Datadog Reviews

Our primary use case is custom and vendor-supplied web application log aggregation, performance tracing and alerting. 

Through the use of Datadog across all of our apps, we were able to consolidate a number of alerting and error-tracking apps, and Datadog ties them all together in cohesive dashboards. 

The centralized pipeline tracking and error logging provide a comprehensive view of our development and deployment processes, making it much easier to identify and resolve issues quickly. 

Synthetic testing is great, allowing us to catch potential problems before they impact real users. Real user monitoring gives us invaluable insights into actual user experiences, helping us prioritize improvements where they matter most. And the ability to create custom dashboards has been incredibly useful, allowing us to visualize key metrics and KPIs in a way that makes sense for different teams and stakeholders. 

While the documentation is very good, there are areas that need a lot of focus to pick up on the key details. In some cases the screenshots don't match the text when updates are made. 

I spent longer than I should trying to figure out how to correlate logs to traces, mostly related to environmental variables.

I've used the solution for about three years.

We have been impressed with the uptime.

It's scalable and customizable. 

Support is helpful. They help us tune our committed costs and alert us when we start spending out of the on-demand budget.

We used a mix of SolarWinds, UptimeRobot, and GitHub actions. We switched to find one platform that could give deep app visibility.

Setup is generally simple. .NET Profiling of IIS and aligning logs to traces and profiles was a challenge.

We implemented the solution in-house.

There has been significant time saved by the development team in terms of assessing bugs and performance issues.

I'd advise others to set up live trials to asses cost scaling. Small decisions around how monitors are used can have big impacts on cost scaling. 

NewRelic was considered. LogicMonitor was chosen over Datadog for our network and campus server management use cases.

We are excited to dig further into the new offerings around LLM and continue to grow our footprint in Datadog. 

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.

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 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.

Positive

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.

We primarily use the solution for centralized dashboarding and telemetry viewing for teams across the organization. 

We're focused on ensuring that both development teams and leadership can reasonably gain insights into the status of various systems. 

At the end of the day, managing various dashboards and metrics aggregators like Prometheus, Kubernetes server, AWS Cloudwatch, and Grafana have lead to some confusion, and we've had issues with teams not knowing where their data exists and where they can view their system metrics. 

Datadog has proven to be easy to set up and legible for both development and operational teams.

The solution has been useful in generally ensuring that teams are able to better visualize and think about their application's impact on data centers/cloud performance. Having centralized tooling for observability means that each team can be on the same page when discussing monitoring. 

There have been some issues where teams have been unable to find metrics within the tool properly and some behaviors with the tagging and grouping functionality that seem not to be as easy to understand as one may expect. That said, overall, the experience has been one that is positive.

The dashboards have proven most helpful in ensuring that teams can track the performance of their apps. On a more practical scale, the alerts have proved invaluable for triaging and bringing services back online.

Being able to tie the alerts generated through Datadog monitors has allowed us to quickly and effectively respond to infrastructure and software issues that would have otherwise hamstrung the organization and prevented us from accomplishing our day-to-day tasks. This is naturally invaluable.

I'm sure that this is said all the time, however, the pricing model has led us to restrict the usage of the service. If there were a more cost-effective manner of deploying the tool, we'd be more likely to adopt it more widely. 

Aside from the cost, the nature of the tagging and grouping features within the monitoring dashboards have often caused headaches when creating new dashboards for aggregate services and infrastructure stacks. It would be nice to ensure that this feature is supported long-term and brought with easier accessibility.

I've been using the solution for three years.

Datadog is easy to use and generally looks great from a customer standpoint. The ability to export metrics all into a central location was crucial.

Datadog is very expensive for smaller organizations. The pricing model might be restrictive until the organization reaches a certain size.

Primarily we did an evaluation of other providers, such as AWS and GCP, outside of in-house solutions.

We mainly use the product to monitor our infrastructure and apps. It is the go-to tool when we want to check that things are running properly. We use Datadog synthetic monitors to ensure our app works across different locations in the United States. 

We also have set up Datadog monitors to send alerts if things stop working as expected. 

