Datadog Reviews

We use the solution for APM, AWS, Lambda, logging, and infrastructure. We have many different things all over AWS, and having one place to look is great.

We have all sorts of different AWS things out there that are in C# and Node. Having a single place to log and APM into is very important to us.

Keeping track of the cloud infrastructure is also important. We have Lambda, containers, EC2, etc.

Having a super simple interface to filter the searching for APM and logging is great. It is super easy to show people how to use. This is super important to us.

Finding issues quickly is super important. Being able to create dashboards and alert on issues.

Having the ability to create dashboards has really taught us how to utilize the searching part of the system. We are able to share them, and build upon them so easily. Many iterations later people are putting some solid information out there.

Alerting is also important to us. We have set up many alerts that help us spot issues in the platform before they become bigger issues. This has enabled my teams to use incidents and address the issues so they are no longer problems.

Alerting on running systems is very helpful. Finding issues is quick. We have one place for logging, searching through. Being able to save these and reference them in the future and build upon them.

The logging in general is one of my favorite features. The search is so straight forward and easy to use. Just being able to click on a field and add it to search has taught me so much about the interface, It might not be as useful without a shortcut like that to teach me the system. We have Cloudflare logs in there, and I have no idea sometimes how to filter on such a buried piece of JSON. That is where the interface helps me by clicking on the add to search I get what I need.

The "Pager Duty" replacement is something we are very interested in. We only really use pager duty to call the team when things are down.

I love to have some DD guru come in and do a department training directly at our setup. We would love to have someone come in and show us the things we could do better within our current setup.

Also saving a bit of cash would also help if there are things we are doing that are costing us. It's a big enough tool that it is tough to have someone dedicated to manage. 

I've used the solution for a bit over a year at this point.

The stability seems good here too.

Scalability seems good to me. I have no complaints

I get answers from our contact, and one team member did reach out. It went well.

We used Loggly. 

We switched because we wanted an all-in-one tool

Some parts of our setup were tough. Some Windows container setups cost us a lot of time.

The AWS infrastructure was tough to fully turn on due to the large cost of everything being run.

We handled the setup ourselves in-house.

This cost us more overall. ROI is hard to sell. That said, I can find issues way faster and see what is going on in my entire platform. I pay back the cost every month with productivity. 

It is going to cost you more than you think to keep everything running. We saw value in the one-for-all solution, however, it came at a premium to what we were paying. 

We did evaluate Dynatrace.

Our primary use case for this solution is comprehensive cloud monitoring across our entire infrastructure and application stack. 

We operate in a multi-cloud environment, utilizing services from AWS, Azure, and Google Cloud Platform. 

Our applications are predominantly containerized and run on Kubernetes clusters. We have a microservices architecture with dozens of services communicating via REST APIs and message queues. 

The solution helps us monitor the performance, availability, and resource utilization of our cloud resources, databases, application servers, and front-end applications. 

It's essential for maintaining high availability, optimizing costs, and ensuring a smooth user experience for our global customer base. We particularly rely on it for real-time monitoring, alerting, and troubleshooting of production issues.

Datadog has significantly improved our organization by providing us with great visibility across the entire application stack. This enhanced observability has allowed us to detect and resolve issues faster, often before they impact our end-users. 

The unified platform has streamlined our monitoring processes, replacing several disparate tools we previously used. This consolidation has improved team collaboration and reduced context-switching for our DevOps engineers. 

The customizable dashboards have made it easier to share relevant metrics with different stakeholders, from developers to C-level executives. We've seen a marked decrease in our mean time to resolution (MTTR) for incidents, and the historical data has been invaluable for capacity planning and performance optimization. 

Additionally, the AI-driven insights have helped us proactively identify potential issues and optimize our infrastructure costs.

We've found the Application Performance Monitoring (APM) feature to be the most valuable, as it provides great visibility on trace-level data. This granular insight allows us to pinpoint performance bottlenecks and optimize our code more effectively. 

