Datadog Reviews

We use the solution to monitor production service uptime/downtime, latency, and log storage. 

Our entire monitoring infrastructure runs off Datadog, so all our alarms are configured with it. We also use it for tracing API performance; what are the biggest regression points. 

Finally we use it to compare performance on SEO metrics vs competitors. This is a primary use case as SEO dictates our position from google traffic which is a large portion of our customer view generation so it is a vital part of the business we rely on datadog for.

The product improved the organization primarily by providing consistent data with virtually zero downtime. This was a problem we had with an old provider. It also made it easy to transition an otherwise massive migration involving hundreds of alarms. 

The training provided was crucial, along with having a dedicated team that can forward our requests to and from Datadog efficiently. Without that, we may have never transitioned to Datadog in the first place since it is always hard to lead a migration for an entire company.

The API tracing has been massive for debugging latency regressions and how to improve the performance of our least performant APIs. Through tracing, we managed to find the slowest step of an API, improve its latency, and iterate on the process until we had our desired timings. This is important for improving our SEO as LCP, INP are directly taking from the numbers we see on Datadog for our API timings. 

The ease of dashboard creation and alarm monitoring has helped us not only stay competitive but be industry leaders in performance.

The product can be improved by allowing the grouping of APIs to add variables. That way, any API with a unique ID could be grouped together. 

Furthermore, SEO monitoring has been crucial for us but also a difficult part to set up as comparing alarms between us and competitors is a tough feat. Data is not always consistent so we have been toying and experimenting with removing the noise of datadog but its been taking a while. 

Finally, Datadog should have a feature that reports stale alarms based on activity.

I've used the solution for six months.

Its very stable and we have not experienced an issue with downtime on Datadog.

Datadog works well for scalability as volume has not seemed to slow.

We haven't talked to the support team. 

We switched to Datadog as we used to have a provider that had very inconsistent logging. Our alarms would often not fire since our services were not working since the provider had a logging problem.

The initial setup was somewhat complex due to the built-in monitoring with services. This is not always super comprehensive and has to be studied as opposed to other metrics platforms that just service all your endpoints, which you can trace them with Grafana.

We implemented the solution through an in-house team.

The ROI is good.

Users must try to understand the way Datadog alarms work off the bat so that they can minimize the requirements for expensive features like custom metrics. 

It can sometimes be tempting to use them; however, it is not always necessary as you migrate to Datalog, as they are a provider that treats alarms somewhat differently than you may be used to.

We have evaluated New Relic, Grafana, Splunk, and many more in our quest to find the best monitoring provider.

The product monitors multiple systems, from customer interactions on our web applications down to the database and all layers in between. RUM, APM, logging, and infrastructure monitoring are all surfaced into single dashboards.

We initially started with application logs and generated long-term business metrics out of critical logs. We have turned those metrics and logs into a collection of alerts integrated into our pager system. As we have evolved, we have also used APM and RUM data to trigger additional alerts. 

The solution has surfaced how integrated our applications really are and helps us track calls from the top down, identifying slowness and errors all through the call stack.

The biggest improvement we have seen is our time to discovery and resolution. As Datadog has improved, and we add new features, the depth and clarity we get from top to bottom has been excellent. Our engineering teams have quickly adopted many features within Datadog, and are quick to build out their own dashboards and alerts. This has also led to a rapid sprawl when left unchecked.

We started with application logs and have expanded over the years to include infrastructure, APM, and now RUM. All of these tools have been incredibly valuable in their own sphere. The huge value is tying all of the data points together.

Logging was the first tool we started with years ago, replacing our ELK stack. It was the easiest to get in place, and our engineers quickly embraced the tools. Several critical dashboards were created years ago and are still in use today. Over time, we have shifted from verbose logs and matured into APM and RUM. That has helped us focus on fine-tuning the performance of our applications.

We need better visibility into our consumption rate, which is tied to our commit levels. We would love to see a % consumed and alert us if we are over budget before getting an overage charge 20 days into the month.

