Jaswinder Kumar - PeerSpot reviewer
Senior Manager - Cloud & DevOps at Publicis Sapient
Real User
Overall useful features, beneficial artificial intelligence, and effective auto scaling
Pros and Cons
  • "Most of the features in the way Datadog does monitoring are commendable and that is the reason we choose it. We did some comparisons before picking Datadog. Datadog was recommended based on the features provided."
  • "All solutions have some area to improve, and in Datadog they can improve their overall technology moving forward."

What is our primary use case?

My customers were using Datadog for monitoring purposes. They were using it only because the solution is running on AWS and it's a microservices-based solution. They were using an application called Dynatrace for their log.

What is most valuable?

Most of the features in the way Datadog does monitoring are commendable and that is the reason we choose it. We did some comparisons before picking Datadog. Datadog was recommended based on the features provided.

Most of the monitoring tools nowadays are have or are going to have embedded artificial intelligence and machine learning to make monitoring and logging more proactive and intelligent. Datadog has incorporated some artificial intelligence.

The solution does not require a lot of maintenance.

The solution had all the features we were looking for and we were able to create a central dashboard as per our requirements.

What needs improvement?

All solutions have some area to improve, and in Datadog they can improve their overall technology moving forward.

For how long have I used the solution?

I have been using Datadog for approximately four months.

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What do I think about the stability of the solution?

Datadog is a stable solution.

What do I think about the scalability of the solution?

Datadog is a highly scalable solution because it is a SaaS solution. Having this solution be a SaaS is one of its most appealing attributes. When the vendor is going to manage data scaling and everything for you, you are only going to use the solution as per your requirements. Autoscaling is a great feature that they have.

How are customer service and support?

The support from Datadog is exellent. If you're stuck on something or you are facing any issue, support from the vendor itself is available. You will receive a response instantly from the vendor on anything related to the requirement,  issues, or feature you are looking for. The responses have always been in a timely manner.

I rate the technical support from Datadog a five out of five.

Which solution did I use previously and why did I switch?

I have used other similar solutions to Datadog and when I do a comparison between the other tools Datadog is on top, it is great.

How was the initial setup?

Since Datadog is a SaaS solution we had not deployed the Datadog on-premise or in any Cloud. We were using the SaaS solution from the vendor itself. From the provisioning perspective or from the monitoring and dashboard perspective, we were using Terraform to create the typical monitoring as code. Everything was basically automated, we were not doing anything manually.

What other advice do I have?

If someone wants to set up Datadog on-premise or in any of the Cloud machines, they have to consider a lot of things from the auto-scaling perspective.

My recommendation is Datadog is very good. Your team can mainly focus on the development rather than the solution itself. The vendor is going to take care of auto-scaling and maintenance and everything for you.

I rate Datadog a nine out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: My company has a business relationship with this vendor other than being a customer: partner
PeerSpot user
Senior Cloud Engineer, Vice President of Monitoring at a financial services firm with 10,001+ employees
Real User
Good ServiceNow integration, helpful API crawlers, and useful APM metrics
Pros and Cons
  • "The seamless integration between Datadog and hundreds of apps makes onboarding new products and teams a breeze."
  • "It seems that admin cost control granularity is an afterthought."

What is our primary use case?

We are using the solution for migrating out of the data center. Old apps need to be re-architected. We are planning on moving to multi-cloud for disaster recovery and to avoid vendor lockouts. 

The migration is a mix between an MSP (Infosys) and in-house developers. The hard part is ensuring these apps run the same in the cloud as they do on-premises. Then we also need to ensure that we improve performance when possible. With deadlines approaching quickly it's important not to cut corners - which is why we needed observability

How has it helped my organization?

Using the product has caused a paradigm shift in how we deploy monitoring. Before, we had a one-to-one lookup in ServiceNow. 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.

What is most valuable?

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 Datadog uses for cloud integrations. These allow us to pick up and leverage the tags teams have already deployed without having to also make them add it at the agent level. Then we use Datadog's conditionals in the monitor to dynamically alert hundreds of teams. 

With the ServiceNow integration, we can also assign tickets based on the environment. Now our top teams are using the APM/profiler to find bottlenecks and improve the speed of our apps

What needs improvement?

The real issue with this product is cost control. For example, when logs first came out they didn't have any index cuts. This caused 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 is no way 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.

For how long have I used the solution?

I've used the solution for about three years. 

What do I think about the stability of the solution?

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

What do I think about the scalability of the solution?

It is easy to scale. That is why we adopted it.

How are customer service and support?

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

How would you rate customer service and support?

Neutral

Which solution did I use previously and why did I switch?

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

How was the initial setup?

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

What about the implementation team?

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

What was our ROI?

The solution has saved months of time and reduced blindspots for all app teams.

What's my experience with pricing, setup cost, and licensing?

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

Which other solutions did I evaluate?

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

Which deployment model are you using for this solution?

