Datadog Reviews

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.

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 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.

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.

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 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 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 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.