Datadog Reviews

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.

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!

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.

We are using the solution for migrating out of the data center. Old apps need to be re-architected. We plan to move to multi-cloud for disaster recovery and avoid vendor lockouts. The migration is a mix between an MSP (Infosys) and in-house devs. The hard part is ensuring these apps run the same in the cloud as they do on-prem. Then we also need to ensure that we improve performance when possible. With deadlines approaching quickly, it is important not to cut corners which is why we needed observability.

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.

For use, the most valuable features we have are infrastructure and APM metrics. The seamless integration between Datadog and hundreds of apps makes onboarding new products and teams a breeze. 

We rely heavily on the API crawlers that Datadog uses for cloud integrations. These allow us to pick up and leverage the tags teams have already deployed without having also to make them add them at the agent level. Then we use Datadogs conditionals in the monitor to dynamically alert hundreds of teams, and with the ServiceNow integration, we can also assign tickets based on the environment. Now, our top teams are using APM/profiler to find bottlenecks and improve the speed of our apps.

The real issue with this product is cost control. For example, when logs first came out, they didn't have any index cuts. This leads to runaway logs and exploding costs. 

It seems that admin cost control granularity is an afterthought. For example, synthetics have been out for over four years, yet there are no ways to limit teams from creating tests that fire off every minute. If we could say you can't test more than once every five minutes that would save us 5X on our bill.

I've been using the solution for about three years. 

The solution is very stable. There are not too many outages, and they fix them fast.

It is easy to scale. It's why we adopted it. 

Before premium support, I would avoid using them since it was so bad.

Neutral

We previously used App Dynamics. It isn't built for the cloud and is hard to deploy at scale.

The initial setup was not complex. We just had to teach teams the concept of tags.

We implemented the solution in-house. It was me. I am the SME for Datadog at the company.

We have seen an ROI. It has saved months of time and reduced blindspots for all app teams.

We'd advise new users to be careful with logs, and the APM as those are the ones that can get expensive fast.

We looked into Dynatrace. However, we found the cost to be high.

We use Datadog for observability and system/application health, mainly for product support, triaging, debugging, and incident responses.

We use a lot of the logging and the Datadog agent to collect logs, metrics, and traces from our GKE workloads. We use APM and continuous profiling for latency and performance measurement. We use RUM to observe frontend user events, such as tracing on request and what actions they take before errors occur. We also use error tracking and source maps to debug production failures.

We are still relatively new to the product, and we are planning to use more of the notebook functionality and power packs to record run books and break knowledge silos. We also need to utilize dashboards and continuous profiling more for performance measurement and integrate Datadog alerts for incident response.

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.

We use structure logging a lot to triage production issues. The querying, attributes and tags manipulation, and customization have been very helpful in isolating and filtering environments. The integration with Winston logger has also been a breeze.

First and foremost, was that structured logging, tags, and attributes have not only allowed us to narrow down to a problem quickly in production, they have also let us create dashboards from these logs to understand more user behaviors, such as how many users stop and leave our application before an upload has completed. That helps us understand how important processing time is to a user.

We also intend to use distributed tracing more to understand where the error has occurred in a particular request.

Definitely, documentation could use improvement. As I navigated and try to find instrumentation and implementation details, I discovered inconsistency among SDKs based on languages. 

There are also places where highlighting can be improved. I once created an issue on GitHub, and it was resolved right away by an engineer. He pointed out that it was actually in the documentation. I looked again and found it was not very obvious. We were stuck on the problem for days.

Auto instrumentation on tracing has not been very easy to find in the documentation. We ended up using OpenTelemetry, yet the conversion between tracing contexts has been difficult.

We've used the solution between six months and a year. 

Customer service and support are generally very fast. I did experience one ticket, which involved changing the log index retention period, not being responded to. Any support tickets related to technical issues were resolved pretty fast.

Positive

We used to use GCP Stackdriver for logging and monitoring since our infrastructure is all GCP based. It was lacking a lot, particularly on tracing and structured logging. We often had a lot of trouble triaging and diagnosing a production problem. Datadog's specialty is observability. Since we started using the product, we were able to create dashboards, and utilize APM, continuous profiling, RUM, and distributed tracing for production support and user trends.

Datadog also offers labs and workshops for its products, which is very helpful.

We implemented the product ourselves.

I'm not sure what our ROI would be.

We started with on-demand pricing as we were re-writing our product, and we weren't sure about the total usage. After we went into production and released the product, we experienced a price surge. Fortunately, our Datadog account manager reached out to us and suggested a monthly subscription, which is what we'll be switching to.

I'd advise keeping an eye on the usage and possibly setting up some monitoring on price. We didn't have much of a setup cost; we started with a free trial and continued with on-demand after the trial ended.

We didn't evaluate many of the other options. However, we do also use OpenTelemetry, which is vendor agnostic and integrates with Datadog.

We always keep the Datadog agent to the latest version.

This solution is for physical device monitoring across breweries, including PLCs, HMI Cameras, RFID panels, scales, etc. We want to gain visibility into these devices to influence predictive maintenance and unscheduled downtime. We want to monitor physical devices across the zone from a control tower perspective for end users and support teams alike. Understanding more about the performance of the devices and mechanical components will allow us to schedule downtime to fix imminent catastrophic failures and prevent unplanned downtime and lost revenue.

