The capabilities we use are unique for each use case. They can be combined in various ways to provide the full observability coverage needed to maintain stable operations in order to become more proactive. 

Our organization uses both site/service reliability for backend and frontend services. Custom monitoring and dashboards that can be dynamic and reused for multiple teams. 

We continue to increase the size of our footprint as we get more and more positive experiences.

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We are currently in a POC and do not own Datadog at the moment. 

So far, there have been a few issues due to security. There are two main security issues. 

The first is moving data off-prem. This has been resolved to a point (filtering logs, etc). However, there is still an issue with moving a JFR as a JFR potentially contains data that is not allowed off-prem.

The second security issue is more internal, however, the main installation requires root access or using an ACL. Our company does not use ACLs on our Linux platform. This is problematic since the install sets a no-login on the Datadog user.

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It hasn't improved the way our organization functions yet, because there's a lot of red tape to cut through with cultural challenges and changes. I don't think it's changed the way we do things yet, but I think it will — absolutely it will. It's just going to take some time.

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We have an API that serves as a critical aspect of our system for generating new requests for us to process in service of a patient. This service has many tentacles, and it was always hard to track down how issues from this API are affecting things downstream. Since we've added more instrumentation in this API, Datadog has changed our status from a reactive posture to a proactive one.

It has also served as a prime example to other applications on what the benefit of a well-instrumented system is for that application and other applications around it. Due to this, more and more people are using Datadog.

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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. 

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We could detect outages on particular websites or problems in specific locations. If I had paid for the full solution, I'm sure I could get a lot of value out of Datadog.

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Using the product has caused a paradigm shift in how we deploy monitoring. Before, we had a one-to-one lookup in ServiceNow. This wouldn't scale, as teams wouldn't be able to create monitors on the fly and would have to wait on us to contact the ServiceNow team to create a custom lookup. Now, in real-time, as new instances are spun up and down, they are still guaranteed to be covered by monitoring. This used to require a change request, and now it is automatic.

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Prior, the team only had Instana, and few people used it. The main barriers to entry were the access (since it was not integrated into our SSO) and the user experience, which made it hard to follow. We had an on-prem version, and it wasn't the snappiest. The APM has made observability and tracing more accessible to developers.

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It is easy to implement and scale applications with standardized visibility, monitoring and alerting

We get a lot of value out of passive and active monitoring. While different teams across our organization have used different services (metrics, logs, APM, RUM), almost all teams have been able to use the dashboards to report and track high-level metrics and active monitoring. 

Active monitoring (static monitors, threshold monitors) is great. We get a lot of value out of anomaly detection as well. SLOs and monitoring of SLOs have been another value add for our organization.

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Since we integrated Datadog, we have had increased confidence in the quality of our service, and we had an easier time increasing our delivery velocity. 

We have seen time after time that the monitors we have carefully created based on all ingested data are detecting issues quickly and accurately. 

This means we allow ourselves to manually test things less frequently. We have also had an easier time investigating application errors and slowness using Datadog's APM and log explorer products which allow us to introspect any part of the system, in its execution context.

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The product has created a paradigm shift in how we deploy monitoring. Before, we had a one-to-one lookup in service now. This wouldn't scale, as teams wouldn't be able to create monitors on the fly and would have to wait on us to contact the ServiceNow team to create a custom lookup. Now, in real-time, as new instances are spun up and down, they are still guaranteed to be covered by monitoring. This used to require a change request, and now it is automatic.

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Datadog has been able to improve our cloud-native monitoring significantly, as CloudWatch doesn't have enough features to create robust, sustainable dashboards that are easily able to present all the information in an aggregated manner in one place for a combination of applications, databases, and other services including our UI applications. 

RUM monitoring is also something we didn't have before Datadog. We had Splunk, which was a lot harder to set up than Datadog's custom RUM metrics and its dashboards.

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It has democratized our logs and metrics, allowing all engineers to have insight into how our apps perform. It is also extremely helpful when debugging issues. 

It would be very difficult to debug issues without aggregated logs and APM traces. 