The biggest complaint we hear comes from the cost of the tool. It is pretty easy to accidentally consume a lot of extra data. Unless you watch everything come in almost daily, you could be in for a big surprise. 

We utilize the Datadog estimated usage metrics to build out alerts and dashboards. The usage and cost system page still doesn't tie into our committed spending - it would be wonderful to see the monthly burn rate on any given day.

I've used the solution for six years.

There have not been as many outages in the past year. We also haven't been jumping into the new features as quickly as they come out. We may be working on more stable products.

It has scaled up to meet our needs pretty well. Over the years, we have only managed to trigger internal DataDog alerts once or twice by misconfiguring a metric and spiralling out of control with costs.

Support has been lacking. Opening a chat with the tech support rep of the day is always a gamble. We are looking into working with third-party support because it has been so rough over the years.

Neutral

We used the ELK stack for logging and monitoring and AppDynamics for APM.

The initial setup for new teams has become easier over the years. We are increasing our adoption rate as we shift our technology to more cloud-native tools. Datadog has supported easy implementation by simply adding a package to the app. 

They have really focused on a lot of out-of-the-box functionality, but the real fun happens as you dive deeper into the configuration. We have also begun adapting open telemetry standards. This has kept us from going too deep into vendor-specific implementations.

We did the initial setup via an in-house team.

As long as we stay on top of our consumption mid-month, it has been worth it. However, the few engineers we have who are dedicated to playing whack-a-mole with the growing spending could be better utilized in teaching best practices to new users. I suppose our implementation of the rapidly changing tools over the years has led to a fair amount of technical debt.

It is quite easy to set up any specific tool, but to take advantage of the full visibility it offers, you need to instrument across the board—which can be time-consuming. Be careful about how each tool is billed, and watch your consumption like a hawk.

We evaluated AppDynamics and Dynatrace.

It's a very powerful tool, with lots of new features coming, but you certainly will pay for what you get.

We use Datadog for monitoring the performance of our infrastructure across multiple types of hosts in multiple environments. We also use APM to monitor our applications in production. 

We have some Kubernetes clusters and multi-cloud hosts with Datadog agents installed. We have recently added RUM to monitoring our application from the user side, including replay sessions, and are hoping to use those to replace existing monitoring for errors and session replay for debugging issues in the application.

We have been using Datadog since I started working at the company ten years ago and it has been used for many reasons over the years. Datadog across our services has helped debug slow performance on specific parts of our application, which, in turn, allows us to provide a snappier and more performant application for our customers. 

The monitoring and alerting system has allowed our team to be aware of the issues that have come up in our production system and react faster with more tools to debug and view to keep the system online for our customers.

Datadog infrastructure monitoring has helped us identify health issues with our virtual machines, such as high load, CPU, and disk usage, as well as monitoring uptime and alerting when Kubernetes containers have a bad time staying up. Our use of Datadog's Application Monitoring, APM over the last six years or so has been crucial to identifying performance and bottleneck issues as well as alerting us when services are seeing high error rates, which have made it easier to debug when specific services may be going down.

We have found that some of the different options for filtering for logs ingestion, APM traces and span ingestion, and RUM sessions vs replay settings can be hard to discover and tough to determine how to adjust and tweak for both optimal performance and monitoring as well as for billing within the console. 

It can sometimes be difficult to determine which information is documented, as we have found inconsistencies with deprecated information, such as environment variables within the documentation.

I've been using the solution for ten years.

The solution seems pretty stable, as we've been using it for more than a decade.

The solution seems quite scalable, especially within Kubernetes. Costs are a factor.

SUpport has been very helpful whenever we need it.

Positive

We had tried some other APM monitoring in the past, however, it was too expensive, and then we added it to Datadog since we were already using Datadog and it seemed like a good value add.

The solution is straightforward to set up. Sometimes, it is complex to find the correct documentation.

We handled the setup in-house.

