Datadog Reviews

We're using the product for logging and monitoring of various services in production environments. 

It excels at providing real-time observability across a wide range of metrics, logs, and traces, making it ideal for DevOps teams and enterprises managing complex environments. 

The platform integrates seamlessly with our cloud services, but browser side logging is a little lagging. 

Dashboards are very useful for quick insights, but can be time consuming to create, and the learning curve is steep. Documentation is vast, but not as detailed as I'd like.

The solution has made logging and tracing a lot easier, and the RUM sessions are something we did not have previously. Datadog’s real-time alerting and anomaly detection help reduce downtime by allowing us to identify and address performance issues quickly. 

The platform’s intelligent alert system minimises noise, ensuring your team focuses on critical incidents. This results in faster Mean Time to Resolution (MTTR), improving service availability. 

It consolidates monitoring for infrastructure, applications, logs, and security into a single platform. This enables us to view and analyse data across the entire stack in one place, reducing the time spent jumping between tools.

Real user monitoring has made triaging any possible bugs our users might face a lot easier. RUM tracks actual user interactions, including page load times, clicks, and navigation flows. This gives our organization a clear picture of how our users are experiencing your application in real-world conditions, including slow-loading pages, errors, and other performance issues that affect user satisfaction. We can then easily prioritize these, and make sure we offer our users the best possible experience.

I'm not sure if this is on Datadog, however, Vercel integration is very limited. 

They need to offer better/more customization on what logs we get and making tracing possible on Edge runtime logs is a real requirement. It is extremely difficult, if not completely impossible, to get working traces and logs displayed in Datadog with our stack of Vercel, NexJs, and Datadog. This is a very common stack in front end development and the difficulty of implementing it is unacceptable. Please do something about it soon. Front end logs matter.

I've used the solution for a little over a year.

Our primary use cases include:

• Alert on errors customers encounter in our product. We've set up logs that go to slack to tell us when a certain error threshold is hit.
• Investigate slow page load times. We have pages in our app that are loading slowly and the logs help us figure out which queries are taking the longest time.
• Metrics. We collect metrics on product usage.
• Session replays. We watch session replays to see what a user was doing when a page took a long time to load or hit an error. This is helpful.
  

It's helped us find bugs that customers are experiencing before they're reported to us. Sometimes, customers don't report errors, so being able to catch errors before they're reported helps us investigate before other users find errors

Datadog has helped us investigate slow page loading times and even see the specific queries that are taking a long time to load

Logging lets us see the context around an error. For example, see if a backend service had an error before it surfaced on the frontend.

Dashboards are helpful for reviewing occasionally to get a higher-level overview of what's happening.

The most valuable aspects include: 

• Logging. Being able to view detailed logs helps debug issues.
• Session replays. They are helpful for seeing what a customer was doing before they saw an error or had a slow page load
• Alerting. This is an important part of our on-call process to send alerts to slack when an error threshold is crossed. Alerts/monitors are easy to configure to only alert when we want them to alert.
• Dashboards. It's helpful to pull up dashboards that show our most common errors or page performance. It's a good way to see how the app is performing from a birds-eye-view.

The UI has a lot going on. It should be simpler and have a better way to onboard someone new to using Datadog.

The log querying syntax can be confusing. Usually, I filter by finding a facet in a log and selecting to filter by that facet - but I'm not sure how to write the filter myself

The monitor/alert syntax is also somewhat hard to understand.

Overall, it should be easier to learn how to use the product while you're using the product. Perhaps tooltips or a link to learn more about whatever section you're using.

I've used the solution for two years.

We did not previously use a different solution.

We did not evaluate other options. 

We use Datadog as our main log ingestion source, and Datadog is one of the first places we go to for analyzing logs. 

This is especially true for cases of debugging, monitoring, and alerting on errors and incidents, as we use traffic logs from K8s, Amazon Web Services, and many other services at our company to Datadog. In addition, many products and teams at our company have dashboards for monitoring statistics (sometimes based on these logs directly, other times we set queries for these metrics) to alert us if there are any errors or health issues.

Overall, at my company, Datadog has made it easy to search for and look up logs at an impressively quick search rate over a large amount of logs. 

It seamlessly allows you to set up monitoring and alerting directly from log queries which is convenient and helps for a good user experience, and while there is a bit of a learning curve, given enough time a majority of my company now uses Datadog as the first place to check when there are errors or bugs. 

However, the cost aspect of Datadog is tricky to gauge because it's related to usage, and thus, it is hard to tell the relative value of Datadog year to year.

The feature I've found most valuable is the log search feature. It's set up with our ingestion to be a quick one-stop shop, is reliable and quick, and seamlessly integrates into building custom monitors and alerts based on log volume and timeframes. 

As a result, it's easy to leverage this to triage bugs and errors, since we can pinpoint the logs around the time that they occur and get metadata/context around the issue. This is the main feature that I use the most in my workflow with Datadog to help debug and triage issues.

More helpful log search keywords/tips would be helpful in improving Datadog's log dashboard. I recently struggled a lot to parse text from raw line logs that didn't seem to match directly with facets. There should be smart searching capabilities. However, it's not intuitive to learn how to leverage them, and instead had to resort to a Python script to do some simple regex parsing (I was trying to parse "file:folder/*/*" from the logs and yet didn't seem to be able to do this in Datadog, maybe I'm just not familiar enough with the logs but didn't seem to easily find resources on how to do this either). 

I've used the solution for 10 months.

Beware that the cost will fluctuate (and it often only gets more expensive very quickly).