It has also definitely saved us some money since we can keep an eye on our running infrastructure in an easy-to-see way, rather than a less friendly CLI. It has been a very big help!

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It is now way easier to search in one place rather than across all of Cloudwatch (and needing to know log groups, etc.). 

Primarily, we run several separate deployments of Rstudio Workbench, which has its own logs that would not be picked up via Cloudwatch. 

We own several dozen of these servers. We used to manage instance logs manually. 

Datadog allows for much better visibility.

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My team has a 24/7 on-call schedule where we need to be ready to handle and mitigate incidents with the platform at any moment. 

We have countless monitors set up on Datadog that alert directly to our queue using an email that generates a ticket. 

The actionable steps for each type of monitor and its associated incident are easily included in the alerts whenever something is triggered. We generate links to the Datadog monitors and can instantly drill down into what went wrong and for how long.

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Thanks to the logs, we manage to make better reports through Jira and also to trace the request with more facility than we would be able to do otherwise. 

Since there are many teams in my company, the fact that we can share the trace of an error, for example, together with all the information about the log, we are able to save a lot of time when it comes to communication between everyone.

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The service catalog helped improve our organization by giving a good view of the flow for our microservices applications. It's important when we have different developers working on different services and having the trace and log features help the on-call person locate the microservice.

The application performance monitoring has also been useful. This module had a few functionalities that we needed for the application health check. This needs to have some more features to consolidate the view in one tree. We may need more of a one-stop shop on top of the dashboard, and that is missing in Datadog. We'd like to be able to scrap our existing monitoring tool.

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The organization changed from having a team to operate different tools and providers to being a team worried about enabling and creating different dashboards, alerts, and automations in order to reduce downtime and increase the visibility of all the products, systems, and applications used. 

We moved from a full operation team to a team that adds value to IT, finance, product, back office, and any other team that requires correct information about the services provided while providing the possibility for them to create their own views and dashboards.

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So far, the solution works very well and solves most of the problems we have. Currently, we are trying to integrate the trace ID into Datadog and correlate the logs and metrics. However, Datadog is not supporting the spring-generated trace IDs, and they are not shown in the Datadog UI. It works in reverse. This means Datadog injects the DD-specific trace ID into the application logs, and those logs can be in other tools, for example, Cloud Watch and Splunk. 

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Previously, we had no visibility into the architectural layout of our infrastructure. The UI of Datadog has allowed for increased visibility and access to broken or underperforming resources or critical pieces of infrastructure. Beyond this, it has allowed us to identify areas where we can optimize cost in our cloud infrastructure.

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The solution has helped with our POV phase.

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The solution has helped out organization gain improved visibility.

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It has allowed us to see into our systems with ease. We are a very small startup (Less than 30 people, and most of them are in sales and marketing). 

When it comes to managing systems, we just don't have time to do everything. However, Datadog has allowed us to do much more with fewer people and still sift through our data with ease. 

We hope to start using the APM feature set to extend this to our dev teams as well.

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Datadog helps us detect issues early on and helps in troubleshooting. Creating Service Level Objectives and defining monitors is helping us to stay on top of potential issues that might affect our users. 

We take advantage of Application Performance Monitoring to ensure our applications are working as expected, and our users can get the healthcare they need at a price they can afford. 

Synthetic monitoring also helps us in testing our application in different browsers.

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Datadog has helped us a ton by allowing us to set up a multitude of easily configurable alarms across our tech stack and infrastructure. It doesn't matter if it's in AWS Lambda or a Docker container in AWS EC2, Datadog's intuitive interface makes alarms incredibly easy to configure, reducing our resolution time for incidents.

A lot of the value comes from how frictionless the integrations are. Adding in a Datadog agent or flipping a switch on the Datadog UI to start streaming Lambda data makes the product so incredibly appealing for my company.

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Cost and performance optimization were the major enhancements for our organization. It gives us platform monitoring for the services that are deployed in AWS for a better way to monitor the services (pods, cost, high availability, etc.). With this product, we ensure that observability and also keep customer services uninterrupted. We host the data pipelines between the cloud and the on-prem. Datadog helps to ensure better services. We find we can report issues based on the metrics reported over it.

