would be greatly appreciated.

I've used the solution for about two years.

I don't recall seeing an incident from Datadog in the past couple of years and that's been wonderful.

The solution is incredibly scalable! To be fair, our data throughput to Datadog isn't super huge, however, we have never seen issues as it scaled to handle more of our data.

We used to use AWS Cloudwatch for a lot of our monitoring needs. That said, the interface felt clunky, confusing, and limited.

We don't have hard numbers on ROI. That said, overall, it has been a wonderful addition to our tooling suite.

We also looked at Honeycomb and are currently using both in production.

Datadog provides us with a solution for data ingesting for all of our application metrics, resource metrics, APM/tracing data etc. 

We use it for use in dashboards, monitoring/alerting, SLO targets, incident response etc. 

We have a lot of applications across multiple languages/frameworks etc., and have deployed in Kubernetes across multiple regions in AWS, along with underlying managed resources such as SQS, Aurora, etc. 

Datadog makes understanding the state of these seamless. We are a company with millions of daily active users, and this level of detail is excellent.

Datadog has allowed us to rapidly spin up alerting and monitoring that helps our incident responders get alerted quickly when our SLOs are in danger and helps to quickly resolve issues. 

It is the single most important tool we have from an SRE perspective. 

It also provides us with an easy way to get information at a glance for all of our services through APM and create unified dashboards that track our underlying resources, such as databases, queues, etc., alongside application data. 

It has been invaluable to our organization.

The management of SLOs and their related burn-rate monitors have allowed us to onboard teams to on-call fast. 

Management of resources using infrastructure-as-code has been a recent game-changer for us. Combining the two has allowed us to provide product teams with a total solution for getting their applications attached to user-focused alerting and monitoring within a matter of days rather than months - and has clearly impacted our ability to discover and respond to significant production incidents.

Managing dashboards as IaC is a bit hard to work out at times. I use custom tools to convert JSON dashboards to Terraform resources. Ideally, I'd like for some sort of building tool for this to be built into the app. For example, a templating system that can easily be exported to IaC would be transformative for us. 

There are also some aspects of the API that can be a bit verbose - especially in the area of new features like SLOs - and take some time to understand. That said, overall, they're well-documented enough to be a minor concern for us.

I've been using the solution for over five years.

I have never seen a major outage that prevented us from using Datadog, although I can't speak for other teams/time zones

This product is massively scalable - I haven't seen any issues as we continue to onboard new technologies and teams

Datadog provides us with a number of direct lines to support, although I haven't personally required their assistance.

We previously used LightStep for APM and switched to Datadog to unify all of our application data.

Most elements are quite simple to set up. However, some types of data collection require organization-wide engineering buy-in.

We handled the initial setup in-house.

Our use case is to provide cloud organization application monitoring. I use it for insight into what host in what region has activity or what market is using Datadog to its fullest potential and utilizing that for cost. This may also help determine who is using monitoring and setting alerts or just setting up monitoring and not doing anything about it. The use case can also be to check when the host or applications are down, or if the usage of CPU, memory, etc, is too high.

The solution has improved our organization from a market perspective. We have multiple departments and need some time to gather that data from a grouping point of view. Grouping that data via tag or seeing the separation is easy. In addition, it provides metrics and insights for senior leadership to have a high level of usage and cost. Application teams have better insight into their application, outages, when to plan for patches, updates, etc. Also, they have a better understanding of where the data gaps may be.

The infrastructure is the most valuable. It has a high-level insight into the infrastructure model of the application and provides important detailed data on the host and metrics, which is the main concern of our customers. It provides confirmation that the layer where the application is running is monitored and will be alerted when it is down and not functional. The customers can have ease of mind knowing their metrics are accurately being measured. The value of data provided, including service name, logs, and all other pertinent details tied to the host, makes it a valuable source of data

The solution can be improved via open communication to the broader audience on what has changed and what has not changed. I sometimes log in and see items changed, either in the UI or a feature enabled. To see it for the first time without proper communication can sometimes come as a shock.

I have been using the solution for three years.

The stability is great.

Technical support is great. Datadog has the resources and knowledge to tackle questions.

Positive

I did not previously use a different solution.

The initial setup is straightforward.

The initial setup was handled in-house.

I did not evaluate any other solutions.

The main use cases are to provide visibility to costs for each product in the company as well as to consolidate all the observability in one tool. We are moving the team from being an operational team that needs to keep the tool up and running (applying patches and resolving problems) to a team that is focused on providing meaningful visibility of the systems, applications, and services of the company. We want to add value where the developers and the systems administrators are not able to focus.

The organization changed from having a team to operate different tools and providers to being a team worried about enabling and creating different dashboards, alerts, and automations in order to reduce downtime and increase the visibility of all the products, systems, and applications used. 

We moved from a full operation team to a team that adds value to IT, finance, product, back office, and any other team that requires correct information about the services provided while providing the possibility for them to create their own views and dashboards.

The tags are quite useful. They are providing the capability to give meaning to on-premises hardware (since it was not possible outside of cloud solutions and containers) as well to tag traces and logs. 

The full stack of integrations made it easier to monitor the different technologies and platform providers, including Software as a Service providers, that otherwise would need a lot of work and customization to be able to see what is happening. We'd also need to use several other separate tools that would require an increase in the required staff to operate them. Datadog gave us the opportunity to have a single platform for observability.