The distributed tracing capability has been particularly useful in our microservices environment, helping us understand the flow of requests across different services and identify latency issues. 

Additionally, the log management and analytics features have greatly improved our ability to troubleshoot issues by correlating logs with metrics and traces. 

The infrastructure monitoring capabilities, especially for our Kubernetes clusters, have helped us optimize resource allocation and reduce costs.

While Datadog is an excellent monitoring solution, it could be improved by building more features to replace alerting apps like OpsGenie and PagerDuty. Specifically, we'd like to see more advanced incident management capabilities integrated directly into the platform. This could include features like sophisticated on-call scheduling, escalation policies, and incident response workflows. 

Additionally, we'd appreciate more customizable machine learning-driven anomaly detection to help us identify unusual patterns more accurately. Improved support for serverless architectures, particularly for monitoring and tracing AWS Lambda functions, would be beneficial. 

Enhanced security monitoring and threat detection capabilities would also be valuable, potentially reducing our reliance on separate security information and event management (SIEM) tools.

I've used the solution for two years.

We currently have an error monitor to monitor errors on our prod environment.  Once we hit a certain threshold, we get an alert on Slack. This helps address issues the moment they happen before our users notice. 

We also utilize synthetic tests on many pages on our site. They're easy to set up and are great for pinpointing when a bug is shipped, but they may take down a less visited page that we aren't immediately aware of. It's a great extra check to make sure the code we ship is free of bugs.

The synthetic tests have been invaluable. We use them to check various pages and ensure functionality across multiple areas. Furthermore, our error monitoring alerts have been crucial in letting us know of problems the moment they pop up.  

Datadog has been a great tool, and all of our teams utilize many of its features.  We have regular mob sessions where we look at our Datadog error logs and see what we can address as a team. It's been great at providing more insight into our users and logging errors that can be fixed.

The error logs have been super helpful in breaking down issues affecting our users. Our monitors let us know once we hit a certain threshold as well, which is good for momentary blips and issues with third-party providers or rollouts that we have in the works. Just last week, we had a roll-out where various features were broken due to a change in our backend API. Our Datadog logs instantly notified us of the issues, and we could troubleshoot everything much more easily than just testing blind. This was crucial to a successful rollout.

I honestly can't think of anything that can be improved. We've started using more and more features from our Datadog account and are really grateful for all of the different ways we can track and monitor our site. 

We did have an issue where a synthetic test was set up before the holiday break, and we were quickly charged a great amount. Our team worked with Datadog, and they were able to help us out since it was inadvertent on our end and was a user error. That was greatly appreciated and something that helped start our relationship with the Datadog team.

We've been using Datadog for several months. We started with the synthetic tests and now use It for error handling and in many other ways.

Stability has been great. We've had no issues so far.

The solution is very easy to scale. We've used it on multiple clients.

We had a dev who had set up a synthetic test that was running every five minutes in every single region over the holiday break last year. The Datadog team was great and very understanding and we were able to work this out with them.

Positive

We didn't have any previous solution. At a previous company, I've used Sentry. However, I also find Datadog to be much easier, plus the inclusion of synthetic tests is awesome.

The documentation was great and our setup was easy.

We implemented the solution in-house.

This has had a great ROI as we've been able to address critical bugs that have been found via our Datadog tools.

The setup cost was minimal. The documentation is great and the product is very easy to set up.

We also looked at other providers and settled on Datadog. It's been great to use across all our clients.

We have a tech stack including all backend services written in TS/Node (mostly) and as a full stack engineer, it is crucial to keep track of new and existing errors. Our logs have been consolidated in Datadog and are accessible for search and review, so the service has become a daily tool for my work. 

More recently, session replay has been adopted at my company, but I do not like it so much because the UI elements are not in their place, so it is very hard to see what the users on the web app are actually clicking on.

The best way it has helped us is by consolidating all our logs into a single place and making it easier to find errors. Previously using AWS Cloudwatch was cumbersome and time-consuming. One issue I do have with logs is the length of time they are on the platform. Some issues happen sporadically, so it would be good to have logs for longer than one month by default or make it a configuration. 