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We have way more observability than what we had before - on the application and the overall system. That includes the GKE cluster, nodes, and pods. It's helped with our cloud-run instances, databases, and data storage.

We also started observability in the CI pipeline to measure our CI performance, as it was a pain point for us. We are aiming to do incremental deployments and releases, and the bottleneck so far has been our CI performance. The visibility on which actions or functions take the most time allows us to pinpoint and focus on improving configurations on these.

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The solution has improved our organization from a market perspective. We have multiple departments and need some time to gather that data from a grouping point of view. Grouping that data via tag or seeing the separation is easy. In addition, it provides metrics and insights for senior leadership to have a high level of usage and cost. Application teams have better insight into their application, outages, when to plan for patches, updates, etc. Also, they have a better understanding of where the data gaps may be.

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At my organization, we have plenty of microservices written in different languages. Different teams prefer one or the other framework or library within those languages.

With Datadog, we can get in a single line and march in the same direction; our logs and metrics are collected in the same fashion, making it easy to find bugs or integration problems across services and understand how they interact with other systems.

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The greatest impact it has had is on the ability to democratize observability and put monitoring into the hands of the people. Teams can quickly get the information they need, without needing a bunch of training, since the UI is super intuitive and easy for beginners. This helps reduce time to resolution during incidents and gives context to developers quickly and easily. Context is really important since seconds matter when the ship is down, and you don't know why.

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I resell all solutions in Datadog, so all features are important for our customers.
We are the biggest Datadog partner in Brazil, and we would like to expand our MSP environment.

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The solution improved our organization with:

• Data-driven decision making
• Dashboards we can share with our customer success team
• Dashboards we can share with our sales engineers
• Help during incidents
• Help with preventing incidents
• Integration with PagerDuty.

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The solution has been useful in generally ensuring that teams are able to better visualize and think about their application's impact on data centers/cloud performance. Having centralized tooling for observability means that each team can be on the same page when discussing monitoring. 

There have been some issues where teams have been unable to find metrics within the tool properly and some behaviors with the tagging and grouping functionality that seem not to be as easy to understand as one may expect. That said, overall, the experience has been one that is positive.

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While my team is relatively new to Datadog, I already see immense value in switching over to Datadog as the primary APM and NPM tool.

The arsenal of features it offers is bound to come in a clutch when facing production issues, and when finding out what went wrong is crucial.

The network map has helped to figure out the golden signals and optimize the infrastructure.

The synthetics have helped ensure the high availability of arch functions as intended.

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We use Datadog mainly for debugging purposes. For example, we use it to navigate where the code trace is when an issue arises due to its ability to search through the logs. 

We also use it to address user queries. Sometimes users would ask us a certain question concerning our codebase, we use Datadog to track the code stack and also use time monitoring to get an idea of the time frame around when the use case happened.

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The product has offered increased visibility via logging APM, metrics, RUM, etc. We've gone from almost nothing to something that didn’t take a lot of time to set up. It has been great since we had so little time to spare.

As a startup, we have limited resources, and no one has enough time for anything. The fact that there are so many easy integrations and configurations by YAML makes everything easy to set up without needing a full-time employee. Instead, we're just configuring monitoring solutions which are very desirable.

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Our usage of Datadog has allowed us to improve our observability at great lengths. We have been able to track pain points more easily with it, and be able to define custom metrics to track our user's usage of the features we roll out.

Being able to generate dashboards has given higher management a better view of our teams' work and has allowed for better client information by our sales team as they have a more transparent way ofdealing with our upcoming features.

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We are still working through fully rolling the service out to our employees. Those that have so far begun using it have found that it decreases the time required to investigate and troubleshoot production issues. 

We have found that we're able to get in and out of troubleshooting issues much more rapidly, which in turn, of course, enables us to spend more time on our products. We are still investigating other areas where other Datadog services could potentially be injected into our workflows.

