Datadog Reviews

We primarily use the solution for centralized dashboarding and telemetry viewing for teams across the organization. 

We're focused on ensuring that both development teams and leadership can reasonably gain insights into the status of various systems. 

At the end of the day, managing various dashboards and metrics aggregators like Prometheus, Kubernetes server, AWS Cloudwatch, and Grafana have lead to some confusion, and we've had issues with teams not knowing where their data exists and where they can view their system metrics. 

Datadog has proven to be easy to set up and legible for both development and operational teams.

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.

The dashboards have proven most helpful in ensuring that teams can track the performance of their apps. On a more practical scale, the alerts have proved invaluable for triaging and bringing services back online.

Being able to tie the alerts generated through Datadog monitors has allowed us to quickly and effectively respond to infrastructure and software issues that would have otherwise hamstrung the organization and prevented us from accomplishing our day-to-day tasks. This is naturally invaluable.

I'm sure that this is said all the time, however, the pricing model has led us to restrict the usage of the service. If there were a more cost-effective manner of deploying the tool, we'd be more likely to adopt it more widely. 

Aside from the cost, the nature of the tagging and grouping features within the monitoring dashboards have often caused headaches when creating new dashboards for aggregate services and infrastructure stacks. It would be nice to ensure that this feature is supported long-term and brought with easier accessibility.

I've been using the solution for three years.

Datadog is easy to use and generally looks great from a customer standpoint. The ability to export metrics all into a central location was crucial.

Datadog is very expensive for smaller organizations. The pricing model might be restrictive until the organization reaches a certain size.

Primarily we did an evaluation of other providers, such as AWS and GCP, outside of in-house solutions.

We primarily use the solution for application monitoring (APM, logs, metrics, alerts).

It's useful for active monitoring (static monitors, threshold monitors). We get a lot of value out of anomaly detection as well. SLOs and monitoring of SLOs have been another value add.

In terms of metrics, the out-of-the-box infrastructure metrics that come with the Datadog agent installation are great. We have made use of both the custom metrics implementation as well as the log-based metrics which are extremely convenient.

We also leverage Datadog for use of RUM and want to explore session replay.

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.

The APM and tracing provide visibility and the ability to get right to root cause issues while being able to deploy new services without much need for custom instrumentation quickly

The active monitoring (static monitors, threshold monitors) has been very helpful. We get a lot of value out of anomaly detection. SLOs and monitoring of SLOs have been extremely valuable.

The metrics and out-of-the-box infrastructure metrics that come with the Datadog agent installation are quite helpful to the organization. We have made use of both the custom metric implementation as well as the log-based metrics which are extremely convenient.

Datadog is so feature-rich that it is often hard to onboard new folks and tough to decide where to invest time. 

The APM is a perfect example of this. This feature alone has so much (profiling, tracing, span summary, flame graphs). I would love to see more of the insight and automation-focused features, such as the log patterns, where I can spend time more efficiently.

The cost of Datadog at scale can get very expensive very quickly. I would like to see a better usage/cost dashboard with breakdowns like the AWS cost explorer.

I've used the solution for three years.

Datadog provides us with a solution for data ingesting for all of our application metrics, resource metrics, APM/tracing data etc. 

We use it for use in dashboards, monitoring/alerting, SLO targets, incident response etc. 

We have a lot of applications across multiple languages/frameworks etc., and have deployed in Kubernetes across multiple regions in AWS, along with underlying managed resources such as SQS, Aurora, etc. 

Datadog makes understanding the state of these seamless. We are a company with millions of daily active users, and this level of detail is excellent.

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.

The management of SLOs and their related burn-rate monitors have allowed us to onboard teams to on-call fast. 

Management of resources using infrastructure-as-code has been a recent game-changer for us. Combining the two has allowed us to provide product teams with a total solution for getting their applications attached to user-focused alerting and monitoring within a matter of days rather than months - and has clearly impacted our ability to discover and respond to significant production incidents.

Managing dashboards as IaC is a bit hard to work out at times. I use custom tools to convert JSON dashboards to Terraform resources. Ideally, I'd like for some sort of building tool for this to be built into the app. For example, a templating system that can easily be exported to IaC would be transformative for us. 

