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
Git
Score 10.0 out of 10
N/A
N/A
N/A
Pricing
Datadog
Git
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
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Offerings
Pricing Offerings
Datadog
Git
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
Optional
No setup fee
Additional Details
Discount available for annual pricing. Multi-Year/Volume discounts available (500+ hosts/mo).
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.
GIT is good to be used for faster and high availability operations during code release cycle. Git provides a complete replica of the repository on the developer's local system which is why every developer will have complete repository available for quick access on his system and they can merge the specific branches that they have worked on back to the centralized repository. The limitations with GIT are seen when checking in large files.
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
Git has met all standards for a source control tool and even exceeded those standards. Git is so integrated with our work that I can't imagine a day without it.
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.
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.
I am not sure what the official Git support channels are like as I have never needed to use any official support. Because Git is so popular among all developers now, it is pretty easy to find the answer to almost any Git question with a quick Google search. I've never had trouble finding what I'm looking for.
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.
I've used both Apache Subversion & Git over the years and have maintained my allegiance to Git. Git is not objectively better than Subversion. It's different. The key difference is that it is decentralized. With Subversion, you have a problem here: The SVN Repository may be in a location you can't reach (behind a VPN, intranet - etc), you cannot commit. If you want to make a copy of your code, you have to literally copy/paste it. With Git, you do not have this problem. Your local copy is a repository, and you can commit to it and get all benefits of source control. When you regain connectivity to the main repository, you can commit against it. Another thing for consideration is that Git tracks content rather than files. Branches are lightweight and merging is easy, and I mean really easy. It's distributed, basically every repository is a branch. It's much easier to develop concurrently and collaboratively than with Subversion, in my opinion. It also makes offline development possible. It doesn't impose any workflow, as seen on the above linked website, there are many workflows possible with Git. A Subversion-style workflow is easily mimicked.
Git has saved our organization countless hours having to manually trace code to a breaking change or manage conflicting changes. It has no equal when it comes to scalability or manageability.
Git has allowed our engineering team to build code reviews into its workflow by preventing a developer from approving or merging in their own code; instead, all proposed changes are reviewed by another engineer to assess the impact of the code and whether or not it should be merged in first. This greatly reduces the likelihood of breaking changes getting into production.
Git has at times created some confusion among developers about what to do if they accidentally commit a change they decide later they want to roll back. There are multiple ways to address this problem and the best available option may not be obvious in all cases.