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We are still taking baby steps with Datadog. Hence, it's hard to come up with quantifiable information. The most immediate benefit is aggregating performance metrics together with log information. Having a better understanding of observability will help my team focus on the business problems they are trying solve and write code that is conducive to being monitored, instead of reinventing the wheel and relying on their own logic to produce metrics that are out of context

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Datadog simplified my ability to watch easily and add monitors on any metric emitted by any team at my organization.

Datadog APM immensely improved our ability to understand the reasons behind production issues. Its ability to navigate across services seamlessly to understand the time spent at each critical stage of a production request is helpful. This, combined with Datadog's historical ability to show business metrics aside, helped get more powerful insights much more quickly.

Datadog's seamless integration with Slack and PagerDuty helped us to receive alerts right to the most common notification methods we use (our mobile devices and Slack).

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It's good to have a single location for all the logs. If you have logs coming from a whole lot of sources, it makes it hard to find where the problem lies. 

We had to spend a lot of time logging into various systems and pursuing a billion different log files looking for something that stands out as a possible cause of the issue. That can take a lot of time and doesn't give much visibility into the possible interactions between systems.

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It has drastically reduced the amount of time we spend on debugging issues and tracking down the root causes of incidents. What might have taken days or hours with separate vendors in the past (or even single vendors with terrible UI) is now quick and easy. 

We've often gone from detecting an incident to identifying the needed fix within ten minutes or less and covered multiple domains like APM, Logs, Database performance monitoring, etc., in just a few clicks. This is extremely powerful.

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Datadog has made it much easier to have a central place for people to look for logs and made it much easier to notify them of any elevated error rates or failures. 

It is also easier to get high-level views of platform health, whereas looking directly at AWS tends to provide very specific insight into particular surface areas or products. 

By having the whole team onboard onto Datadog, we also have a single source of truth that everyone can use when triaging and resolving incidents that occur across any surface area.

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Developers are able to see how code is running in production, where this was mostly opaque previous to us implementing DataDog. We are able to emit custom metrics that are specific to our business, and the built-in metrics have also proven useful. Having a wealth of information has helped us investigate outages, and having historical data helps us tune our system.

DevOps engineers are able to put sensors around our system to proactively detect problems, whereas before, our engineers heard about problems from customers. Logs are easier to find for developers.

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My current company didn't have very good monitoring in the past. We had been using basic CPU monitoring. We have been able to set very specific CPU and memory alerts, at the very base level, then we started to pull real business value, like 99th percentile response rates for our API calls. 

It has turned into an operational dashboard. If you felt something is going wrong, you can immediately open up Datadog. It has been our go to application because we know the answer will be there.

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Monitoring has been better and easier since we started using the Terraform infrastructure.

APM has been easier as we had to enable it through the CronJob directly.

Profiling has made it easier in terms of getting many insights into the code.

The logs are the most valuable and the best solution. Datadog can help us to solve any slow queries or database-related errors.

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We are providing managed services to our customers across multiple industries. 

Datadog is key to delivering these services. It brings in observability, monitoring, and alerting capabilities - all of which we need to operate at scale. 

We operate custom cloud native workloads as well as ISV products such as Atlassian Jira or Confluence. 

Integrating Synthetics, infrastructure, and application performance monitoring, as well as piping all logs through Datadog, help with getting alerts in real-time.

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The solution has improved the organization by providing good insights into app performance and offering good dashboards.

With it, our company can track fixes and track test coverage. We get confidence in the fix/improvement and are able to provide a response.

I've been able to present data to the team/ management based on the team's dashboards.

It's helped us when we've needed to monitor users and what they access or needed to identify security loopholes and attack patterns. It can help identify and quickly respond to issues.

Datadog allows us to identify pushbacks, and get insight into application components (how they stack up with each other). When we need to know which component, libraries, code, and teams to alert, we can raise and track incidents to completion and gather data for reporting and post-mortems.

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Using Datadog metrics has helped the organization a lot in many manners. With one centralized monitoring place, it's a lot less effort to keep track of the system and applications' health. 

Using this also helps teams be proactive in dealing with any issues before they get escalated by customers. 