There are also some aspects of the API that can be a bit verbose - especially in the area of new features like SLOs - and take some time to understand. That said, overall, they're well-documented enough to be a minor concern for us.

I've been using the solution for over five years.

I have never seen a major outage that prevented us from using Datadog, although I can't speak for other teams/time zones

This product is massively scalable - I haven't seen any issues as we continue to onboard new technologies and teams

Datadog provides us with a number of direct lines to support, although I haven't personally required their assistance.

We previously used LightStep for APM and switched to Datadog to unify all of our application data.

Most elements are quite simple to set up. However, some types of data collection require organization-wide engineering buy-in.

We handled the initial setup in-house.

Our use case is to provide cloud organization application monitoring. I use it for insight into what host in what region has activity or what market is using Datadog to its fullest potential and utilizing that for cost. This may also help determine who is using monitoring and setting alerts or just setting up monitoring and not doing anything about it. The use case can also be to check when the host or applications are down, or if the usage of CPU, memory, etc, is too high.

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.

The infrastructure is the most valuable. It has a high-level insight into the infrastructure model of the application and provides important detailed data on the host and metrics, which is the main concern of our customers. It provides confirmation that the layer where the application is running is monitored and will be alerted when it is down and not functional. The customers can have ease of mind knowing their metrics are accurately being measured. The value of data provided, including service name, logs, and all other pertinent details tied to the host, makes it a valuable source of data

The solution can be improved via open communication to the broader audience on what has changed and what has not changed. I sometimes log in and see items changed, either in the UI or a feature enabled. To see it for the first time without proper communication can sometimes come as a shock.

I have been using the solution for three years.

The stability is great.

Technical support is great. Datadog has the resources and knowledge to tackle questions.

Positive

I did not previously use a different solution.

The initial setup is straightforward.

The initial setup was handled in-house.

I did not evaluate any other solutions.

The main use cases are to provide visibility to costs for each product in the company as well as to consolidate all the observability in one tool. We are moving the team from being an operational team that needs to keep the tool up and running (applying patches and resolving problems) to a team that is focused on providing meaningful visibility of the systems, applications, and services of the company. We want to add value where the developers and the systems administrators are not able to focus.

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.

The tags are quite useful. They are providing the capability to give meaning to on-premises hardware (since it was not possible outside of cloud solutions and containers) as well to tag traces and logs. 

The full stack of integrations made it easier to monitor the different technologies and platform providers, including Software as a Service providers, that otherwise would need a lot of work and customization to be able to see what is happening. We'd also need to use several other separate tools that would require an increase in the required staff to operate them. Datadog gave us the opportunity to have a single platform for observability.

The product could be improved by providing remote control to agents, enabling them to execute automation and collections without requiring another automation tool or integration. 

Also, there is a lot of space for the FinOps discipline. For example, it could potentially provide better and richer information for the teams to check the costs and optimize the product.

I've used the solution for one year.

The stability is very good even though we have had some minor problems recently.

The scalability is very good. We've had no problems until now.

Technical support is good. That said, we had some cases that needed to be escalated to get to a faster resolution.

Positive

We previously used AppDynamics. The tool was not providing good system visibility as it was limited and had a very high cost.

The initial setup is somewhat complex. There is a need to create a new automation to install and deploy agents that needs to consider the required security for a financial company.

We handled the implementation in-house.

The ROI is still being calculated.

Users need to be aware of licensing control. With autodiscovery, the product can begin to come at a high cost.

We also looked into Splunk, ELK, and Dynatrace.

We use it to monitor and alert our ECS instances as well as other AWS services, including DynamoDB, API Gateway, etc. 

We have it connected to Pagerduty for alerting all our cloud applications. 

We also use custom RUM monitoring and synthetic tests for both our internal and public-facing websites. 

For our cloud applications, we can use Datadog to define our SLOs, and SLIs and generate dashboards that are used to monitor SLOs and report them to our senior leadership.

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.

I really enjoy the RUM monitoring features of Datadog. It allows us to monitor user behavior in a way we couldn't before. 

It's useful to be able to obfuscate sensitive information by setting up custom RUM actions and blocking the default ones with too much data. 