Hybrid Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Buyer's Guide
Datadog
March 2024
Learn what your peers think about Datadog. Get advice and tips from experienced pros sharing their opinions. Updated: March 2024.
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JamesPhillips - PeerSpot reviewer
System Engineer at Raymond James
Real User
Top 20
A stable and scalable infrastructure monitoring solution
Pros and Cons
  • "Datadog has flexibility."
  • "The product needs to have more enterprise approach to configuration."

What is most valuable?

Datadog has flexibility.

What needs improvement?

The product needs to have more enterprise approach to configuration.

For how long have I used the solution?

We use the tool to monitor our whole infrastructure. CPU, memory, and disk space are the types of things we use it for.

What do I think about the stability of the solution?

It is a stable solution.

What do I think about the scalability of the solution?

It is a scalable solution.

How are customer service and support?

The technical support team is good and responsive.

How would you rate customer service and support?

Positive

How was the initial setup?

The initial setup is not very easy and the deployment took eight months.It took quite a few teams to get it all accomplished. I rate it a six out of ten.

What other advice do I have?

I rate the solution eight out of ten.

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Delivery Manager, DBA Services at a manufacturing company with 10,001+ employees
Real User
It combines tracing and logging in one tool
Pros and Cons
  • "Datadog provides tracing and logging, whereas Dynatrace focuses on tracing, and Splunk is more of a logging tool. Datadog's advantage is that we don't need two tools."
  • "Datadog isn't as mature as some of the established players like Dynatrace or Splunk. It's a new product, so they are constantly releasing new features, and I don't have much to complain about."

What is our primary use case?

We use Datadog for monitoring to get the traces and logs of all our applications. Datadog provides dashboard and alert capabilities to identify if something is wrong with various teams. More than 200 users, mostly software engineers, work with Datadog. 

What is most valuable?

Datadog provides tracing and logging, whereas Dynatrace focuses on tracing, and Splunk is more of a logging tool. Datadog's advantage is that we don't need two tools. 

What needs improvement?

Datadog isn't as mature as some of the established players like Dynatrace or Splunk. It's a new product, so they are constantly releasing new features, and I don't have much to complain about.

For how long have I used the solution?

We have used Datadog for seven months.

What do I think about the stability of the solution?

We haven't issued any issues so far, so it's a highly stable platform. 

What do I think about the scalability of the solution?

We are a unit within a much larger entity that is using Datadog. It can scale up to meet your needs. 

How are customer service and support?

We have regular calls with the Datadog team. They take feedback and bring in the product managers to quickly answer questions and fix issues. They help you deal with some of the issues you have with any new product, but Datadog is one of the fastest-growing products in the monitoring space.

How was the initial setup?

You don't need to install anything because it's a SaaS product with a web-based UI. They provide you the credentials to give you admin access. You only need to install the agents where you need monitoring. The time required to deploy the agent depends on what you're monitoring, but the solution itself works like Office 365 or any other SaaS product. 

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Architect at a comms service provider with 10,001+ employees
Real User
Good for monitoring and following metrics with a helpful flame graph
Pros and Cons
  • "Flame graphs are pretty useful for understanding how GraphQL resolves our federated queries when it comes to identifying slow points in our requests. In our microservice environment with 170 services."
  • "I often have issues with the UI in my browser."

What is our primary use case?

We use the solution primarily for distributed tracing, service insight and observability, metrics, and monitoring. We create custom metrics from outbound service calls to trace the availability of back-office systems. 

We use the flame graph to get insights into our GraphQL implementation. It helps highlight how resolvers work. 

However, it's lacking in tracing which GraphQL queries are run, and we use custom spans for that.

How has it helped my organization?

Prior, the team only had Instana, and few people used it. The main barriers to entry were the access (since it was not integrated into our SSO) and the user experience, which made it hard to follow. We had an on-prem version, and it wasn't the snappiest. The APM has made observability and tracing more accessible to developers.

What is most valuable?

Flame graphs are pretty useful for understanding how GraphQL resolves our federated queries when it comes to identifying slow points in our requests. In our microservice environment with 170 services. There are complex transactions over the course of a single user request since we essentially operate as a middle layer with 90 back office systems we integrate to.

What needs improvement?

I often have issues with the UI in my browser. I tend to have a lot of tabs open, yet have issues with it not responding or not showing data. A couple of times, pasting the URL into an incognito window shows the data that's there.

For how long have I used the solution?

I've used the solution for two years. 

How was the initial setup?

The initial setup was complex and required a bit of tweaking to get everything configured correctly and into our pipelines.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Software Engineering Manager at a hospitality company with 1,001-5,000 employees
Real User
Easy to implement with great passive and active monitoring
Pros and Cons
  • "It is easy to implement and scale applications with standardized visibility, monitoring and alerting"
  • "Datadog is so feature-rich that it is often hard to onboard new folks and tough to decide where to invest time."

What is our primary use case?

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.

How has it helped my organization?

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.