Lastly, having so many integrations makes the DevOps and SRE's lives a lot easier when automating the detection and resolution of any issues hidden in the system or applications. Overall, it has helped a lot.

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The solution has offered increased visibility via logging APM, metrics, RUM, etc. Going from almost nothing to something that didn’t take a lot of time to set up has been great since we have so little time to spare

As a startup, we have limited resources, and no one has enough time for anything. There are so many easy integrations and things configurable by YAML making everything easy to set up without needing a full-time employee. Just configuring monitoring solutions is very desirable. 

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Datadog has given us near-live visibility across our entire cloud platform. We are finally in a state where we are alerting our users about degraded performance well before the helpdesk tickets start rolling in.

We are making major architectural decisions based on the data we are getting from Datadog. It also gives us an idea of where the complexity really lies in some older, monolithic apps. 

We have used the APM endpoint monitoring to prioritize work on slower endpoints because we can see the total count, as well as the latency. That has been a big driver in our refactor work prioritization.

We have struggled to get more business-centric measures in our code to surface actual business values in our reports, but that is our next initiative.

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Datadog provided us the tooling to help us effectively monitor, troubleshoot, and operate the AWS platform, including Server, Network, Database, and key AWS Services. It highlights detected problems and anomalies and provides best practice recommendations, expedites root-cause analysis, and performance troubleshooting.

Datadog provides analytics and insights that are actionable through out-of-the-box visualizations, dashboards, aggregation, and intuitive searching that shortens the time to value and account for our limited time & resources we have to operate in production.

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The solution it has improved our organization by expanding the awareness of issues and alerts beyond SRE and really empowering software engineers at a team level to make changes to monitoring and incident responses.

There could still be more training to bring this even further. A lot of the time I get into Datadog and it's already an incident and I am not in the right mindset to learn about the product or set alerts up.

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So far, we are just in the evaluation stages so it's hard to say how it's improved out organization. However, one positive impact it had is it's been just showing us an example of how to build in observability, metrics, tracing, etc., in a better way. 

Even if we don't end up using Datadog, it revealed problems and optimizations to us that weren't obvious before. One potential reason why it may not help us is that we have strict rules around log parsing and may not be able to send it to an external organizaton for ingestion/processing.

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It will solve a lot of our problems. We have different tools for each of them in our organization; they are open-source and therefore not very well maintained with there is no customer support. 

Having an industry-standard product such as Datadog would be ideal for us as we are short on manpower. Since this is a managed all-in-one product with readily available support, we will be able to focus on application logic rather than figuring out why a tool isn't working.

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The ability to easily drill down into log queries quickly and efficiently has helped us to resolve several critical incidents so far this year, and we heavily rely on a series of dashboards showing us various queues and load on CPU and memory for servers. 

We also have a view of the information required when we begin the patch and/or upgrade processes. 

I've also set up several monitors to alert the Site Reliability Engineering team when various metrics show a server might be reaching capacity. We use it to send an email suggesting we increase the size of the cloud instance.

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We use the application for our application monitoring, data security monitoring, and log management. It helps us to track issues proactively instead of reactively.

It helps us better manage our logs.

We can effectively track down issues.

We have dashboards that give us an overview of our environment.

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This tool is the sole purpose of my company and the solutions we provide around it. Enabling customers to manage their content in a fast, reliable, and highly user-friendly setup has been critical to our success. 

Offering our product at SaaS, PaaS, cloud, and on-prem editions has enabled us to provide a solution for all types of customers. 

The platform appeals to companies spanning many industries on a global scale. 

Being based in SUI allows us to secure both the EU and other companies more easily.

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It has provided visibility with ease of implementation and allowed multiple teams to quickly onboard it. This provided a standard way to approach observability and visibility. 

Monitoring rules and alerting thresholds can also be set and exported to other teams for use. 

There is an issue with federated dashboards, as multiple teams running on different Datadog instances cannot use features like the service catalog or easily switch between services in a long business flow.