 Our ROI is ease of mind with alerts and monitoring, as well as the ability to review and debug issues for our customers.

Getting settled on pricing is something you want to keep an eye on, as things seem to change regularly.

We used New Relic previously.

Datadog is a great service that is continually growing its solution for monitoring and security. It is easy to set up and turn on and off its features once you have instrumented agents and tailored solutions to your needs.

We were looking for an all-in-one observability platform that could handle a number of different environments and products. At a basic level, we have a variety of on-premises servers (Windows/Mac/Linux) as well as a number of commercial, cloud-hosted products. 

While it's often possible to let each team rely on its own means for monitoring, we wanted something that the entire company could rally around - a unified platform that is developed and supported by the very same people, not others just slapping their name on some open source products they have no control over.

Datadog has effortlessly dropped in to nearly every stage of observability for us. We appreciate how it has robust cross-platform support for our IT assets, and for integrating hosted products, enabling integrations often couldn't be easier, with many of them including native dashboards and even other types of content packs. 

Over the last couple of years, we have onboarded a number of engineering teams, and each of them feels comfortable using Datadog. This gives us the ability to build organizational knowledge.

Datadog's learning platform is second to none. It's the gold standard of training resources in my mind; not only are these self-paced courses available at no charge, but you can spin up an actual Datadog environment to try out its various features. 

I just hate when other vendors try to upsell you on training beyond their (often poorly-written) documentation. Apart from that, we appreciate the variety of content that comes from Datadog's built-in integrations - for common sources, we don't have to worry about parsing, creating dashboards, or otherwise reinventing the wheel.

Datadog's roadmap can be a bit unpredictable at times. For instance, a few years ago, our rep at the time stated that Datadog had dropped its plans to develop an incident on-call platform. However, this year, they released a platform that does exactly that.

They also decided to drop chat-based support just recently. While I understand that it's often easier to work with support tickets, I do miss the easy availability of live support. 

It would be nice if Datadog continued to broaden its variety of available integrations to include even more commercial platforms because that is central to its appeal. If we're looking at a new product and there isn't a native integration, then that's more work on our part.

We use the solution for logs, infrastructure metrics, and APM. We have many different teams using it across both product and data engineering.

The solution has improved our observability by giving us rapid log search, a correlation between hosts/logs/APM, and tons of features in one website.

I enjoy the rapid log search. It's such a pleasure to quickly find what you're looking for. The ease of graph building is also nice, and MUCH easier than Prometheus.

It is far too easy to run up huge unexpected costs. The billing model is not flexible enough to handle cases where you temporarily have thousands of nodes. It is not price effective for monitoring big data jobs. We had to switch to open-source Grafana plus Prometheus for those.

It would be cool to have an open telemetry agent that automatically APM instruments everything in the next release.

I've used the solution for three years.

I'd rate the stability ten out of ten.

I'd rate the scalability ten out of ten.

We did not previously use a different solution.

The setup is very straightforward. Users just install the helm chart, and boom, you're done.

We handled the setup in-house.

Be careful about pricing. Make sure you understand the billing model and that there are multiple billing models available. Set up alarms to alert you of cost overruns before they get too bad.

We've never evaluated other solutions.

It's a great product. However, you have to pay for quality.

Our primary use of Datadog involves monitoring over 50 microservices deployed across three distinct environments. These services vary widely in their functions and resource requirements. 

We rely on Datadog to track usage metrics, gather logs, and provide insight into service performance and health. Its flexibility allows us to efficiently monitor both production and development environments, ensuring quick detection and response to any anomalies. 

We also have better insight into metrics like latency and memory usage.

Datadog has significantly improved our organization’s monitoring capabilities by centralizing all of our logs and making them easily searchable. This has streamlined our troubleshooting process, allowing for quicker root cause analysis. 

Additionally, its ease of implementation meant that we could cover all of our services comprehensively, ensuring that logs and metrics were thoroughly captured across our entire ecosystem. This has enhanced our ability to maintain system reliability and performance.