The soluton is used for full stack enterprise performance monitoring for our primarily cloud-based stack on AWS. We have implemented monitoring coverage using RUM for critical apps and websites and utilize APM (integrated with RUM) for full stack traceability.  

We use Datadog as our primary log repository for all apps and platforms, and the advanced log analytics enable accurate log-based monitoring/alerting and investigations. 

Additionally, we some advanced RUM capabilities and metrics to track and optimize client-side user experience. We track SLO's for our critical apps and platforms using Datadog.

We now have full-stack observability, which allows us to better understand application behavior, quickly alert users about issues, and proactively manage application performance.  

We've seen value by implementing observability coordinated across multiple applications, allowing us to track things like customer shopping and orders across multiple applications and services.  

For critical application launches, we've built dashboards that can track user activity and confirm users are able to successfully utilize new features, tracking user activities in real-time in a war-room situation.  

Datadog is our go-to tool for analyzing, understanding, and investigating application performance and behavior.

APM accurately tracks our service performance across our ecosystem. RUM gives us client-side performance and user experience visibility, and the rate of new features implemented in the Digital Experience area recently has been high. Log analytics give us a powerful mechanism for error tracking, research, and analysis.  

Custom metrics that we've created allow us to track KPIs in real-time on dashboards. All of these have proven valuable in our organization.  Additionally, Datadog product support teams are responsive and have provided timely support when needed.

Agent remote configuration should be provided/improved and streamlined, allowing for config changes/upgrades to be performed via the portal instead of at the host.   

Cost tracking via the admin portal is a bit lacking, even though it has gotten better.  I'm looking for usage trends (that drive cost) across time and better visibility or notifications about on-demand charges.  

Network device and performance monitoring could be improved, as we've faced some limitations in this area.  

The Datadog usage-based cost model, while giving us better transparency, is difficult to follow at times and is constantly evolving.  

I've used the solution for three years.

Support has been responsive and helpful.  

Positive

Pricing is straightforward. That said, it's sometimes difficult to estimate usage volumes.

We evaluated Datadog and New Relic in detail and chose Datadog due to their straightforward and competitive pricing model, and their full coverage of monitoring features that we desired, and an easy-to-use UI.  

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.

Our company deploys the solution for our customers as an observability tool to define SLOs and SLIs along with logs and metrics. 

The solution includes incident, post-mortem, and root cause analysis that provides a level of truth for incidents and issues with applications. 

We have SREs and teams in operations, management, and applications who all access to the solution and ensure proper integrations. 

Our company is adopting SRE practices and the solution helps us to align the practices with our site reliability. We get more insights about issues at the outset which helps us to make better decisions such as continuing with agility or stopping to fix issues. 

We are at the beginning stages of using the solution but are defining it as our company standard for use by all teams. 

The solution allows flexibility and heightened observability for presenting data, creating indicators, and setting service-level objectives. There are interesting options for monitors and features that offer flexible ways to analyze data.

The solution should provide alerts for cloud outages that would allow us to report potential service impacts directly to applications or on the dashboard. Alerts are important because there is a need to determine the impact on your SLAs, SLOs, and SLIs to decide whether to move toward disaster recovery or another environment. 

I would like the ability to share dashboard screenshots via email rather than having to direct others to the dashboard because it sometimes requires permissions. 

I have been using the solution for one year. 

Our SRE teams report that the solution is very solid, stable, and reliable. 

We are using the SaaS model so do not manage scalability because the product takes care of our scaling needs with no issues. 

I don't have direct access with support but our SRE teams work with them and are satisfied. 

Our company used other products in the past but wanted to move to the cloud. We found that the solution was a very good fit for us. 

The initial setup is not that easy because there are many choices for configuration, workloads, servers, and containers. 

We utilized technical support to help us understand integration and prepare patterns for other applications. 

We created small configurations and then utilized technical support to configure an application selected from our portfolio. 

We utilize a team approach for implementations that sometimes includes SREs. 

The solution is fairly priced but history and log storage can get costly depending on your needs. 

I rate the cost a four out of ten. 

The solution is appropriate for companies that are moving to the cloud and want a very solid tool for observability, logging, and everything related to SRE practices. 

I rate the solution a nine out of ten. 

I work in product design, and although we use Datadog for monitoring, etc, my use case is different as I mostly review and watch session recordings from users to gain insight into user feedback.

We watch multiple sessions per week to understand how users are using our product. From this data, we are able to hone in on specific problems that come up during the sessions. We then reach out to specific users to follow up with them via moderated testing sessions, which is very valuable for us.

Using Datadog has allowed us to review detailed interactions of users at a scale that leads us to make informed data-driven UX improvements as mentioned above.

Being able to pinpoint specific users via filtering is also very useful as it means when we have direct feedback from a specific user, we can follow up by watching their session back. 

The engineering team's use case for Datadog is for alerting, which is also very useful for us as it gives us visibility of how stable our platform is in various different lenses.

Session recordings have been the most valuable to me as it helps me gain insights into user behaviour at scale. By capturing real-time interactions, such as clicks, scrolls, and navigation paths, we can identify patterns and trends across a large user base. This helps us pinpoint usability issues, optimize the user experience, and improve the overall experience for our users. Analyzing these recordings enables us to make data-driven decisions that enhance both functionality and user satisfaction.

I'd like the ability to see more in-depth actions on user sessions, such as where there are specific problems and rather than having to watch numerous session recordings to understand where this happens to get alerts/notifications of specific areas that users are struggling with - such as rage clicks, etc.

In terms of UI, everything is very small, which makes it quite difficult to navigate at times, especially in terms of accessibility, so I'd love for there to be more attention on this.

I've used the solution for over one year.

We did not evaluate other options. 

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

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