Datadog is used in the organization for the observability implementations for the application of frontend and backend that are running in the Kubernetes and cloud services. It also used for the Alerting, Dashboarding, SLI-SLO setup for service performance monitoring, Enabling the traces for the all components journey monitoring. Servers monitoring that running with multiple workloads, for the incident management Datadog is used.
Pros
Dashboard creation process is good and shows the critical data in visually and easily understandable manner.
Alert setup is easy and helps in knowing the service abnormal functionalities very easily.
Supports multiple cloud integrations and third party tools integration and check the performance of those services.
Cons
API calls sometimes takes times in showing the data these can be improved as it is a minor issue.
Support case tickets can be processed quickly for the fast issue resolution.
Service Map visibility can be more clear when the application multiple components involvement.
Likelihood to Recommend
In Application monitoring Datadog helps in understanding all the components of apps and know the performance and troubleshoot the issue very quickly. Provides the option of synthetics which helps in proactive monitoring of the endpoints and knowing about the status of the apps and services. Visually creating the Dashboards which provides the understanding of the key metrics and understand the stack in the boarder manner. Getting notification about the services whenever it causes the abnormal activities in the workflow.
VU
Verified User
Engineer in Information Technology (Information Technology & Services company, 201-500 employees)
in my org, Datadog is used to for monitoring the application performance, and managing logs across different environments. we use this for tracking latency, throughput, and errors in our microservices. We use it for creating custom dashboards and alerts on deployments. also for monitoring cloud resources and Kubernetes clusters. this makes debugging faster and cross team interaction is smooth.
Pros
Tracking Microservice Performance
managing the logs
Monitors Cloud and Kubernetes
Speeds Up Debugging and dashboards
Cons
costing incerease based on the logs volume.
Creating monitors, dashboards, or logs requires some context on Datadog syntax.
role based access needs to be more granular if someone has larger team members
Likelihood to Recommend
It does a great job of monitoring our cloud infra and Kubernetes clusters, giving us a clear picture of system health. custom dashboards are also helpful for checking the key metrics during deployments. But, also need to improve in the costing, especially with large volumes of logs which makes it important to be selective about what data you log.
I use Datadog to monitor my websites, infrastructure and databases etc. It solves multiple business problems. Earlier, I was using various tools for observability and monitoring purposes, but now, after migrating everything to Datadog, I don't have to use multiple tools. Now I am using it as a centralized observability solution. It gives real time incidents which helps us to reduce the MTTD and MTTR. I am using microservices architecture, so earlier I was using different tools to troubleshoot my issues, and it's really difficult to show the end-to-end transaction journey in those tools, but now after migrating to Datadog, it's easy to show the end-to-end transaction journey with the help of its distributed tracing concept. And also helps us to monitor my cloud services, with the help of Datadog, I am able to increase the availability of my services and reduce the downtime.
Pros
The thing which Datadog does really well, one of them are its broad range of services integrations and features which makes it one step observability solution for all. We can monitor all types of our application, infrastructure, hosts, databases etc with Datadog.
Its custom dashboard feature which helps us to visualize the data in a better way . It supports different types of charts through those charts we can create our dashboard more attractive.
Its AI powered alerting capability though that we can easily identify the root cause and also it has a low noise alerting capability which means it correlated the similar type of issues.
Cons
The thing which I feel that Datadog can improve is its UI it should make its UI more user friendly so that any new user also can easily work on Datadog.
It is a bit costly compare to other observability tool, so Datadog team can work on that.
Likelihood to Recommend
Datadog works really well with complex microservices architecture like any E-commerce platform which will be having multiple services but they all are interdependent to others so in this scenario Datadog will be best to monitor these as it will show the transactions also between those microservices. If you are using multiple services in your architecture whether it will be cloud services or on prem services Datadog will be the best choice to monitor all those service with in Datadog so that you can see everything in a single place. But if you are having small architecture and few services in that then in that scenario you can use Datadog but it will be little costly as compared to other but obviously the features are very well.
VU
Verified User
Engineer in Engineering (Information Technology Services company, 201-500 employees)
Our use case crosses teams—DevOps, backend engineering, and QA. We use it for everything from monitoring system CPU and memory usage on our cloud instances to application-level error custom alerting. We also use it extensively for deployment alerting, real-time SRE dashboards, and even for long-term performance analysis.Overall, Datadog is now part of our observability stack. It makes us proactive about issues in the system, and not reactive.
Pros
Unified Observability (Metrics, Logs, Traces in One Place)
Real Time Dashboards
Good Alerting mechanism
Cons
learning curve for new users
Dashboards can sometimes be disorganized if not maintained properly
Alert configurations take time example user has to plug and play sometimes
Likelihood to Recommend
What i really like is the APM of the Datadog, typically my project consists of many microservices and recently upgraded for kubes , it helped up to maintain to track and trace the request
Dashboards setup takes some time to get your hands dirty but once you do it, it is very helpful.
Single place to see everything, breadt of the tools available cover pretty much every use case. We focus on the most basic ones like logging/tracing/metrics, but also rely a lot on database monitoring and ci visibility.
Pros
database monitoring
alerting
dashboarding
Cons
cost management, its so hard to understand how much something will cost until you start using it
cost management, ,its also hard if not impossible to create alerts for spend
docs, they need to be kept up to date better
Likelihood to Recommend
Datadog is a great tool for pretty much everything, but past a small volume of data you need to be really careful so costs do not eat you.
if you cannot sample down data, it can get pretty expensive and fast.
I'd recommend Datadog to any company starting on their o11y journey, but would recommend them to go the opentelementry way so if they need to switch some services its easy to do so.
