Easy dashboard creation and alarm monitoring with a good ROI
What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I've used the solution for six months.
What do I think about the stability of the solution?
Its very stable and we have not experienced an issue with downtime on Datadog.
What do I think about the scalability of the solution?
Datadog works well for scalability as volume has not seemed to slow.
How are customer service and support?
We haven't talked to the support team.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
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.
What about the implementation team?
We implemented the solution through an in-house team.
What was our ROI?
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
We have evaluated New Relic, Grafana, Splunk, and many more in our quest to find the best monitoring provider.
Which deployment model are you using for this solution?
Hybrid Cloud
A great tool with an easy setup and helpful error logs
What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
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.
What do I think about the stability of the solution?
Stability has been great. We've had no issues so far.
What do I think about the scalability of the solution?
The solution is very easy to scale. We've used it on multiple clients.
How are customer service and support?
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.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
The documentation was great and our setup was easy.
What about the implementation team?
We implemented the solution in-house.
What was our ROI?
This has had a great ROI as we've been able to address critical bugs that have been found via our Datadog tools.
What's my experience with pricing, setup cost, and licensing?
The setup cost was minimal. The documentation is great and the product is very easy to set up.
Which other solutions did I evaluate?
We also looked at other providers and settled on Datadog. It's been great to use across all our clients.
Which deployment model are you using for this solution?
Private Cloud
Unified platform with customizable dashboards and AI-driven insights
What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I've used the solution for two years.
Good dashboards, easy troubleshooting, and integrations
What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
The dashboards are super convenient to us for a more zoomed out view of what is going on with each integration that we utilize.
What needs improvement?
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.
For how long have I used the solution?
I've used the solution for about 1.5 years.
Monitoring with datadog
What do you like best about the product?
APM traces, In detailed logging of pdos, service level data.
What do you dislike about the product?
Implementation of cloud siem is not so easy and its not as good as wazuh.
What problems is the product solving and how is that benefiting you?
Out entire monitoring is based on datadog
Becoming the Gold Standard
What do you like best about the product?
DataDog provides thorough insights accross all of the important facets
What do you dislike about the product?
DataDog has an excellent offering and continues to provides new services to keep clients and fulfill the needs of the industry. However, with this comes a premium and in turn hinders its adoption or for those who use it, to use it completely.
What problems is the product solving and how is that benefiting you?
Providing key actionable insights
This is a good product, but is only just starting to bubble up observability. Takes minutes
What do you like best about the product?
It is all in one place, the UI is fairly nice. Customer support is bomb! Thanks!
What do you dislike about the product?
Graphs are a bit small, it would be nice to have more datapoints. Also still no real useable HEATMAPS.
What problems is the product solving and how is that benefiting you?
Incident management using workflows/appbuilder/incident management
APM is growing by leaps and bounds
Great product, could get very expensive if you're not careful
What do you like best about the product?
Easy to use, great functionality for monitoring backend apps. Scales well for applications under big loads
What do you dislike about the product?
Because of it's pricing model, sometimes you pay for things you are not using. Easy to forget you have loads of metrics on some forgotten apps generating cost.
What problems is the product solving and how is that benefiting you?
Helps me monitor the health of my apps
Incredibly flexible, intuitive, and accessible
What do you like best about the product?
The ease of integration for most of their products like RUM, super easy to set up even with limited knowledge, and due to their wide variety of products you can have all your logs/alerting/monitoring in one place across multiple services.
What do you dislike about the product?
Once you get deep into custom metrics and analysis it can get confusing, their graphing/visualization tool is unfortunately not very intuitive and they are missing a lot of features that something like Tableau might have.
What problems is the product solving and how is that benefiting you?
Being able to track user activity/history makes it much easier when debugging errors
Powerful insights for infrastructure, services, and applications
What do you like best about the product?
When implemented well, it has a comprehensive feature set that encompasses the entire SDLC.
What do you dislike about the product?
The documentation only enables a real-world application to a degree, but some hands on support/implementation would be beneficial and drive value for our Datadog investment.
What problems is the product solving and how is that benefiting you?
Datadog is helping us resolve incidents in near real-time.