The product could be improved by providing remote control to agents, enabling them to execute automation and collections without requiring another automation tool or integration. 

Also, there is a lot of space for the FinOps discipline. For example, it could potentially provide better and richer information for the teams to check the costs and optimize the product.

I've used the solution for one year.

The stability is very good even though we have had some minor problems recently.

The scalability is very good. We've had no problems until now.

Technical support is good. That said, we had some cases that needed to be escalated to get to a faster resolution.

Positive

We previously used AppDynamics. The tool was not providing good system visibility as it was limited and had a very high cost.

The initial setup is somewhat complex. There is a need to create a new automation to install and deploy agents that needs to consider the required security for a financial company.

We handled the implementation in-house.

The ROI is still being calculated.

Users need to be aware of licensing control. With autodiscovery, the product can begin to come at a high cost.

We also looked into Splunk, ELK, and Dynatrace.

We currently use it for log aggregation and SEIM. We send logs from our AWS account (particularly our Cloudtrail and S3 logs) and use them to give us security signals. 

This has helped with our SOC2 certification process and has given us a window into our processes and the security holes in our system. 

We are also considering using the APM features to help with our development effort. We want to be able to profile all of our code and see what is going on with it.

It has allowed us to see into our systems with ease. We are a very small startup (Less than 30 people, and most of them are in sales and marketing). 

When it comes to managing systems, we just don't have time to do everything. However, Datadog has allowed us to do much more with fewer people and still sift through our data with ease. 

We hope to start using the APM feature set to extend this to our dev teams as well.

The ease of use is the primary aspect. I have used, at previous jobs, the ELK stack and Splunk for log management. Both of them were useful, yet required a lot of manual effort to get set up (and a lot of continuing effort to tweak. A simple monitoring solution turned into a full-time job! However, Datadog has so far been a breeze to use and set up. It looks at what I am sending it and figures out what it is almost by magic. Even the manual configuration makes sense and gives very fast and thorough results

One thing we have run into is that it is so easy to add monitoring that we turn on things without really understanding the costs. 

I would like a way to show a continuous indication of what my setup will cost on a daily or weekly basis.

I've used the solution for six months.

We use the solution primarily for platform monitoring for the services that are deployed in AWS. It gives a better way to monitor the services, including pods, cost, high availability, etc. This way, observability is ensured and also customer services are uninterrupted. 

Also, we host the data pipelines between the cloud and the on-prem for which Datadog is used to ensure better services. We report issues based on the metrics reported over it. 

Cost and performance optimization were the major enhancements for our organization. It gives us platform monitoring for the services that are deployed in AWS for a better way to monitor the services (pods, cost, high availability, etc.). With this product, we ensure that observability and also keep customer services uninterrupted. We host the data pipelines between the cloud and the on-prem. Datadog helps to ensure better services. We find we can report issues based on the metrics reported over it.

The observability pipelines are the most valuable aspect of the solution. 

Platform monitoring for the services that are deployed in AWS is helpful. It gives a better way to monitor the services. With Datadog, we ensure observability and maintain uninterrupted customer service. 

We can host the data pipelines between the cloud and the on-prem. Issues are easily reported.

The data streams are good. Data lineage is something that really helped in ensuring tracking of the data and metrics and also the volumes processed.

We'd like to see better transformers.

Live chat would be the best way to support us. 

Also, the features that we saw getting launched recently were something we expected and we're glad to see them coming.  

Geo-data is also something very critical that we hope to see in the future.

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

We primarily use the solution for log management and application performance monitoring. We have been getting into using more solutions on Datadog, such as runbooks, monitoring, and dashboards. 

Another area that we've been investing some time in is the database monitoring. We've been able to get some relatively new employees onboarded into the tool, and they've been able to create some meaningful dashboards and reports without too much hand-holding at all. 

We plan on exploring the synthetics solution as well.

We are still working through fully rolling the service out to our employees. Those that have so far begun using it have found that it decreases the time required to investigate and troubleshoot production issues. 

We have found that we're able to get in and out of troubleshooting issues much more rapidly, which in turn, of course, enables us to spend more time on our products. We are still investigating other areas where other Datadog services could potentially be injected into our workflows.

Correlation between logs and APM has been the most important feature that we've found in Datadog to date. Previous solutions around log collection or APM instrumentation were rather cumbersome to connect. We previously needed to use different solutions for each which were not connected and required complex queries and a lot of time investment by key employees.

The search and filtering capabilities are rather helpful as well. The aggregation of all currently available properties has been great. It's excellent that available options drop as filters are refined. This allows for a nuanced view of available data.

We intend on exploring other products at Datadog, so this list may expand.

I've found that the documentation is lacking in certain regards. In going through sessions around certain services, the presenter expressed opinions on best practices that are not covered by documented examples. 

In taking these thoughts to the "experts," further research is required both by us and those working the table to come to a solution that meets our needs. If there were more documentation on best practices this may be easier to manage.

I've been using the solution for ten years. 

The solution overall seems rather stable.

The solution seems scalable. We just need to keep an eye on the costs as it scales.

Customer support has been ok, yet not great. We've had ticket resolution drag on for weeks.

Neutral

We previously used Scalyr for logs and switched due to APM linkage.

The initial setup was straightforward.

We handled hte setup in-house.

We've saved many developer hours by using Datadog. We plan on expanding our investment in this solution (and thus our return).

Pricing can be a bit of a sell internally. We've found it to be worth it, though.

We came from using other solutions.