Another issue that I have is with the search syntax, it could be simpler and it feels like there are too many ways to do the same things.

Logs search is the most valuable feature because it has consolidated all of our backend services logs into one place. Now we can see the relationship between them as requests are going from one service to other dependencies. 

One issue I do have with logs is the length of time they are on the platform. Some issues happen sporadically, so it would be good to have logs for longer than one month by default or make it a configuration. I have yet to try rehydrating logs, so this might be an option I need to try. Another issue I have is with the search syntax, it could be simpler. The syntax is a bit cumbersome and there is not an intuitive to save them to look for similar searches in the future. 

Finally, while my company replaced a different tool for session replay with DataDog's version, I find it clunky and in need of further improvements. For example, when troubleshooting a web portal issue, it is super important to know what the user clicked, but the elements are not where they should be in the replay.

It is also hard to find details about the sessions, and metadata such as user email, account, etc. that exist on other services with replay features.

I have been using Datadof for approximately five years.

So far we haven't had any issues with uptime and Datadog has been available when needed.

It seems to scale well as we continue to add services that need monitoring.

I haven't had to contact support.

Cloudwatch was not a great tool for what we need to do to troubleshoot issues.

We deployed it in-house with intermediate expertise.

I am not sure how much we are paying, but I use the app often enough to feel like we are getting a good ROI.

I was not involved in the choosing process as a software engineer

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.  

We use the product for instrumentation, observability, monitoring, and alerting of our system. 

We have multiple environments and a variety of pieces of infrastructure including servers, databases, load balancers, cache, etc. and we need to be able to monitor all of these pieces, while also retaining visibility into how the various pieces interact with each other. 

Tracing data between components and user interactions that trigger these data flows is particularly important for understanding where problems arise and how to resolve them quickly.

It provides a lot of options for integrations and tooling to observe what is happening within the system, making diagnosis and triage easier/faster. 

Each user can set up their own dashboards and share them with other users on the team. We can instrument monitors based on various patterns that we care about, then notify us when an event triggers an alert with platforms such as Slack or PagerDuty. 

Our ability to rapidly become aware of problems focused on the symptoms being observed and entry points into the tool to rapidly identify where to investigate further is important for our team and our users.

The most valuable aspects of the solution include log search to help triage specific problems that we get notified about (whether by alerts we have configured or users that have contacted us), APM traces (to view how user interactions trace through the various layers of our infrastructure and services to be able to reproduce and identify the source of problems), general performance/system dashboards (to regularly monitor for stability or deviation), and alerting (to be automatically informed when a problem occurs). We also use the incident tools for tracking production incidents.

In some ways, the tool has a pretty steep learning curve. Discovering the various capabilities available, then learning how to utilize them for particular use cases can be challenging. Thankfully, there is a good amount of documentation with some good examples (more are always welcome), and support is very helpful. 

While DataDog has started adding more correlation mapping between services and parts of our system, it is still tricky to understand what is the ultimate root cause when multiple views/components spike. Additionally, there are lots of views and insights that are available but hard to find or discover. Some of the best ways to discover is to just click around a lot and get familiar with views that are useful, but that takes time and isn't ideal when in the middle of fighting a fire.

I've used the solution for about four years.

It seems stable.

It seems to scale well. Performance for aggregating or searching is usually very fast.

Technical support is helpful and pretty responsive.

Positive

We did not use a different solution. 

It's hard to say what ROI would be as I have not managed our system without it to compare to.

I don't manage licensing.

We did not evaluate other options. 

It's a great tool with new features and improvements continuously being added. It is not simple to use or set up, however, if you have the right personnel, you can get a lot of value from what DataDog has to offer.

We monitor our multiple platforms using Datadog and post alerts to Slack to notify us of server and end-user issues. We also monitor user sessions to help troubleshoot an issue being reported. 

