Datadog Reviews

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 currently use it for log aggregation and SEIM. We send logs from our AWS account (particularly our Cloudtrail and S3 logs) and use them to give us security signals. 

This has helped with our SOC2 certification process and has given us a window into our processes and the security holes in our system. 

We are also considering using the APM features to help with our development effort. We want to be able to profile all of our code and see what is going on with it.

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.

The ease of use is the primary aspect. I have used, at previous jobs, the ELK stack and Splunk for log management. Both of them were useful, yet required a lot of manual effort to get set up (and a lot of continuing effort to tweak. A simple monitoring solution turned into a full-time job! However, Datadog has so far been a breeze to use and set up. It looks at what I am sending it and figures out what it is almost by magic. Even the manual configuration makes sense and gives very fast and thorough results

One thing we have run into is that it is so easy to add monitoring that we turn on things without really understanding the costs. 

I would like a way to show a continuous indication of what my setup will cost on a daily or weekly basis.

I've used the solution for six months.

We use the solution primarily for platform monitoring for the services that are deployed in AWS. It gives a better way to monitor the services, including pods, cost, high availability, etc. This way, observability is ensured and also customer services are uninterrupted. 

Also, we host the data pipelines between the cloud and the on-prem for which Datadog is used to ensure better services. We report issues based on the metrics reported over it. 

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.

The observability pipelines are the most valuable aspect of the solution. 

Platform monitoring for the services that are deployed in AWS is helpful. It gives a better way to monitor the services. With Datadog, we ensure observability and maintain uninterrupted customer service. 

We can host the data pipelines between the cloud and the on-prem. Issues are easily reported.

The data streams are good. Data lineage is something that really helped in ensuring tracking of the data and metrics and also the volumes processed.

We'd like to see better transformers.

Live chat would be the best way to support us. 

Also, the features that we saw getting launched recently were something we expected and we're glad to see them coming.  

Geo-data is also something very critical that we hope to see in the future.

I've used the solution for two or more years. 

We primarily use the solution for log management and application performance monitoring. We have been getting into using more solutions on Datadog, such as runbooks, monitoring, and dashboards. 

Another area that we've been investing some time in is the database monitoring. We've been able to get some relatively new employees onboarded into the tool, and they've been able to create some meaningful dashboards and reports without too much hand-holding at all. 

We plan on exploring the synthetics solution as well.

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.

Correlation between logs and APM has been the most important feature that we've found in Datadog to date. Previous solutions around log collection or APM instrumentation were rather cumbersome to connect. We previously needed to use different solutions for each which were not connected and required complex queries and a lot of time investment by key employees.

The search and filtering capabilities are rather helpful as well. The aggregation of all currently available properties has been great. It's excellent that available options drop as filters are refined. This allows for a nuanced view of available data.

We intend on exploring other products at Datadog, so this list may expand.

I've found that the documentation is lacking in certain regards. In going through sessions around certain services, the presenter expressed opinions on best practices that are not covered by documented examples. 

In taking these thoughts to the "experts," further research is required both by us and those working the table to come to a solution that meets our needs. If there were more documentation on best practices this may be easier to manage.

I've been using the solution for ten years. 

The solution overall seems rather stable.

The solution seems scalable. We just need to keep an eye on the costs as it scales.

Customer support has been ok, yet not great. We've had ticket resolution drag on for weeks.

Neutral

We previously used Scalyr for logs and switched due to APM linkage.

The initial setup was straightforward.

We handled hte setup in-house.

We've saved many developer hours by using Datadog. We plan on expanding our investment in this solution (and thus our return).

Pricing can be a bit of a sell internally. We've found it to be worth it, though.

We came from using other solutions.

We are trying to get a handle on observability. Currently, the overall health of the stack is very anecdotal. Users are reporting issues, and Kubernetes pods are going down. We need to be more scientific and be able to catch problems early and fix them faster.

Given the fact that we are a new company, our user base is relatively small, yet growing very fast. We need to predict usage growth better and identify problem implementations that could cause a bottleneck. Our relatively small size has allowed us to be somewhat complacent with performance monitoring. However, we need to have that visibility.

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

The most useful feature is the APM. Being able to quickly view which requests are time-consuming, and which calls have failed is invaluable. Being able to click on a UI and be pointed to the exact source of the problem is like magic. 

I'm also very intrigued by log management, although I haven't had quite a chance to use it very effectively. In particular, the trace and span IDs don't quite seem to work for me. However, I'm very keen on getting this to work. This will also help my developers to be more diligent and considerate when creating log data.

