Datadog Reviews

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.

We use it to monitor and alert our ECS instances as well as other AWS services, including DynamoDB, API Gateway, etc. 

We have it connected to Pagerduty for alerting all our cloud applications. 

We also use custom RUM monitoring and synthetic tests for both our internal and public-facing websites. 

For our cloud applications, we can use Datadog to define our SLOs, and SLIs and generate dashboards that are used to monitor SLOs and report them to our senior leadership.

Datadog has been able to improve our cloud-native monitoring significantly, as CloudWatch doesn't have enough features to create robust, sustainable dashboards that are easily able to present all the information in an aggregated manner in one place for a combination of applications, databases, and other services including our UI applications. 

RUM monitoring is also something we didn't have before Datadog. We had Splunk, which was a lot harder to set up than Datadog's custom RUM metrics and its dashboards.

I really enjoy the RUM monitoring features of Datadog. It allows us to monitor user behavior in a way we couldn't before. 

It's useful to be able to obfuscate sensitive information by setting up custom RUM actions and blocking the default ones with too much data. 

I also like being able to generate custom metrics and monitors by adding facets to existing logging. Datadog can parse logs well for that purpose. The primary method of error detection for our external website is synthetic tests. This is extremely valuable for us as we have a large user base.

At times, it can be hard to generate metrics out of logs. I've seen some of those break over time and have flakey data available. 

Creating a monitor out of the metric and using it in a dashboard to generate our SLIs and SLOs has been hard, especially in cases where the data comes from nested logging facets.

I've used the solution for two years.

The stability is pretty good.

The solution is pretty scalable! It's hard to set up all the infra (terraform code) required to link private links in Datadog to all of our different AWS accounts.

They offer good support. Solutions are provided by the team when needed. For example, we had to delete all our RUM metrics when we accidentally logged sensitive data and the CTO of Datadog stepped in to help out and prioritize it at the time.

Positive

We previously used Splunk and some internal tools. We switched due to the fact that some cloud applications don't integrate well with pre-existing solutions.

The initial setup for connecting our different AWS accounts via Datadog private link wasn't great. There was a lot of duplicate terraform that had to be written. The dashboard setup is way easier.

We installed it with the help of a vendor team.

Our return on investment is great and is so much better than CloudWatch. We can easily integrate with Pagerduty for alerting.

Our company set up the product for us, so the engineers didn't need to be involved with pricing. 

The pricing structure isn't very clear to engineers.

We looked into Splunk and some internal tools.

We use the product for recording loggers on our various services across different teams. For example, we use logs to keep track of info logs for events and error logs to catch exceptions. 

When users ask us to investigate a situation, we use logs to keep track of events and where the user's code traveled to. We also use synthetic testing and monitoring features to keep track of our many alerts in the production and QA environments.

We use Datadog mainly for debugging purposes. For example, we use it to navigate where the code trace is when an issue arises due to its ability to search through the logs. 

We also use it to address user queries. Sometimes users would ask us a certain question concerning our codebase, we use Datadog to track the code stack and also use time monitoring to get an idea of the time frame around when the use case happened.

The feature I have found to be the most valuable is the filtering feature in logs. It is really easy to type plus and minus to filter out different logs. I use it to navigate the noise. 

I use synthetic tests as well. It is easy to navigate the menu and create tests. 

Much of the UI is very straightforward, and I do appreciate the ability to search for any documentation on the various features when I need to as well. The DASH monitoring boards are nice to give an overview of various performances and allow us to track use cases.

This service could be less costly. Right now, we only keep 15 days worth of logs since we want to be more economical in terms of cost. It would be nice if I had the option to monitor logs beyond 15 days. For APM traces, we only keep a year worth of traces. The UI can be a little more straightforward as well. I found it to have too many options.

I've used the solution for three years.

The stability is good.

The scalability is good.

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 Datadog to view and aggregate logs and monitor all of our services. We have a lot of running infrastructure and it is very convenient to have logs and metrics all aggregated somewhere we can view and chart them. 