We monitor 3.5 platforms on our Datadog instance, and the team always monitors the trends and Dashboards we set up. We have two instances to span the 3.5 platforms and are currently looking to implement more platform monitoring over time. The user session monitoring is consistent for one of these platforms. 

Datadog has improved our response time and cost-efficiency in bug reporting and server maintenance. We're able to track our servers more fluidly, allowing us to expand our outreach and decrease response time. 

There are many different ways that Datadog is used, and we monitor three and a half platforms on the Datadog environment at this time. By monitoring all of these platforms in one easy-to-use instance, we're able to track the platform with the issue, the issue itself, and its impact on the end user. 

The server monitoring, service monitoring, and user session monitoring are extremely helpful, as they allow us to be alerted ahead of time of issues that users might experience. More often than not, an issue is not only able to be identified, but solved and released before an end user notices an issue. 

We are currently using this as an investigative tool to notice trends, identify issues, and locate areas of our program that we can improve upon that haven't been identified as pain points yet. This is another effective use case. 

I would like to see a longer retention time of user sessions, even if by 24 to 48 hours, or even just having the option to be configurable. By doing this, we're enabled to store user sessions that have remained invisible for a long time, and identify issues that people are working around. 

I would also like to see an improvement in the server's data extraction times, as sometimes it can take up to ten minutes to download a report for a critical issue that is costing us money. Regardless, I am very happy with Datadog and love the uses we have for the program so far.  

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

We did not previously use a different solution. 

Internally our primary usage of Datadog pertains around APM/tracing, logging, RUM (real user monitoring), synthetic testing of service/application health and state, overall general monitoring + observability, and custom dashboards for aggregate observability. We also are more frequently leveraging the more recent service catalog feature.

We have several microservices, several databases, and a few web applications (both external and internal facing), and all of these within our systems are contained within several environments ranging from dev, sit, eat, and production.

Datadog has had a massive impact on our department. Before, we had loose logging dumped into a sea of GCP logs with haphazard custom solutions for traceability between logs and network calls. Datadog has helped standardize and normalize our processes around observability while providing fantastic tools for aggregating insight around what is monitored regularly, all wrapped in an easy-to-use UI.

Additionally, a range of types of users exist within our department, each with its own positive impact on Datadog. DevOps leverages it to easily manage infra, developers leverage it to easily monitor/debug services and applications, and business leverages it for statistics.

Personally I've found the RUM (real user monitoring) to be above and beyond what I've worked with before. Client-side monitoring has always been on the short end of the stick but the information collected and ease of instrumentation provided by Datadog is second to none.

Having a live dynamic service map is also one of my favourite features; it provides real-time insights into which services/applications are connected to which.

We are also investigating the new API catalog feature set, which I believe will provide a high-value impact for real-time documentation and information about all of our shared microservices that other dev teams can use.

In production, we intend to use trace IDs generated by RUM to attach to support tickets when a user experiences a traceable network error, and we want to display this trace ID to the user so if they were to contact us about a specific issue, they can provide us an exact ID displayed to them back to us. Currently, this is not possible out-of-the-box client-side without inventing our own solution for capturing these trace IDs, such as shimming the native fetch or returning the ID from the service response.

I've used the solution for approximately two years across our department and around a year or so of it being used practically and fully integrated into our systems.

Aside from one very brief bad update from the Datadog team around RUM where they broke the native 'fetch' for node in an update to RUM (which was resolved quickly) as it used to -- and may still -- modified the global 'fetch'; Datadog as a whole solution has been highly stable.

It's easy to implement and scale provided a there's a solid IaC solution in place to integrate across your system.

The Datadog support team is prompt and helpful when tickets have been submitted from our end. When their support team have been unsure, they've properly reached out internally to the relevant SME to help answer any questions we've had prior.

Positive

I've personally dabbled with some other open-source observability and monitoring solutions; however, prior to Datadog, our department did not have any solutions other than log dumps to GCP.