We use Continuous Integration Pipeline visibility to make sure our developers are not being blocked by infrastructure and other things that might be out of their control.

Datadog helps us detect issues early on and helps in troubleshooting. Creating Service Level Objectives and defining monitors is helping us to stay on top of potential issues that might affect our users. 

We take advantage of Application Performance Monitoring to ensure our applications are working as expected, and our users can get the healthcare they need at a price they can afford. 

Synthetic monitoring also helps us in testing our application in different browsers.

The most valuable aspects of the solution include: 

CI visibility, which helps us in making sure our CI systems are running efficiently and are not blocking our developers from releasing new software and fixing bugs.

Logs, which help us in debugging issues where we can search for logs and can make sure they are relevant to the issues we are looking at.

APM, which can help us to stay on top of our applications by giving us the confidence that our apps are running.

Monitoring. We use monitoring a lot to ensure we know about potential issues and fix them before they affect our customers.

Overall, we really like the quality and relevance of all of the Datadog products that are currently being used. 

The documentation is very well organized and is the go-to place for us to find answers to our questions. 

We would really like to see more from the Service Catalog. It is something that we are interested in. However, some might think it lacks some key features at this time. We will definitely keep our eye out for this and adopt it when all the features are implemented. 

We're really looking forward to all the great things DD will do.

I've used the solution for three years.

The stability is great.

The scalability is great.

Technical support is great.

We handled the initial setup in-house.

I don't have any insights into pricing.

We primarily use the solution for application monitoring (APM, logs, metrics, alerts).

It's useful for active monitoring (static monitors, threshold monitors). We get a lot of value out of anomaly detection as well. SLOs and monitoring of SLOs have been another value add.

In terms of metrics, the out-of-the-box infrastructure metrics that come with the Datadog agent installation are great. We have made use of both the custom metrics implementation as well as the log-based metrics which are extremely convenient.

We also leverage Datadog for use of RUM and want to explore session replay.

It is easy to implement and scale applications with standardized visibility, monitoring and alerting

We get a lot of value out of passive and active monitoring. While different teams across our organization have used different services (metrics, logs, APM, RUM), almost all teams have been able to use the dashboards to report and track high-level metrics and active monitoring. 

Active monitoring (static monitors, threshold monitors) is great. We get a lot of value out of anomaly detection as well. SLOs and monitoring of SLOs have been another value add for our organization.

The APM and tracing provide visibility and the ability to get right to root cause issues while being able to deploy new services without much need for custom instrumentation quickly

The active monitoring (static monitors, threshold monitors) has been very helpful. We get a lot of value out of anomaly detection. SLOs and monitoring of SLOs have been extremely valuable.

The metrics and out-of-the-box infrastructure metrics that come with the Datadog agent installation are quite helpful to the organization. We have made use of both the custom metric implementation as well as the log-based metrics which are extremely convenient.

Datadog is so feature-rich that it is often hard to onboard new folks and tough to decide where to invest time. 

The APM is a perfect example of this. This feature alone has so much (profiling, tracing, span summary, flame graphs). I would love to see more of the insight and automation-focused features, such as the log patterns, where I can spend time more efficiently.

The cost of Datadog at scale can get very expensive very quickly. I would like to see a better usage/cost dashboard with breakdowns like the AWS cost explorer.

I've used the solution for three years.

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.

I have been using Datadog products and capabilities increasingly over the last 4 years, from POC to widespread adoption. 

The capabilities we use are unique for each use case and can be combined in various ways to provide the full observability coverage needed to maintain stable operations and shift from becoming more reactive to proactive. 

Our organization uses both site/service reliability for the range of backend and frontend services, custom monitoring, and dashboards that can be dynamic and reused for multiple teams.

The capabilities we use are unique for each use case. They can be combined in various ways to provide the full observability coverage needed to maintain stable operations in order to become more proactive. 

Our organization uses both site/service reliability for backend and frontend services. Custom monitoring and dashboards that can be dynamic and reused for multiple teams. 

We continue to increase the size of our footprint as we get more and more positive experiences.