As a new customer, the Datadog user interface is a bit daunting. It gets easier once one has had a chance to get acquainted with it, yet at first, it is somewhat overwhelming. Maybe having a "lite" interface with basic features would make it easier to climb the learning curve.

Maybe the feature already exists. However, I'm not sure how to keep dashboard designs and synthetic tests in source control. For example, we may replace a UI feature, and rebuild a test accordingly in a pre-production environment, yet once the code is promoted to production, the updated test would also need to be promoted.

We have just started using the solution and have only used it for about two months.

We're new at this. That said, so far, there haven't been any issues to report.

I have not had the opportunity to evaluate the scalability.

Customer support is full of great folks! We're beginning our Datadog journey, so I haven't had that much experience. The little I have had has been great.

Positive

This is all new. 

We used to work with New Relic. New Relic has an amazing APM solution. However, it also became cost-prohibitive

Since we are relatively greenfield, it was relatively painless to set up the product. 

Our in-house DevOps team did the implementation.

I don't know what the ROI is at this stage.

I'm not sure what the exact pricing is. 

So far, it's been great!

The product is used for APM solutions for the metrics and traces for the REST API requests and service maps to understand the upstream and downstream services.

We are creating dashboards and widgets to monitor the status. We are creating alerts and monitors as well. We integrated the alerts and ticketing system in our organization with SNOW and Netcool.

We are using Kubernetes, AWS, and infrastructure metrics. We are using Kafka and Aurora Postgres logs as well, and we are using HTTP status codes to identify the error types.

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. 

The most valuable aspect is the APM which can monitor the metrics and latencies. There's a low error rate, and any alerts can be tagged to the service requests and sent via email to the required DLs. 

We can create incidents as well in our internal tools, like SNOW and Netcool.

The monitoring enables different dimensions of metrics to monitor the services and infrastructure. 

We have cloud infrastructure monitoring in Kubernetes nodes, pods containers, and ingress metrics.

Alerts are sent to an email in case of any issues. The metrics are used to create alerts.

The solution offers good dashboards, service maps, traces and flame graphs, HTTP status codes, power packs, service catalogs, and profiling.

While the logs module is not activated, we are using all other modules.

The correlation between the logs and the metrics needs improvement as most cases, we might use another logging tool (that is cheaper in cost) which then we have to link together. 

They can improve the SSO logging as well. Currently, we are logging in every two to three days by sending the login link explicitly.

I've been using the solution for two years. 

The stability is awesome. 

We are expanding beyond observability right now.

They offer pretty awesome customer support.

Positive

We did not previously use a different solution.

The initial setup was easy.

We implemented the solution with the help of a vendor team.

I'd rate the ROI ten out of ten.

I would recommend Datadog to others.

We also evaluated ECE and Splunk.

The solution has a great support model.

We use the solution for application hosting and a little bit of everything when it comes to supporting a worldwide logistics tracking service. It's used as a central service for collecting telemetrics and logs. We find it does the same work as all of our old tools combined, including Prometheus, Kibana, Google Logs, and more; putting all of this information in a single platform makes it easy to corroborate information and associate a request with the data, which might be lost when it is saved as logs.

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.

I primarily prefer to utilize the profiling and tracing feature. It can potentially be used as a more-informed alternative to logs.

Beyond that, anything I've wanted to do, I found a way to get it done through Datadog. It allows for testing, logging, hardware monitoring, system performance, memory consumption, advanced observability, AI assistance, cross-team collaboration, and business analytics. Datadog helps some of the world’s biggest brands transform faster with the help of true AIOps, AI-assisted answers, UX and business analytics, cloud observability, and smart AI assistance.

It's all supporting my desire to build a great application, and in a centralized SaaS application, it's hard to say anything can beat it.

The storage of logs is a little bit unexpected; most services generate gigabytes of logs, and their size is not excessive. When it comes to storing the logs with Datadog, I'm not sure why it costs so much to store gigabytes or terabytes of information when it's a fraction of the cost to do so myself.

I've used the solution for one year.

We have no concerns with stability.

It appears to be that there are no issues with scaling.

Technical support is slow. It takes forever to get responses from the support team.

Neutral

I've previously used Kibana and Prometheus. We are still using these.

Setting up through the environment variables made it unbelievably easy to get started.

We've implemented the solution in-house.

I do not have this number off-hand, as I am not the finance guy. I just like the product.

I'd advise new users not to start off by sending logs.

We did not really look at other options.