The initial setup was straightforward from my own experience, helping integrate within the application and service levels; however, our DevOps team handled most of the infra process with minimal complaints.

We handled the solution in-house.

I personally am not involved in the decision around costing; however, I am aware that when we first set up Datadog, we explicitly configured our services/applications to have a master switch to enable Datadog integration so that we can dynamically enable/disable targeted environments as need due to the costs being associated on a per service basis for APM/logging/etc.

I was not involved in the decision-making regarding the evaluation of other options.

I highly recommend Datadog, and I would explore it for my own individual projects in the future, provided the cost is within reason. Otherwise, I would highly recommend it for any medium-to-large-sized org.

From day one, we have seamlessly integrated our new product into Datadog, a comprehensive monitoring and analytics platform. By doing so, we are continuously collecting essential data such as host information, system logs, and key performance metrics. This enables us to gain deep insights into product adoption, monitor usage patterns, and ensure optimal performance. Additionally, we use Datadog to capture and analyze errors in real-time, allowing us to troubleshoot, replay, and resolve production issues efficiently.

It has proven invaluable in helping us identify early issues within the product as soon as they occur, allowing us to take immediate action before they escalate into more significant problems. This proactive approach ensures that potential challenges are addressed in real-time, minimizing any impact on users. Furthermore, the system allows us to measure product adoption and usage trends effectively, providing insights into how customers are interacting with the product and identifying areas for improvement or enhancement.

There isn't any single aspect that stands out in particular; rather, everything is interconnected and works together harmoniously. Each component complements the other, creating a cohesive system where data, logs, and metrics are seamlessly integrated. This interconnectedness ensures that no part operates in isolation, allowing for a more holistic view of the product's performance and health. The way everything binds together strengthens our ability to monitor, analyze, and improve the product efficiently.

At the moment, nothing specific comes to mind. Everything seems to be functioning well, and there are no immediate concerns or issues that I can think of. 

The system is operating as expected, and any challenges we've faced so far have been successfully addressed. If anything does come up in the future, we will continue to monitor and assess it accordingly, but right now, there’s nothing that stands out requiring attention or improvement. 

Datadog is too pricey when compared to its competitors, and this is something that its always on my mind during the decision-making process.

I've used the solution for nearly two years now.

We use the solution for APM, anomaly detection, resource metrics, RUM, and synthetics. 

We use it to build baseline metrics for our apps before we start focusing in on performance improvements. A lot of times that’s looking at methods that take too long to run and diving into db queries and parsing.

I’ve used it in multiple configurations in aws and azure. I’ve built it using terraform and hand rolled. 

I’ve used it predominantly with Ruby and Node and a little bit of Python. 

The solution provides deep insights into our stack. It gives us the ability to measure and monitor before making decisions.

We're using it to make informed decisions about performance. Being able to show how across a timeline we increased performance from a release via a visual indication of p50+ metrics is almost magical. 

Another way we use it is for leading indicators of issues that might be happening. So for example, anomaly detection on gauge metrics across the app and having synthetics build in with alerting configurations are both ways we can get alerted sometimes even before a big issue is about to happen. 

The most valuable aspects include APM, RUM and dashboards. 

I think of Datadog as an analytics company first. And that the integrations around notifications and alerts as a part of insight discoverability. 

Everything Datadog offers for me is around knowledge building and how much do I know about the deep details of my stack.

The pricing model makes more sense than what we paid for against other competitors. I was at one job where we used two competing services because DD didn’t have BAA for APM. And then when it offered it, we immediately dumped the other solution for Datadog.

Logging is not a great experience. Searching for specific logs and then navigating around the context of the results is slow and cumbersome. Honestly that is my only gripe for Datadog. It’s a wonderful product outside of log searching. I have had better experience using other services that aggregate logs for search. 

My use case for it is around discoverability. Log search is fine if I’m just looking for something specific. That said, if it’s something else targeted and I am wandering around looking for possible issues, it’s really unintuitive. 

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

Very stable. 

We always implement the solution in-house.