Datadog is a monitoring service for IT, Dev and Ops teams who write and run applications at scale, and want to turn the massive amounts of data produced by their apps, tools and services into actionable insight.
$18
per month per host
Grafana
Score 8.6 out of 10
N/A
Grafana is a data visualization tool developed by Grafana Labs in New York. It is available open source, managed (Grafana Cloud), or via an enterprise edition with enhanced features. Grafana has pluggable data source model and comes bundled with support for popular time series databases like Graphite. It also has built-in support for cloud monitoring vendors like Amazon Cloudwatch, Microsoft Azure and SQL databases like MySQL. Grafana can combine data from many places into a single dashboard.
$0
Prometheus
Score 7.9 out of 10
N/A
Prometheus is a service monitoring and time series database, which is open source.
N/A
Pricing
Datadog
Grafana
Prometheus
Editions & Modules
Log Management
$1.27
per month (billed annually) per host
Infrastructure
$15.00
per month (billed annually) per host
Standard
$18
per month per host
Enterprise
$27
per month per host
DevSecOps Pro
$27
per month per host
APM
$31.00
per month (billed annually) per host
DevSecOps Enterprise
$41
per month per host
Grafana Cloud - Pro
$8
per month up to 1 active user
Grafana Cloud - Free
Free
10k metrics + 50GB logs + 50GB traces up to 3 active users
Grafana Cloud - Advanced
Volume Discounts
custom data usage custom active users
Grafana - Enterprise Stack
Custom Pricing
No answers on this topic
Offerings
Pricing Offerings
Datadog
Grafana
Prometheus
Free Trial
Yes
Yes
No
Free/Freemium Version
Yes
Yes
No
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
Optional
No setup fee
No setup fee
Additional Details
Discount available for annual pricing. Multi-Year/Volume discounts available (500+ hosts/mo).
Dynatrace was cheaper but, in my opinion, its setup, features, and overall user experience do not come close to what Datadog can offer, making it more of a pain to use and not worth the cheaper cost over Datadog (especially if migrating away from Datadog to Dynatrace).
1. Grafana is good, but a lot of integration is required for it to work. .that not the case of Datadog 2. Faster to set up Datadog instead of Grafana 3. Alerting in Datadog feels much easier thanin Grafana.
we primarily use Kubernetes, and Prometheus is great for collecting time series metrics, especially in Kubernetes. and Grafana is used for dashboards. As these are open source, we host them and manage them internally. We choose Datadog because of its logs, traces, and …
All other tools dont have all the features which Datadog provides. Easy to use from UI where other may have complicated UI or no UI at all to create monitors. Consider like AWS Grafana, we have limitation to create monitors from UI. There is no recurring downtime for monitors. …
First think first - it's easy to use, and very easy to implement in any infrastructure. It provides a custom dashboard and monitors. I’ve used or evaluated Grafana, Prometheus, Amazon CloudWatch, and Dynatrace, and each tool has strong capabilities. Prometheus + Grafana provide …
Datadog is a more complex but complete solution than any of the other Log Aggregation, monitoring, or general observabilty tools that we have trialed. I found it easier to setup following useful and up-to-date documentation provided directly by Datadog instead of scattered …
I use Datadog because it concentrates all these features into a single tool, facilitating the learning curve that my platform and development engineering team needs in order to be able to set up the monitors/alerts/SLIs/SLOs as well as to diagnose a production issue. Its easier …
Kibana Datadog … because within our usecase we have all the events in Kibana but sampled traces in Datadog … but if we had all the traces it would have been much more useful
Datadog is best for cloud-native and fast-setup. It is more mature for infrastructure and real-time observability. The UI is more user-friendly and provides wide coverage of app insights.
I selected Datadog because of its features and the wide range of integration support. As I already told it supports more that 600+ integrations which helps and organization to keep everything in a single place and also its AI feature which is reducing the time for root cause …
ease of use and implementation, other than New Relic (which I think is terrible in every possible way), the other two support opentelemetry better, have more manageable costs and comparable basic services, but they do not have the breadt of services dd does.
It's a one-stop solution for all our needs whereas in other open-source tools, we have an operational overhead to keep and manage the uptime of these tools as well and also manage their versioning, upgrade, and patching cycle. Also if there are any bugs then we have to raise an …
Grafana has a direct plugin to Icinga monitoring solution and allowed for easy configuration for us. At the time of implementation, other services did not have such an integration. As we already had a very customized and heavily introduced monitoring solution in place, we …
Grafana blows Nagios out of the water when it comes to customization. The ability to feed almost any data source makes it very versatile and the cost is great.
We evaluated Datadog and New Relic but cost-wise, these 2 are very expensive. Prometheus does require more leg work to match the feature sets but other than time, the cost is free. Pairing with Grafana, Prometheus can pretty much match features with the big players and still …
The software is very lightweight and can be hosted with minimal resource usage. Many exporters exist for various software. Prometheus has a powerful and flexible query language (PromQL) that allows analyzing your data easy. I use this software with its various exporters, …
We considered TICK stack as an alternative to our Prometheus/Grafana setup that we have for capturing, storing and visualizing the time series data. But it seemed more complicated to learn and required a separate DB called InfluxDB to be setup. So, after all these considerations, …
Prometheus is great for quantifiable metrics. Loki is intended for log aggregation. Depending on project a different combination of data source types may be needed. However, quantifiable metrics are predominantly supported by Prometheus. Other data sources like elastic search …
It is easier to setup, but learning curve is quite moderately steep. Prometheus is a best-in-class tool for engineers and SREs in cloud-native environments. When extended with tools like Thanos or Cortex, it can rival commercial platforms in scale and capability—but requires …
As I mentioned earlier, Prometheus had an added advantage that we were able to monitor CPU, RAM, Disk Space, process monitoring which other tools did not provide us Some tools were obsolete, and other were costly when we wanted this good feature , only Prometheus delivered on …
Highly customized pricing plans to choose from. Lower pricing for the same features compared to competitors. Easy to reach the support team, which provided detailed documentation and helped set up the Prometheus. Monitoring metrics gets very easy after the integration with …
Since Prometheus is free to use and provides all the features we required we went with Prometheus if any feature is missing then we can consider other paid solutions like data dog.