The APM, RUM, synthetics, and infrastructure host maps have been some of the most popular and commonly used features. 

Overall, the Data UI and the usability of customer features continue to improve. 

The RUM session data and replays are much more convenient and applicable than other tools I have worked with in the past, and by combining multiple capabilities or features together, there is full visibility across the technology stacks and can identify specific bottlenecks or areas for risk and vulnerabilities to be likely to exist. 

Watchdog insights take the work out of the hardest part, helping us identify the issues before our customers.

It is very difficult to make the solutions fit perfectly for large organizations, especially in terms of high cardinality objects and multi-tenancy, where the data needs to be rolled up to a summarized level while maintaining its individual data granularity and identifiers. Tagging is imperative. However, the solutions could be improved for these needs in the future.

I've used the solution for over four years now.

The stability is excellent.

You can work with engineering to make it work for your needs. They are excellent at supporting their customers.

Technical support is excellent.

Neutral

I previously used New Relic, App Dynamics, Heap, Clicktale, and more. Datadog has incorporated many of the features we were looking for into a one-stop shop.

The initial setup is simple and straightforward.

We had an in-house team working directly with Datadog engineering support and technical enablement.

We looked into New Relic, App Dynamics, Heap, Clicktale, and more. Datadog has many of the features we were looking for in one place.

We use all versions of the solution.

We primarily use Datadog for:

• Native memory
• Logging
• APM
• Context switching
• RUM
• Synthetic
• Databases
• Java
• JVM settings
• File i/o
• Socket i/o
• Linux
• Kubernetes
• Kafka
• Pods
• Sizing

We are testing Datadog as a way to reduce our operational time to fix things (mean time to repair). This is step one. We hope to use Datadog as a way to be proactive instead of reactive (mean time to failure).

So far, Datadog has shown very good options to work on all of our operational and development issues. We are also trying to use Datadog to shift left, and fix things before they break (MTTF increase).

We are currently in a POC and do not own Datadog at the moment. 

So far, there have been a few issues due to security. There are two main security issues. 

The first is moving data off-prem. This has been resolved to a point (filtering logs, etc). However, there is still an issue with moving a JFR as a JFR potentially contains data that is not allowed off-prem.

The second security issue is more internal, however, the main installation requires root access or using an ACL. Our company does not use ACLs on our Linux platform. This is problematic since the install sets a no-login on the Datadog user.

The most valuable aspects of the product include the APM and profiler.

These two have given us insights into things that are very difficult to track down given the standard OS (Linux) tools. 

The native memory tracking is super difficult to see exactly where it comes from. I attended a course (continuous profiling), and it showed me the potentially very important capabilities.

If you add these details to a standard dashboard, or a sub-dashboard for techy people, or even just a notebook, it would be easy to identify issues before they occur.

Combining these details with the basic tools (infra, logging, APM, and good rules), Datadog can easily show the details that a true engineer would need. It isn't just for monitoring, however, I see the value in it for engineers.

I have done every training offered (and in a short period of time: two days for 20 courses).

I find the training great. That said, it is set for the LCD (lowest common denominator). Of course, this is very helpful to sell the product, yet, to really utilize the product, you need to get more detailed.

If I did the training as it is written and I cut/paste a bunch of stuff and see the cut/paste work, I didn't really learn anything. Later sessions (I quit using the editor and switched to VI) stopped cutting and pasting, and learned much more.

I've used the solution for one month.

I' give stability a thumbs up.

We are not sure yet in terms of scalability. The off-prem solution seems to scale well (although had issues with the training slowing down).

Technical support is great.

Positive

I previously used Dynatrace and Elastic. We didn't switch. We are in a POC.

The initial setup is simple yet complex. There are too many teams are needed.

We did the initial setup in-house.

In terms of ROI, the labor saving is probably the biggest. The NPR is probably second - although management would probably reverse these.

Pricing and licensing is fairly complicated. A GB for .1 sounds great, however, once you put all 16 or so prices together, it adds up fast. A cost model sheet on the main site would be very helpful.

We are currently in a POC.

We work with all product versions.