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If we have a large load for users using our basic Datadog, it will immediately fire off an alert notifying us either something's wrong or not. It provides us insights on our calls to other services, such as how long each call is taking and what is the whole stack trace.

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Datadog has allowed us to rapidly spin up alerting and monitoring that helps our incident responders get alerted quickly when our SLOs are in danger and helps to quickly resolve issues. 

It is the single most important tool we have from an SRE perspective. 

It also provides us with an easy way to get information at a glance for all of our services through APM and create unified dashboards that track our underlying resources, such as databases, queues, etc., alongside application data. 

It has been invaluable to our organization.

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It has helped us build pipelines for ops review and other functions.

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We are looking into a lot of modules. We collect all data logs from all operating systems, including Windows, Linux, VMware, and bare metal data centers. We also automatize the installation of the agent on servers. 

We're developing POCs for APM and security modules. We'll also have a unique portal for observability. This will make it easy to troubleshoot. 

The most valuable aspect is for us to have everything in one place.

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We've been able to glean from the monitors what servers are down, and can alert the team in Slack. Knowing what we need to do next is what we would like to move to, so seeing the power of Notebooks is key. We also have several other services that we are underutilizing (logging, error tracking, etc.) that would be better housed in DataDog since it gives us more visibility into linking all of the things together into one cohesive picture.

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Before Datadog, all we had to go on was the gut reaction of the old guard on our team. While useful, the reactions and inherent knowledge only benefited a few folks.

Datadog has allowed us to create comprehensive dashboards and proactively send out alerts. We used the knowledge of people very versed with our products to help set up the platform and have since benefited from that.

The operative word here is visibility, and we've seen a huge improvement in that.

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We use Datadog quite extensively. I primarily work with APM traces and logs to debug issues and unblock myself in my day-to-day role. I have found the traces and spans most useful in providing details about why certain services are performing poorly.

Datadog provides a lot of value in terms of adding monitoring and observability to our app. There are so many different solutions, it is sometimes difficult to gauge where to start, and I sometimes miss a lot of functionality (such as the very useful error-tracking dashboard mentioned in my review above).

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Elastic container services have improved our organization by allowing us to deploy our application. While this problem was not solved by using the elastic container service since we had a previous solution on a different provider, the ease and flexibility of deploys have greatly benefited the ops team and the overall engineering organization. It is easy to use. However, there are many out-of-the-box features like metrics and seeing task definitions that make life easier.

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The solution has helped our organization with custom events to track specific cases.

It's helped with monitoring time spent on views and events triggered. For example, for one of our products, we have created a custom dashboard that lets us track all the custom events as well as multiple entry points into the same part of the application.

Knowing the entry point helps us choose which part of the program should be improved. It's collecting important data about the overall usage of each module within our application. 

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My team focuses on the backend. Day-to-day monitoring includes observing metrics such as the CPU and memory until it gets too high. This solution provides an alert during the metric collection.  

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It helps us to be more proactive. We can help customers with their e-commerce applications for any networking issues. We can also help them in any area from a development standpoint. It could be a non-prod environment where they're going through testing and various functionalities. It helps them be able to be more successful with their deployments.

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Datadog gave us awesome visibility across all of our applications.

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Datadog shows all the logs for the services, and it is very useful for troubleshooting.

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This solution improves our organization as now we have higher visibility into our application that we otherwise would not have. 

Since the Datadog agent comes in three forms, agentless, scraping, and through the API, it is very flexible. It is this flexibility in how to report our logs that keeps our logs centralized and organized. 

One major drawback of Datadog is the cost. Sometimes we set up flows in place to monitor resources that end up logging more than we thought, and the bill is too high.

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We have been able to be a more confident, knowledgeable, and capable team when everything is being ported into a centralized format. Beforehand, knowledge was isolated to individuals. Knowledge in terms of what information represented and where it was led to a lack of confidence. By having everything in one place, rules out that confusion and allows us to respond better to issues.

It also allows for personal growth as our team is learning the application from the ground up, and each person is enhancing their own skills.

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This spectrum of solutions has allowed us to track down bugs faster and more rapidly, which allows us to limit revenue lost during downtime. 