The intuitive user interface has been one of the most valuable features for us. Unlike other platforms like Grafana, as an example, where learning how to query either involves a lot of trial and error or memorization almost like learning a new language, Datadog’s UI makes finding logs, metrics, and performance data straightforward and efficient. This ease of use has saved us time and reduced the learning curve for new team members, allowing us to focus more on analysis and troubleshooting rather than on learning the tool itself.

While the UI and search functionality are excellent, further improvement could be made in the querying of logs by offering more advanced templates or suggestions based on common use cases. This would help users discover powerful queries they might not think to create themselves. 

Additionally, enhancing alerting capabilities with more customizable thresholds or automated recommendations could provide better insights, especially when dealing with complex environments like ours with numerous microservices.

I've used the solution for five years.

We have never experienced any downtime.

We previously used Sumo Logic.

The current use case for Datadog in our environment is observability.  We use Datadog as the primary log ingestion and analysis point, along with consolidation of application/infrastructure metrics across cloud environments and realtime alerting to issues that arise in production.  

Datadog integrates within all aspects of our infrastructure and applications to provide valuable insights into Containers, Serverless functions, Deep Logging Analysis, Virtualized Hardware and Cost Optimizations.

Datadog improved our observability layer by creating a consistent, centralized service for all of our varied cloud-based applications. All of our production and non-production environment applications and infrastructure send metrics directly to Datadog for analysis and determination of any issues that would need to be looked at by the Infrastructure, Platform and Development teams for quick remediation. Using Datadog as this centralized Observability platform has enabled us to become leaner without sacrificing project timelines when issues arise and require triage for efficient resolution.

All of Datadog's features have become valuable tools in our cloud environments.

Our primary alerts, based on metrics and synthetic transactions, are the most used and relied upon for decreased MTTA/MTTR across all of our platforms. This is followed by deep log analysis that enables us to quickly and easily get to a preliminary root cause that someone on the infrastructure, platform or development teams can take and focus their attention on the precise target that Datadog revealed as the issue to be remediated.

The two areas I could see needing improvement or a feature to add value are building a more robust SIM that would include container scanning to rival other such products on the market so we do not need to extend functionality to another third-party provider. The other expands the alerting functions by creating a new feature to add direct SMS notifications, on-call rotation scheduling, etc., that could replace the need to have this as an external third party solution integration. 

I've been a Datadog user for almost ten years.

Datadog is very stable, and we've only come across a few items that needed to be addressed quickly when there were issues.

Scalability is very favorable, aside from cost/budget, which limits the scalability of this platform.

Both customer service and support need a little work, as we have had a number of requests/issues that were not addressed as we needed them to be.

Neutral

Being an Observability SME, I have used many native and third party solutions, including Dynatrace, New Relic, CloudWatch and Zabbix. As previously mentioned, Datadog provides a superior platform for centralizing and consolidating our Observability layer. Switching to Datadog was a no-brainer when most other solutions either didn't provide the maturity of functions, or have them available, at all.

The initial setup was very straightforward, and the integrations were easily configured.

We implemented Datadog in-house.

For the most part, Datadog's ROI is quite impressive when you consider all of the features and functions that are centralized on the platform. It doesn't require us to purchase additional third-party solutions to fill in the gaps.

The setup was dead simple once the cloud integrations and agent components were identified and executed. Licensing falls into our normal third-party processes, so it was a familiar feeling when we started with Datadog. Cost is the only outlier when it comes to a perfect solution. Datadog is expensive, and each add-on drives that cost further into the realm of requiring justifications to finance expanding the core suite of features we would like to enable.

Yes, we evaluated several competing platforms that included Dynatrace, New Relic and Zabbix.

They should provide more inclusive pricing, or an "all you can eat" tier that would include all relevant features, as opposed to individual cost increases to let Datadog to become more valuable and replace even more third-party solutions that have a lower cost of entry.