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.

The most valuable features I have found are network monitoring, testing, and integration tools. The visibility into our network has allowed for quick diagnosis of failures, identification of underutilized or over-utilized resources, and allowed for cloud cost optimization opportunities. The ability to correlate metrics has proven useful in determining downstream or upstream issues influencing the device, machine, or database having issues.

I would love to see more metrics or analytics in IoT devices. 

I've been using the solution for approximately two years.

I have never experienced an issue or outage.

The solution is very scalable and developed in a fashion that provides the ability to scale easily.

Customer service has been outstanding. They have been timely and knowledgeable with all of my questions.

Positive

We used a different product for the total stack solution.

The initial setup was straightforward.

We handled the setup process in-house.

I'm unsure as to if we've seen an ROI.

We did evaluate SolarWinds.

We share dashboards, set up alerts, and monitor everything that happens in our system. We use it in staging, features, production, and our load test environment. It is exceptionally helpful for making our engineering more data-driven. 

I came from a company that believes we should focus on being telemetry driven. Instilling this in a smaller, less mature engineering organization has been challenging. However, it is much easier while using Datadog.

The dashboards are great. They are an easy way to give visibility into what we need to watch with others who are not SMEs.

I enjoy the custom metrics. With this, we can take things that were once logs and then retain them longer.

We are able to parse logs. To be honest, this was only useful due to the fact that we had not yet set up the Datadog agent properly in PHP. Once we did this, the Datadog log parsing was no longer needed.

The ability to pin to a date and time is very helpful. This allows us to pinpoint exactly what was happening.

We need more advanced querying against logs. While most issues I have had here can be alleviated by way of sending better-formatted logs, it would be cool to do SQL-type queries against our data.

We need a way to see dashboard metadata. We launched a huge customer, and we saw more people using Datadog than ever across the entire organization, yet had no way to tell.

It would be ideal if we had some way to compare arbitrary date times more easily. We would love to use the Diff Graph command against some hard-coded value, for instance, against some known event.

I've used the solution for eight months.

The scalability is great!

We previously used New Relic. I was not part of the decision-making team that made the switch.

The ROI is the speed at which we can debug live sites. It has been excellent. It's amazing how many incidents we can capture before customers notice.

We looked into New Relic and a home-brewed solution as potential other options.

We primarily use the solution for log monitoring across our entire cloud infra (EB, EC2, Batch, and Lambda).

This is in addition to Rstudio Workbench, which has its own logs that would not be picked up via Cloudwatch(https://docs.rstudio.com/ide/server-pro/server_management/logging.html#default-log-file-locations). 

We own several dozen of these servers, and we used to manage instance logs by tailing logs when incidents occurred. Datadog allows for much better visibility across our entire fleet and has saved us countless hours.

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.

We've found it most useful for managing Rstudio Workbench, which has its own logs that would not be picked up via Cloudwatch. 

Datadog allows for much better visibility across our entire fleet and has saved us countless eng hours as a result. 

We plan on trying out offerings such as APM moving forward too.

Some things that Datadog does very well:

• Technical documentation (the docs are clear, concise, and include realistic code samples)
• Overall education efforts (e.g. the codelabs/workshops)

We primarily use the log management functionality, and the only feedback I have there is better fuzzy text searching in logs (the kind that Kibana has). 

I've learned about a ton of other offerings, like APM, NPM, etc., over the course of workshops. Once I try those out, I'm sure I will have additional feedback.

I've used the solution for one year. 

We use this solution for our customer's IP and to support their cloud infrastructure.

Datadog is easy to use and easy to deploy. It's a better solution compared to others on the market in terms of being budget friendly for our customers.

Datadog could be improved if it could detect other software in a container or server. Datadog is better than other APM or observability tools, but it focuses mostly on telling the customer what they need to know about the software, database or applications that land on the server. We also need to know the version before setting up an agent with the APM modeling tool.

In some instances, the owner of a particular software changes to another person and this person did not originally transfer the knowledge or data to manage the server. The new person needs to monitor this server and they need to know what software or version of software was installed on this server before they used the APM agent for monitoring. If datadog could provide this insight, it would improve how we use the solution. 

In a future release, we would like to be able to complete a network traffic or network flow analysis to detect the errors or problems on the network.

I have been using this solution for two years. 

This is a stable solution. 

The initial setup was straightforward. We needed two engineers for the deployment.

This solution is budget friendly.

Overall, Datadog is a good product to use and is easy to deploy.

I would rate this solution a nine out of ten. 

I used Datadog typically for monitoring website statistics and some of the cloud networking equipment.

The most valuable features are the dashboards and the reporting.

I have been using this solution for approximately three years.

I found the solution to be stable, I did not experience any bugs or glitches. However, some of the managing team did.

The scalability of the solution was good. Being a cloud solution, if there was an issue with the scalability it would be easily fixed with an update.

We have approximately 200 users using the solution in my organization.

I did not need to use the support.

I was previously using SolarWinds in the company I was working with before.

I rate Datadog nine out of ten.