It also allows us to accurately record and project current and future revenue by measuring the application's metrics. This way, my team can accurately and rapidly create reports for upper management that are easy to read and understand. 

Datadog is also easy to read by non-technical personnel. This way, if there are any erroneous readings, everybody has a chance to find them.

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It really provides a lot of visibility in terms of how our software is working. If there are any problems, it surfaces them right away. We get alerts in Slack. It's really an essential tool for a company that provides software as a service.

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It has empowered all our platform engineers with a very powerful and easy to use monitoring system. Most of our platform organization is now involved in monitoring. Previously, only a handful of platform engineers were involved, because Graphite and Sensu were so cumbersome to use.

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The solution has helped out organization by allowing us to ingest data from various sources to monitor log metrics and enabling alert mechanisms to notify teams if something goes wrong.

Datadog agents act as an integration to different services, providing easy access and management.

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The RUM solution has improved our ability to triage faster and hand more capabilities to our customer support.

The RUM is implemented for customer support. It can quickly route, triage, and troubleshoot support issues that are sent to our engineering teams. 

Customer support can log in and start troubleshooting after receiving a customer request. The replay and RUM help pinpoint the issue. This functionality is combined with APM and Infra trace to be able to look for the cause of the issue. Incident management is leveraged to open a Jira ticket for engineering, and it can integrate with ITSM systems and on-call as needed.

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It makes the system easier to debug. 

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Helped to reduce production issues in a defined timeframe

Helped to refine UX

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We implemented Datadog around the same time as the company was growing from 30 to 150 people. Before that, we didn't have a standard stack for monitoring. Each team used their own logging solutions, metrics were missing or non-existent, and it was impossible to correlates metrics collected by different teams. DataDog provided us with an out-of-the-box solution that allowed us to focus on putting in place practices and processes around monitoring, rather than focus on implementation details.

Every squad is now confident in their ability to quickly identify and diagnose issues when they arise.

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Observability is something that a lot of Companies are trying to achieve. Having a clear view, not only of our infrastructure but our apps and services as well, has brought a great added value to our customers.

For a logging solution, we use to have Papertrail. It did the trick but having a single point that manages and indexes all the logs is a BIG improvement. Also, having the option to generate metrics from logs is a game-changer that we're trying to include in our monitoring strategy.

I would like to say the same about APM but the support for PHP seems to be somewhat lacking. It works but I think this service could provide us more information.

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The solution has provided us with a lot more insight into service-level metrics, which is especially useful with APM/tracing. It gives us all-up dashboards and alerts to assist with incident management.

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The solution affords us many uses when it comes to troubleshooting and application. Should we encounter issues while troubleshooting under load, we can take advantage of Datadog usage metrics to see which method or area is having difficulty, in order that we may resolve the issue. 

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Datadog has improved our visibility into infrastructure topology and performance. It provided a simplified view and ability to drill down to system performance, process usage, and logs.

We were able to set up monitors for infrastructure and applications, as the metrics were readily available in the platform. Fine-tuning monitors is very easy and the ability to configure monitor alerts with details on how to resolve the alert is a key value add. 

Integration with PagerDuty, teams ensure timely alerting. PagerDuty integration bring tags from Datadog to PagerDuty, which is very useful in routing incidents to the right service

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It lets us react more quickly to things going wrong. Whereas before, it might have been 30 minutes to an hour before we noticed something going on, we will know within a minute or two if something is off, which will let us essentially get something back up and running faster for our customers, which is revenue.

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We can build dashboards as fast we roll out new systems, which can be fast.

We use standard and custom metrics for every new system we roll out for 360 degree visibility into our systems.

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With Datadog, we were able to gain observability in our system. 

The installation step is pretty straightforward. 

It's easy to use by non-DevOps users. For instance, our engineers do not interact with K8s often; therefore, it is hard for them to debug. However, with Datadog, they are able to view their containers and deployments with a single click. 

We also heavily use the tags to help us identify who the service owners are. This is super useful when we need to track owners for patching or pick up new features we implemented.

