Datadog Reviews

We use Datadog to monitor our Kubernetes clusters. 

We have 3 different clusters for different parts of the SDLC. We run the Datadog agent DaemonSet as well as the Datadog cluster agent. Our services have the APM installed by default. 

To create monitors, we use Terraform. This is provided out-of-the-box for our service owner. 

We run EKS on top of K8s, therefore, we also make use of some of the AWS monitoring capabilities that can be integrated into Datadog. 

We are hugely reliant on Datadog for all aspects of our system.

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.

The APM and K8s monitoring are the most valuable aspects of the solution. The K8s monitoring allows all customers to view their infra, even if they do not use K8s daily. They can just click on a few tabs to get all of the information they need. 

It is also very easy to install on our system. APM has helped debug applications on our system as well. We were able to view why a service has suddenly shut down.

We also use Datadog for SLOs/SLAs as well. We check the live endpoint of services to ensure they are still up and running.

There is not much that needs to be improved. 

The UI is super user-friendly. The deployment process is easy. We enjoy using the integrations with Slack and PagerDuty. 

Customer support is awesome from our experience. There is a lot of documentation for us to be able to use if we need to. 

I'm not sure if Datadog can monitor K8s deployments in real-time. For instance, being able to see a deployment step by step visually. This would be helpful if there were any incidents during the deployment. 

In general, Datadog is a great solution.

I've used Datadog since I joined my company about a year ago.

We haven't had issues with the stability.

The scalability is really great.

We've had no issues with the product or support. 

The initial setup is super simple, and the documentation was helpful.

We managed the initial setup process in-house.

We've witnessed ROI in our DevOps.

We primarily use the solution for monitoring and log analysis.

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

The most valuable aspect of the solution is the APM.

The logging capabilities are quite useful. 

The logging could be improved in the future. 

I've used the solution for four years.

We primarily use Datadog for alerts. If we're running out of database connections or CPU credits we want to find out in Slack. Datadog provides nice features for that.

Secondarily, we use Datadog for analyzing historical trends and forecasting potential issues.

I'm trying to learn how to add in Continuous Profiler in our primary backend servers and set up Synthetic Tests for monitoring our front end.

Everything is mostly on AWS, and the Datadog integrations help a ton.

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.

The monitoring feature has been the most valuable.

I really like the dashboard. Monitoring has a straightforward tie-in to business value at my company (i.e. declaring incidents, etc). Things like having a dashboard and APM make my job easier. That said DevX is a little bit of a harder sell to executives in my company.

The dashboard feature makes it so easy to inspect multiple metrics at once across services. It's truly been a lifesaver when I'm personally trying to understand why performance degradation is happening.

I found the documentation can sometimes be confusing. I tried configuring APM for some of our Python containers, and I had to cross-reference multiple blog posts and the official documentation to figure out which Datadog-agent to use. If I needed a ddtrace trace, what environment variables I should set, etc. 

Furthermore, to generate my own traces, I wasn't aware that ddtrace adds its own "monkey patching," which led to headaches with respect to configuring the service for RabbitMQ.

A more unified and up-to-date documentation suite would be greatly appreciated.

I've used the solution for about two years.

I don't recall seeing an incident from Datadog in the past couple of years and that's been wonderful.

The solution is incredibly scalable! To be fair, our data throughput to Datadog isn't super huge, however, we have never seen issues as it scaled to handle more of our data.

We used to use AWS Cloudwatch for a lot of our monitoring needs. That said, the interface felt clunky, confusing, and limited.

We don't have hard numbers on ROI. That said, overall, it has been a wonderful addition to our tooling suite.

We also looked at Honeycomb and are currently using both in production.

The product is primarily used for the DevOps team. 

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

Datadog is providing efficiency in the products we develop for the wireless device engineering department. We had to provide more developer integration tools and also needed to help in creating easy queries that would help in creating efficient toolsets for management to make decisions based on these metrics.

We need more integration functionality, including certain metrics integration. We should be able to monitor devs and need it to build more monitoring tools and offer leadership metrics.

I've used the solution for almost six months.

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 the solution for monitoring our logs across distributed clusters. Right now, we have an Elasticsearch solution that is tied to each platform (our product is a PaaS solution). 

We are looking at moving to a single pane of glass solution, which Datadog would be good for (plus, we could wrap up other tools like Prometheus, Grafana, Pagerduty, Pingdom, and more). We want to be able to have Datadog running on one single cluster and ingesting and processing logs from all our distributed clusters.

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.

The CPU profiler has been interesting even though it isn't our core use case. 

We are finding that Datadog has way more offerings than originally expected, so we are constantly finding new parts of it that would be convincing to use. 

The log and ingestion are very similar to our current Elasticsearch setup. We find the tracing and overall integration/ecosystem to be the most valuable part. Basically, the CPU profiler is a good example of a value add for a problem we knew we had yet was low priority and had hacky workarounds. The value proposition is in the ecosystem as a whole.

The sheer amount of products that are included can be overwhelming. 

The solution requires better overarching UI, which would make things clearer. Even though I generally dislike the AWS UI, it makes the different services very clear, and it also makes where you are at any given point clear. 

The sidebar for all the different services is a bit much. 

I also found the tagging of logging pipelines to be a bit tedious. It would be great if, once marked up, it would automatically be a first-class citizen in Datadog.

We are still in the evaluation stage and have used it less than one month.

The stability looks good so far.

It seems easier to scale and build app functionality across multiple teams rather than other solutions.

We have used Elasticsearch, Grafana, and Prometheus. We are still evaluating Datadog.

The product has provided good ROI by saving development time as well as time managing setting up ES.

It is somewhat expensive compared to open-source options.

We evaluated Elasticsearch, Grafana, and Prometheus. We are still evaluating Datadog.

We use different tools for log collection and monitoring. Using Datadog will combine different use cases into one product that will be easier to manage. 

The tools we use are open-source, so there is no commercial support. Having customer support would be ideal since we're a small team. 

Profiling would be another great feature to have. Currently, it's manual. Having Datadog would give us a standard, and we don't have to do much manual work.

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.

The biggest thing I liked was the combination of all the things - monitoring, log aggregation, and profiling. We have different tools for each of them in our organization and all of them are open-source. These are not very well maintained and there is no customer support. 

Having an industry-standard product is ideal for us as we are short on manpower. Profiling is another amazing feature. Currently, we rely on some open-source solutions, and it's all done locally. Having it done on Kubernetes would give us more insights and help with performance. Alerting is again a nightmare for us. Datadog solves all of these issues.

It can be overwhelming for new people as it has a lot of features. The UI could certainly be improved. Having less information with better organization could help newcomers. I haven't seen the documentation, however, a well-organized documentation would invite many varied users.

I've been using the solution for three years.

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.