Datadog is used as a monitoring and observability platform to gain real-time insights into performance, health, and errors within our infrastructure. We use infrastructure monitoring, application performance monitoring, database monitoring, and real user monitoring on a daily basis to check the status of our web and mobile applications. Datadog integration is at the front of any new application that we build and use.
Pros
Unified monitoring - it is easy to find errors and issues from the database all the way to the user experience and vice versa.
Easy integration - set up was quick and painless and there are many out of the box integrations that require just a few button clicks.
Dashboards/monitoring - easy to build complex monitors and dashboards to account for the smallest of details.
Cons
Pricing - pricing is complex and can become expensive if you don't keep an eye on it.
Logs - equally as complex and not easy to manage when you have high log volume.
Mobile app - hard to use and doesn't have a lot of items.
Likelihood to Recommend
Datadog is great on modern applications and will serve users well when adding monitoring, logging, and other integrations into these applications. Datadog is not as easy on legacy (or monolith) applications that have years of tech debt. These older, monolithic applications are often hard for engineers to work with, so adding in modern monitoring solutions into something that is already challenging is a challenge itself.
VU
Verified User
Team Lead in Engineering (Human Resources company, 51-200 employees)
Datadog served as our end-to-end monitoring solution, covering applications, databases, servers, and more. We extensively utilized Datadog's modules to monitor our entire infrastructure, addressing various use cases such as identifying latency in applications, databases, and networks. The ability to pick and correlate small metrics proved valuable in enhancing user experience and achieving a better Mean Time to Resolution (MTTR).
Pros
Application performance monitoring which is related to collecting traces in the application
Synthetic monitoring which is really cool feature of record and play with prior no programming knowledge is needed
Notebook where you can share the documents, SOP or any kind of documentation or notes with your team
Cons
Synthetic agent for private location as they have bug and this agents can't handle SSO
Also in synthetic monitoring while recording scenario everytime you have to go for incognito mode as there are some issue related to cache.
Database monitoring need some improvement in terms of monitoring oracle based databases
Likelihood to Recommend
I guess Datadog has performed really outstanding in almost every sphere and every module of Datadog is really good. One of the cool feature is calculating SLA and SLO, based on that you can setup even alerting and notification.
The area where it didn't perform well is synthetic monitoring private agent to monitor SSO application as it's failed to captured metrics due to limitation and bugs.
DataDog is our OneStop solution for all our observability and monitoring stack needs for monitoring the Backend, Frontend, Database, Proxies, Servers, and all our other Infrastructure Components. Also has the flexibility to create, share and use Custom as well as predefined sets of Dashboards that help us troubleshoot many severe as well as production system-impaired issues. Fed custom metrics in Dashboards like Response time, data per environment, site availability, error rates, success rates, error as per error codes, and request tracing. Database dashboards can also give us many details like slow queries, reading high throughput, writing high throughput, and some recommendations to make our platform faster and more robust.
Pros
Create Dashboards as per application, environments, and Custom metrics in one panel.
Log aggregation, one-stop Application monitoring tools for the whole infrastructure.
Playbooks, SLA definition, success and error quotas, request visualizations.
DB monitoring, Serverless stack monitoring.
Alerting of Production incidents so we can quickly resolve the issues on time.
Cons
Handy Documentation.
Make Cost effective.
Likelihood to Recommend
DataDog Is well suited to all of the Infrastructure Monitoring Solutions, DB monitoring, and other Network monitoring also. It's not well suited because it cannot give perfect Infrastructure recommendations for our use case but also For example: If we are using AWS DB to monitor performance insights then Datadog is less effective there because AWS gives very niche recommendations.
It is used to monitor multiple hosts for networking, compute, and memory along with application-level metrics (errors, performance, etc). This allows for site reliability engineers to determine the cause of errors and gain other data for root cause analysis (RCA).
Pros
Visualization
Network mapping
Error finding
Cons
Navigation to new areas can be counterintuitive.
Likelihood to Recommend
A situation in which you have multiple applications deployed to virtual machines (VMs) with the need for application-level performance, errors, etc.
VU
Verified User
Engineer in Information Technology (Information Technology & Services company, 501-1000 employees)
DataDog is currently being used by Wayfair's Monitoring, Network Ops, and Critical Incident teams to provide a variety of data to provision, manage and maintain the existing infrastructure for internal and external sites and applications. It is a critical input to how these teams identify, tag, and respond to alerts within the company. We use Datadog for the current day to day provisioning tasks and well as prior/after any maintenance.
It has a key plug-in with ServiceNow and this is a key integration at Wayfair. ServiceNow is used as the reporting platform to store and index all DataDog alerting. Further, we have built several notification systems to piggy back off DataDog alerts for clear and wide-spread notifications to all stakeholders of a particular stack of technology.
The visuals and simple interface provides a clean and ready-made application in which to address the business and technical needs, for a rapidly evolving infrastructure. Wayfair has a tremendous amount of growth and we rely on this tool to grow and adapt alongside us. The Platform as a Service approach has proven to provide what we need, without the dependencies to support the infrastructure, which is a big bonus for our teams.
Pros
APIs, the ability to interact with the data we pull into data dog is key. We port the information over to Servicenow, so the ability to pull everything into DataDog, then Servicenow, is a key component of our success here at Wayfair.
Simple Interface - clean, useful, effective. Allows users to use DataDog for one reason, get work done.
Lightweight agent on hosts
Cons
Reducing the delays in monitoring agent response time
Configuration of aggregate metrics, allow us to compile / aggregate data faster and with more ease
Improvements in Security policies
Likelihood to Recommend
DataDog is well suited for an environment that requires low-level investment on supporting infrastructure, a cloud-based approach or something that is priced at a fair enough level where you can grow the business with your footprint without sacrificing on features.