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We are using it to indicate issues and alert our operations team. With this, we better monitoring of our applications and logs.

However, the main difficulty is implementing the solutions in our Kubernetes cluster, separated just as logs to the specific namespace as the volume of logs is tremendous.

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The product has provided our company with improved observability, which has helped make the incident response more targeted and quicker.

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The solution primarily has helped the organization by helping us better understand the health of applications, modern environments, et cetera. 

We can see the health of the technology stack and services. We can also integrate multiple metric sources, security, business data, and much more. It centralizes data and unifies monitoring in one place. It's Helping reduce costs with other solutions, and also reduces costs with teams that might waste time with manual troubleshooting.

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It has reduced some challenges, and it has optimized the time spent on monitoring and management activities. It has improved the visualization and the ability to monitor and control.

Datadog increases our visibility. It puts all the data in one log so that we can use that log in a contextual manner. Some operational optimizations definitely have happened with this solution. In general, the user community is happier than before. We are basically asking them every quarter how happy they are on a scale of zero to five. That needle has moved but not significantly. If it was 3 earlier, it is still less than 3.5 now, but the user experience is better than before. 

Because of this monitoring, we are empowered to publish certain dashboards for the business folks as well. We have three to five senior business folks who are looking at their investments and operations optimization. They are basically putting money on the table for this.

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It provides more cloud data. They tend to just get the way a service would be designed on the cloud. Datadog can handle a server disappearing and account for it, but they will kick somebody out. 

The ease with which we can filter, use metrics, and give accounts to customers, then let the customer filter, set up metrics, and alerts. This has been a big win for us. This can't be done with a lot of the other platforms. This has made things considerably easier. Where we used to get "What's my performance?" Here, have access. Go nuts. Tell us if you need it. Now, our customers no longer ask us for all that, as they want to go do it themselves. This has made our lives infinitely easier.

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With this product, we get more visibility into our K8s clusters and more intelligent alerting.

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It has enhanced the performance of my team.

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Previously, we had monitors scattered with different places and products, making troubleshooting harder and slower. Also, logs and monitors were on different platforms, making it harder to put the infrastructure puzzle together.

Datadog documentation on web pages has improved a lot and is pretty easy to follow and find.

Additionally, integrations with, for example, GCP, Network, component, and Software providers are much easier as everything is now centralized.

API and notification integrations are also a great benefit for our organization.

Datadog is listening actively for customer feedback and develops improvements for us effectively.

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The Datadog suite has allowed us to easily integrate log collection into all of our services and quickly detect unexpected changes in system data to declare security incidents.

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It is a cloud monitoring tool. I believe it would make things easier for our clients in terms of monitoring.

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From our customer's and our perspective, it has not brought much change in terms of the process. Datadog did not change anything about the process. It only provided a single tool instead of having to use multiple tools.

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Keeping track of incidents makes it possible to share learnings and follow-up. It is also required for SOC2 compliance.

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We have a better grasp of what is occurring during the deployment cycle. If something fails, we have an idea what has failed, where it has failed, and how it failed to better mitigate the situation.

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We are working as an SMS segregator. Therefore, we send a lot of SMS message to customers. This product holds one of the most important dashboards for our traffic from each server or cluster on our Gateway. It gives us very good information, mainly for the operations team and other sales guys, about what each account is sending, how often, etc.

Using the data, our operation teams works with the dashboards to get their statistics, analytics, etc.

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I've been using this solution for a few months at my current company as a member of the Kubernetes team we use Datadog to provide monitoring and telemetry for our team and our customers.

This solution has improved our organization by giving us deeper insight into what's running in our clusters and their performance of it.

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At other places I've worked, we had to rely on different types of infrastructure -- Datadog does all that. Its very time saving. Datadog has alerting events and metrics all in one place; This was a huge plus, other solutions were trying to treat monitoring as a multi-faceted problem. Datadog treated it as one problem. Also, We no longer use Nagios for alerting, we use Datadog’s alarms, and then we push the data into PagerDuty. View full review »