A winning team: Cloud cost management meets performance management
As I have written before in "Why capacity management is more relevant than ever in the cloud era", optimizing cloud cost is dependent on an understanding of application performance and value for any workloads running in the cloud. Or to put it differently, knowledge of cloud infrastructure cost is much more useful when you can match it with the revenue it's generating, and uncover waste as well as marginal cost issues in the process. Optimizing cloud cost always has to do with optimizing applications, and balancing performance and cost.
It's no surprise, then, that vendors in the cost management, performance management and capacity management spaces are teaming up to build integrated offerings to span the cloud side and application side.
Apptio, an IT cost management vendor, first built its own AWS cloud cost management integration in 2017. Then in late 2018, Apptio acquired FittedCloud, which had been applying machine learning to AWS cost data. This year, in a much larger deal, it then acquired well-funded Cloudability. Previously, Cloudability had already taken a "multi-cloud governance" angle and extended its offering over all major public cloud vendors. Now, Apptio has a portfolio of overlapping solutions to integrate over time, though it's still squarely focused on cost and governance, and less on the more technical application performance management aspects.
The combination of (AIOps) monitoring and cost management is also soon arriving with Virtual Instruments' acquisition of Metricly. Virtual Instruments had previously concentrated on AI-assisted optimization of storage device performance and then worked its way up the stack with server monitoring. Now, they can through AWS cost data into the mix with Metricly's integrations with AWS services.
Meanwhile, in late 2018, VMware acquired CloudHealth in what is probably the largest deal in the Cloud Management space so far. At that point, CloudHealth was probably the best-funded and most well-known cloud management solution, having started with AWS cost analysis and then adding in multi-cloud support and governance features. While CloudHealth had some notion of utilization and performance of servers in their software, it's easy to see how CloudHealth could integrate with other Vmware offerings such as Wavefront.
Of course, the consolidation of CMPs has been going on for a while - see for example Microsoft's acquisition of CloudDyn (now part of Azure Cost Management), Cisco and Cliqr (now Cloudcenter), HP's acquisition of CloudCruiser (now HPE Consumption Analytics), Nutanix's acquisition of BotMetric, and AWS's acquisition of TSO Logic.
And some monitoring vendors have been integrating cloud cost data into their offerings, such as Site24x7's CloudSpend.
In "The battle for control of multi-cloud", I've written about the ambitious expectations for the "multi-cloud" story in the context of Gartner's Cloud Management Platform MQ. I still think that CMPs are over-ambitious in wanted to handle deployment and placement of workloads across different cloud environments. However, the acquisitions in the CMP and IT cost management space show that the cloud is here to stay, that cloud cost is a real issue, and that cloud cost problems can't be solved while only looking at the cost side - a deep understanding of applications, performance and deployment options is required to solve these kinds of issues.
For those looking to introduce a standalone cloud management solution, there are still quite a few independent players left:
- Applatix Claudia (https://github.com/Applatix/claudia) - open source
- Centilytics (https://www.centilytics.com/)
- Cloudaware (https://www.cloudaware.com/)
- CloudBolt (https://www.cloudbolt.io/)
- CloudCheckr (https://cloudcheckr.com/)
- CloudWiry (https://www.cloudwiry.com/)
- Densify (https://www.densify.com/) -
- Embotics (https://www.embotics.com/)
- Flexera (Rightscale) (https://www.flexera.com)
- ITRS / Sumerian (https://www.sumerian.com/)
- Koku (https://github.com/project-koku/koku) - open source
- Morpheus (https://www.morpheusdata.com/)
- Stax (https://www.stax.io/)