I use Datadog to create dashboards and runbooks, and sharable graphs, which really help out my whole team. We mostly use logs and APM, yet have been starting to use other products. I would like to use more synthetic monitors.

It has democratized our logs and metrics, allowing all engineers to have insight into how our apps perform. It is also extremely helpful when debugging issues. 

It would be very difficult to debug issues without aggregated logs and APM traces. 

It has also definitely saved us some money since we can keep an eye on our running infrastructure in an easy-to-see way, rather than a less friendly CLI. It has been a very big help!

The log stream has been the most useful thing. Having so many logs on so many different running containers means it is very inconvenient to view them individually. Datadog's log aggregation is really helpful since it lets me and every other engineer on my team login, view, and share logs when we need to debug our application. 

APM has also been extremely helpful for debugging issues and profiling and optimizing our apps. Dashboards have also been really helpful for communicating needs and priorities to engineering leadership. 

It is very easy to get buy-in with graphs to back things up.

I recently saw the education, and it is amazing. Events like DASH are extremely helpful in understanding the deep set of features. Anything that helps to educate users is a huge win here. 

The menu on the left is pretty dense (and I know it has to be). I never knew about the cmd+k functionality until recently. It would be helpful to offer more tips/cheat sheets to see handy shortcuts like that.

I've used the solution for three years. 

We previously used AWS Cloudwatch logs. It was way less friendly and fully featured.

The solution is pretty straightforward to set up. It helps with logs and metrics, and the AWS integration is really great.

We handled the implementation in-house.

It is hard to educate an entire team. There is a big learning curve.

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.

We are trying to get a handle on observability. Currently, the overall health of the stack is very anecdotal. Users are reporting issues, and Kubernetes pods are going down. We need to be more scientific and be able to catch problems early and fix them faster.

Given the fact that we are a new company, our user base is relatively small, yet growing very fast. We need to predict usage growth better and identify problem implementations that could cause a bottleneck. Our relatively small size has allowed us to be somewhat complacent with performance monitoring. However, we need to have that visibility.

We are still taking baby steps with Datadog. Hence, it's hard to come up with quantifiable information. The most immediate benefit is aggregating performance metrics together with log information. Having a better understanding of observability will help my team focus on the business problems they are trying solve and write code that is conducive to being monitored, instead of reinventing the wheel and relying on their own logic to produce metrics that are out of context

The most useful feature is the APM. Being able to quickly view which requests are time-consuming, and which calls have failed is invaluable. Being able to click on a UI and be pointed to the exact source of the problem is like magic. 

I'm also very intrigued by log management, although I haven't had quite a chance to use it very effectively. In particular, the trace and span IDs don't quite seem to work for me. However, I'm very keen on getting this to work. This will also help my developers to be more diligent and considerate when creating log data.

As a new customer, the Datadog user interface is a bit daunting. It gets easier once one has had a chance to get acquainted with it, yet at first, it is somewhat overwhelming. Maybe having a "lite" interface with basic features would make it easier to climb the learning curve.

Maybe the feature already exists. However, I'm not sure how to keep dashboard designs and synthetic tests in source control. For example, we may replace a UI feature, and rebuild a test accordingly in a pre-production environment, yet once the code is promoted to production, the updated test would also need to be promoted.

We have just started using the solution and have only used it for about two months.

We're new at this. That said, so far, there haven't been any issues to report.

I have not had the opportunity to evaluate the scalability.

Customer support is full of great folks! We're beginning our Datadog journey, so I haven't had that much experience. The little I have had has been great.

Positive

This is all new. 

We used to work with New Relic. New Relic has an amazing APM solution. However, it also became cost-prohibitive

Since we are relatively greenfield, it was relatively painless to set up the product. 

Our in-house DevOps team did the implementation.

I don't know what the ROI is at this stage.

I'm not sure what the exact pricing is. 

So far, it's been great!