Our primary use case is custom and vendor supplied web application log aggregation, performance tracing and alerting. We run a mix of AWS EC2, Azure serverless, and colocated VMWare servers to support higher education web applications. 

Managing a hybrid multi-cloud solution across hundreds of applications is always a challenge. Datadog agents on each web host, and native integrations with GitHub, AWS, and Azure gets all of our instrumentation and error data in one place for easy analysis and monitoring.

Through 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. 

Whether the app is vendor-supplied or we built it ourselves, the depth of tracing, profiling, and hooking into logs is all obtainable and tunable. Both legacy .NET Framework and Windows Event Viewer and cutting edge .NET Core with streaming logs all work. 

The breadth of coverage for any app type or situation is really incredible. It feels like there's nothing we can't monitor.

When it comes to Datadog, several features have proven particularly valuable. The centralized pipeline tracking and error logging provides a comprehensive view of our development and deployment processes, making it much easier to identify and resolve issues quickly. 

Synthetic testing has been a game-changer, 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. 

Together, these features form a powerful toolkit that helps us maintain high performance and reliability across our applications and infrastructure, ultimately leading to better user satisfaction and more efficient operations.

I'd like to see an expansion of the Android and IOS apps to have a simplified CI/CD pipeline history view. I like the idea of monitoring on the go yet it seems the options are still a bit limited out of the box. While the documentation is very good considering all the frameworks and technology Datadog covers, there are areas - specifically .NET Profiling and Tracing of IIS-hosted apps - that need a lot of focus to pick up on the key details needed. In some cases the screenshots don't match the text as updates are made. I spent longer than I should figuring 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 and clean and light resource usage of the agents.

The solution is very scalable, very customizable.

Service is always helpful in tuning our committed costs and alerting us when we start spending outside the on-demand budget.

We used a mix of a custom error email system, SolarWinds, UptimeRobot, and GitHub actions. We switched to find one platform that could give deep app visibility regardless of whether it is Linux or Windows or Container, cloud or on-prem hosted.

The setup was generally simple. However, .NET Profiling of IIS and aligning logs to traces and profiles was a challenge.

We implemented the solution in-house. 

I'd count our ROI as significant time saved by the development team assessing bugs and performance issues.

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.

I'm excited to dig further into the new offerings around LLM and continue to grow our footprint in Datadog. 

We are using Datadog to improve our cloud monitoring and observability across our enterprise apps.  We have integrated a lot of different resources into Datadog, like Kubernetes, App Gateways, App Service Environments, App Service Plans, and other Web App resources. 

I will be using the monitoring and observability features of Datadog. Dashboards are used very heavily by teams and SREs. We really have seen that Datadog has already improved both our monitoring and our observability.

The ease and speed of which you can create a dashboard has been a huge improvement.  

The different types of monitors we can create have been huge, too. We can do so many different things with monitors that we couldn't do before with our alerts. 

Being able to click on a trace or log and drill down on it to see what happened has been great.  

Some have found the learning curve a bit steep. That said,they are coming around slowly. There is just a lot of information to learn how to navigate.

The different types of monitors have been very valuable. We have been able to make our alerts (monitors) more actionable than we were able to previously.  

Watchdog is a favorite feature among a lot of the devs. It catches things they didn't even know were an issue. 

RUM is another feature a lot of us are looking forward to seeing how it can help us improve our customer experience during tax season.  

We hope to enable the code review feature at some point to so we can see what code caused the issue.

I would like to see the integration between PagerDuty and Datadog improved.  The tags in Datadog don't match those in PagerDuty, and we have to make it work.  Also, I would like to see if the ability to replicate a KQL query in Datadog is made easier or better.  

I would like to see the alert communications to email or phones made better so we could hopefully move off PagerDuty and just use Datadog for that. 

There are also a lot of features that we haven't budgeted for yet and I would like for us to be able to use them in the future.

I've used the solution for about two years.

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.

Positive

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.