I also like being able to generate custom metrics and monitors by adding facets to existing logging. Datadog can parse logs well for that purpose. The primary method of error detection for our external website is synthetic tests. This is extremely valuable for us as we have a large user base.

At times, it can be hard to generate metrics out of logs. I've seen some of those break over time and have flakey data available. 

Creating a monitor out of the metric and using it in a dashboard to generate our SLIs and SLOs has been hard, especially in cases where the data comes from nested logging facets.

I've used the solution for two years.

The stability is pretty good.

The solution is pretty scalable! It's hard to set up all the infra (terraform code) required to link private links in Datadog to all of our different AWS accounts.

They offer good support. Solutions are provided by the team when needed. For example, we had to delete all our RUM metrics when we accidentally logged sensitive data and the CTO of Datadog stepped in to help out and prioritize it at the time.

Positive

We previously used Splunk and some internal tools. We switched due to the fact that some cloud applications don't integrate well with pre-existing solutions.

The initial setup for connecting our different AWS accounts via Datadog private link wasn't great. There was a lot of duplicate terraform that had to be written. The dashboard setup is way easier.

We installed it with the help of a vendor team.

Our return on investment is great and is so much better than CloudWatch. We can easily integrate with Pagerduty for alerting.

Our company set up the product for us, so the engineers didn't need to be involved with pricing. 

The pricing structure isn't very clear to engineers.

We looked into Splunk and some internal tools.

We use Datadog for general observability into our infrastructure, as well as running analytics queries for our SLI/SLO platform. This helps all of our teams be informed of how well their products are actually performing in production, and aim their efforts at the thing that will provide the highest ROI. 

We also use it for general monitoring and alerting during load tests and service releases to detect any issues related to the deployments. This helps us maintain our high contractual uptime promises to our clients.

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.

Distributed tracing is the most valuable feature. We have hundreds of microservices, and knowing how top-level requests weave throughout all of them is invaluable. 

At one glance, we can clearly see which service is slow and then switch over to the infrastructure view or container view to debug why the slowness is happening. This is true of all their other integrated products as well; the more you add, the more insights you get when looking at traces.

We also use RUM extensively. This helps us cover the last mile of application performance. Without it, we wouldn't know if our browser applications were functioning slowly for our users.

There is occasional UI slowness and bugs. While the Datadog UI is generally miles above its competitors, there are a few cases where it falls short or has started to slow down over time. They also occasionally make poor UI redesign choices. They should continue focusing on this area to maintain the high standard they started out with.

I've used the solution for five years.

We've never had major stability issues.

Scalability has never been an issue, although there is occasionally UI slowness.

Support via tickets is absolutely terrible. It's the one obvious bad spot for Datadog. If we didn't have direct relationships with many of their product managers, our experience would be much worse.

Negative

We previously used New Relic. It had a terrible UI and the integration between products was not great. Datadog is miles ahead of them and is continuing to increase that distance.

The initial setup is straightforward, and the docs are done well.

We managed the implementation in-house.

Our ROI is high.

I'd advise users to negotiate rates. Datadog's off-the-shelf rates are pretty high.

We have only used and looked into New Relic.

We use the product for recording loggers on our various services across different teams. For example, we use logs to keep track of info logs for events and error logs to catch exceptions. 

When users ask us to investigate a situation, we use logs to keep track of events and where the user's code traveled to. We also use synthetic testing and monitoring features to keep track of our many alerts in the production and QA environments.

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.

The feature I have found to be the most valuable is the filtering feature in logs. It is really easy to type plus and minus to filter out different logs. I use it to navigate the noise. 

I use synthetic tests as well. It is easy to navigate the menu and create tests. 

Much of the UI is very straightforward, and I do appreciate the ability to search for any documentation on the various features when I need to as well. The DASH monitoring boards are nice to give an overview of various performances and allow us to track use cases.

This service could be less costly. Right now, we only keep 15 days worth of logs since we want to be more economical in terms of cost. It would be nice if I had the option to monitor logs beyond 15 days. For APM traces, we only keep a year worth of traces. The UI can be a little more straightforward as well. I found it to have too many options.

I've used the solution for three years.

The stability is good.

The scalability is good.