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
IBM Cloud Pak for AIOps
Score 8.8 out of 10
N/A
IBM Cloud Pak for AIOps (formerly IBM Watson AIOps) allows users to deploy advanced, explainable AI on an open platform to assess, diagnose, and resolve incidents across mission-critical workloads. With it, users can extend the event analytics from IBM Netcool Operations Insight with real-time analysis of unstructured data, holistic correlation, and ChatOps integration; or, users can augment an existing monitoring solutions.
N/A
Pricing
Datadog
IBM Cloud Pak for AIOps
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
No answers on this topic
Offerings
Pricing Offerings
Datadog
IBM Cloud Pak for AIOps
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 can be pricey for larger scale businesses, so it really depends on your use case. For us, we have a small single deployment application and a small developer team, so our costs are mostly reasonable. There are more features than we can explore which can be somewhat overwhelming. It is mostly easy and intuitive to use but for larger scale you may consider rolling your own solutions.
IBM Watson AIOps is well suited for IT Asset Management tasks due to its insightful dashboard that leads our IT Team to track IT Asset usage/requirements in real-time. We have also enjoyed using the reports provided for IT Asset Management which we provide to our upper management team for predictive budgeting purposes. We found that IBM Watson AIOps is not as well suited for our Application resource automation as the tuning (aggressiveness) is not as tunable as we would like.
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
There is some room for improvement, but the Datadog team sends out updates frequently, and the UI is user-friendly for engineers, with no significant loading issues or region-specific problems. That was one of the key reasons we preferred Datadog; our company has employees worldwide, and it wasn't difficult to transition to the tool.
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.
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 open source issue and many problems as we have to keep 2 to 3 people aligned to manage the stack.
IBM Watson AIOps stacks up well with Turbonomic because it basically is Turbonomic. IBM added Turbonomic's feature set into IBM Watson AIOps and we were therefore quite comfortable shifting to the re-branded version introduced by IBM. We do like the fact that IBM Watson AIOps includes the functionality of Turbonomic.