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

I've used the solution for three years.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Ramon Snir - PeerSpot reviewer
CTO at a tech vendor with 1-10 employees
Real User
Increases delivery velocity with les manual testing and good integrations
Pros and Cons
  • "Since we integrated Datadog, we have had increased confidence in the quality of our service, and we had an easier time increasing our delivery velocity."
  • "Since the Datadog platform has so many separate features, solving so many use cases, there are often inconsistencies in feature availability and interoperability between products."

What is our primary use case?

We use Datadog for three main use cases, including:

  • Infrastructure and application monitoring. It is ensuring that our services are available and performant at all times. This allows us to proactively address incidents and outages without customers contacting us. This includes monitoring of cloud resources (databases, load balancers, CPU usage, etc.), high-level application monitoring (response times, failure rates, etc.), and low-level application monitoring (business-oriented metrics and functional exceptions to customer experience.
  • Analyzing application behavior, especially around performance. We often use Datadog's application performance monitoring on non-production environments to evaluate the impact of newly introduced features and gain confidence in changes.
  • End-to-end regression testing for APIs and browser-based experiences. Using Datadog's synthetic testing checks periodically that the system behaves in the exact correct way. This is often used as a canary to detect issues even before users reach them organically.

How has it helped my organization?

Since we integrated Datadog, we have had increased confidence in the quality of our service, and we had an easier time increasing our delivery velocity. 

We have seen time after time that the monitors we have carefully created based on all ingested data are detecting issues quickly and accurately. 

This means we allow ourselves to manually test things less frequently. We have also had an easier time investigating application errors and slowness using Datadog's APM and log explorer products which allow us to introspect any part of the system, in its execution context.

What is most valuable?

The most valuable features include:

  • Integrated observability data ingestions: All data that Datadog collects is connected. This allows easily connected logs with failed requests, and slow database questions with services and requests.
  • Broad integrations allow us to monitor our entire production environment in a single place, not just cloud resources. Since all parts stream metrics, logs, and events to Datadog, we can have unified dashboards and manage monitors and incidents all from the same page.
  • A high level of configuration. We can configure and modify many parts, from how data is collected from our applications to how Datadog parses and visualizes it. This means that we always get the best experience, and we don't need to find ten different products that do small things well or settle on one product that does everything badly.

What needs improvement?

Since the Datadog platform has so many separate features, solving so many use cases, there are often inconsistencies in feature availability and interoperability between products. 

Older, more mature products tend to be complete (many features, customization, broad integrations, etc.), while newer products will often be at a "just above minimum viable product" phase for a long time, doing what's intended yet missing valuable customizations and integrations.

For how long have I used the solution?

We've used the solution for 12 months.

What do I think about the scalability of the solution?

The solution scales very well on technical aspects, being able to ingest large quantities of data from many services. However, the pricing often doesn't scale naturally, and effort has to be put in to keep ongoing costs at a reasonable amount.

How are customer service and support?

Customer service and support are generally very high-quality. In most cases, they reply very quickly and offer well-researched and relevant responses. This is contrasted with many vendors who take a long time to reply and send links to documentation instead of understanding the problem.

However, we had cases where support took several weeks to reply to a complicated request and sometimes eventually responded that the issue cannot be resolved. These are rare edge-case occurrences.

How would you rate customer service and support?

Positive

How was the initial setup?

A large part of the initial setup was straightforward. We were able to collect about 80% of the relevant and 90% of the meaningful insights from just a couple of hours of connecting the AWS integration and the Datadog APM agent. 

Getting it to 100% and configuring and customizing things to our unique situation, took about two weeks. Datadog's documentation and support team were extremely helpful during both phases.

What about the implementation team?

We handled the setup in-house.

What was our ROI?

From the number of outages stopped or shortened (which lead to lost revenue from non-renewals) and the number of hours saved on investigations (which correlates to engineering salaries), I estimate that the ROI of the implementation time and monthly charges to be between 10x and 20x.

What other advice do I have?

We use the solution as a SaaS deployment.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Staff Cloud Engineer at a energy/utilities company with 51-200 employees
Real User
Good infrastructure and APM metrics with easy onboarding of new products
Pros and Cons
  • "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."
  • "The real issue with this product is cost control."

What is our primary use case?

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.

How has it helped my organization?

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.

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

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

What do I think about the stability of the solution?

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

What do I think about the scalability of the solution?

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

How are customer service and support?

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

How would you rate customer service and support?

Neutral

Which solution did I use previously and why did I switch?

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

How was the initial setup?

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

What about the implementation team?

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

What was our ROI?

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

What's my experience with pricing, setup cost, and licensing?

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

Which other solutions did I evaluate?

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

Which deployment model are you using for this solution?

Hybrid Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
PeerSpot user
Buyer's Guide
Download our free Datadog Report and get advice and tips from experienced pros sharing their opinions.
Updated: March 2024
Buyer's Guide
Download our free Datadog Report and get advice and tips from experienced pros sharing their opinions.