Prometheus is cheaper, and you can quickly set it up compared to others. It is integrated with most of the open-source monitoring and alerting tools and can help small companies in having a cost-effective solution early in their stage.
Prometheus is similar to some of its competitors but delivers with regards to metrics; being used internally by Google and other cloud-native companies like ours gives us the confidence that the alerting industry stakeholders view it as a long-term solution that the community …
Datadog may be better suited for teams that have a more out-of-the-box infrastructure, on the primary platforms Datadog supports. You may also have better results if you have a bigger team dedicated to devops and/or a bigger budget. We found that trying to adapt it to our use case (small team, .NET on AWS Fargate) wasn't feasible. We continually ran into roadblocks that required us to dig through documentation (and at times, having to figure out some documentation was wrong), go back and forth with support, and in my opinion, waste money on excessive and unintended usages due to opaque pricing models and inaccurate usage reports, as well as broken/non-functional rate sampling controls.
Just about any organization with more than one server and more than one cluster as it scales very well. Configuration of the application takes time and finesse to fine tune to where the balance of load time and getting data quickly meets. The plugins add load time but fine tuning for the application to meet demand needs nailed down at implementation
This program works from the roots of the problem and creates a professional matrix for each of its users. This will give them more skills and resources to carry out tasks and reduce the difficulties of operating each of the processes of my work, as well as being An ally for the manipulation and operability of all your master data; Prometheus is very easy to recommend since it is a program that fulfills its mission.
The thing which Datadog does really well, one of them are its broad range of services integrations and features which makes it one step observability solution for all. We can monitor all types of our application, infrastructure, hosts, databases etc with Datadog.
Its custom dashboard feature which helps us to visualize the data in a better way . It supports different types of charts through those charts we can create our dashboard more attractive.
Its AI powered alerting capability though that we can easily identify the root cause and also it has a low noise alerting capability which means it correlated the similar type of issues.
Alert windows cause lag in notifications (e.g. if the alert window is X errors in 1 hour, we won't get alerted until the end of the 1 hour range)
I would appreciate more supportive examples for how to filter and view metrics in the explorer
I would like a more clear interface for metrics that are missing in a time frame, rather than only showing tags/etc. for metrics that were collected within the currently viewed time frame
Customer Service: since this is an open-source tool, customer service is not that great. Generally, you get all answers to your problems in online forums, but in case you got stuck, nobody will assist you in a channelised manner. You will have to find the way out on your own, and it may become frustrating at times.
More metrics for dashboards shall be added per the application being monitored. Standards metrics will work in most cases but may not in specific applications. Therefore, customised metrics shall be created for some of the industry-standard niche applications.
There are so many features that it can be hard to figure out where you need to go for your own use case. For example, RUM monitoring us buried in a "Digital Experience" sidebar setting when this is one of our key use cases that I sometimes struggle to find in the application. It appears that ECS + Fargate monitoring was recently released which is great because we had to build a lambda reporting solution for ephemeral task monitoring. But this new feature was never on my radar until I starting clicking around the application.
It is infinitely flexible. If you can imagine it, Grafana can almost certainly do it. Usability may be in the eye of the beholder however, as there is time needed to curate the experience and get the dashboards customized to how it makes sense to you. I know one thing they are working on are more templates, based on data sources
It is usable and one can learn if few people in the team are already using it. It can be difficult to understand at the beginning because of non intuitive UI and syntax of the rules. So, I've gone for 7 points as there is some room for improvement in user interface and rules syntax.
The support team usually gets it right. We did have a rather complicate issue setting up monitoring on a domain controller. However, they are usually responsive and helpful over chat. The downside would be I don’t think they have any phone support. If that is important to you this might not be a good fit.
Our logs are very important, and Datadog manages them exceptionally well. We frequently use Datadog services for our investigations. Use case: Monitor your apps, infrastructure, APIs, and user experience.
Key features:
Logs, metrics, and APM (Application Performance Monitoring)
Real-time alerting and dashboards
Supports Kubernetes, AWS, GCP, and other integrations
RUM (Real User Monitoring) and Synthetics
✅ Best for backend, server, and distributed systems monitoring.
Grafana blows Nagios out of the water when it comes to customization. The ability to feed almost any data source makes it very versatile and the cost is great.
Highly customized pricing plans to choose from. Lower pricing for the same features compared to competitors. Easy to reach the support team, which provided detailed documentation and helped set up the Prometheus. Monitoring metrics gets very easy after the integration with Grafana. It also has a sophisticated alert setting mechanism to ensure we don't miss anything critical.
The ROI mentioned during the purchase has not been achieved, however this could be due to lack of data from our side. 2 years of implementation is too early to calculate and confirm the ROI.