HG Cloud Dynamics spend estimates are based on a machine learning model that relies on a growing collection of signals. The 3 signals below are some of the most influential.

  1. Product Traffic - How much application traffic the product delivers.

  2. Product Adoption - How widely deployed the product is across a company’s applications or infrastructure.

  3. Product Cost - How expensive the product is relative to other products in its category.

Let's look at an example

A user wants to estimate a company’s spend with Amazon EC2.

HG Cloud Dynamics's spend model will look at which applications are deployed across EC2 and the amount of application traffic EC2 is supporting (in addition to many other signals).

The model will then estimate a cost for Amazon EC2 based on actual spend data we have from cloud hosting customers with similar signals.

 

Applications, Traffic, Product Cost are some of the more influential signal to our spend model.

Improving the model with data

There are two investments HG Cloud Dynamics continuously make to improve our spend estimates.

  1. Collecting real-world product spend data from partners, customers, and even companies themselves who either openly publish their costs or share them privately.

  2. Expanding our signal library by building new features into our sensor network.

Both of these ongoing data collection activities feed the spend model intelligence that makes it better over time.

Common Challenges 

HG Cloud Dynamics spend model can calculate product spends for over 90% of businesses world-wide. However, there are challenges when trying to estimate spends in certain situations.

Outliers and massive companies

When it comes to estimating product spends for Netflix, Facebook, Apple, and similar-sized businesses, the spend model will always underestimate real-world spend, sometimes by a lot. This is because the scale at which these companies operate is unlike that of other businesses so the model doesn’t have a lot of real-world data to rely on.

These spend estimates in this case should be used as relative measure of scale rather than one that's absolute.

Private and unreachable infrastructure

Another challenge is getting comprehensive visibility into a company's traffic and application infrastructure. If a company's applications are entirely hidden from view, inside of a VPC (virtual-private cloud) or behind a firewall that's blocked off from sensors, there won't be a direct view into the applications and workloads running there. While the HG Cloud Dynamics spend model does a good job at calculating spend that is not visible, it can be particularly challenging when there isn't data available. This is another reason HG Cloud Dynamics maybe low at times.

How to use spend estimates correctly

Note: HG Cloud Dynamics spend estimates should only be viewed relative to other spends in the Intricately dataset. In other words, HG Cloud Dynamics spend estimates should not be compared to actual real-world spends but instead, to other spends in the Intricately dataset.

It's a common misconception that HG Cloud Dynamics's spend estimates are intended to communicate actual spend. 

HG Cloud Dynamics spend estimates are designed to accomplish two things:

  1. Communicate a directional sense of product usage.

  2. Allow users to perform math and relative comparisons of one spend with another.

Users should keep this in mind when HG Cloud Dynamics spend estimates. HG Cloud Dynamics provide an unprecedented view into relative product usage but only the company will know its true spend. However, users should be confident that HG Cloud Dynamics's methodology delivers directionally accurate results for the majority of businesses. 

A deeper dive into data collection

To address the challenges of estimating infrastructure costs, HG Cloud Dynamics sensor network was purposely designed to collect detail on product deployments, application configurations, application traffic, product adoption/usage and more.

A few examples of the types of infrastructure and data monitored are:

  1. The web, mobile, and back-end applications a company operates

  2. The operating environments a company manages

  3. The cloud and infrastructure products each application and environment relies on

  4. The usage and application traffic of each product

Discover, measure, monitor and repeat

Some core concepts used in the HG Cloud Dynamics spend calculation: 

  • Application & environment discovery: HG Insights is continuously searching for new applications and hosting environments to ensure they are captured as soon as they are launched.

  • Service change detection: Monitoring agents detect when an application starts or stops using a cloud service. These events typically signify a migration from one provider to another and will impact spend, sometimes significantly.

  • Traffic estimation: Traffic is a key ingredient to HG Cloud Dynamics spend estimates. HG Cloud Dynamics is agnostic in terms of its view into application traffic and when combined with third-party log and audience measurement data, the end result is the most complete and unbiased view of global traffic and application demand.

HG Cloud Dynamics uses substantial data collection, machine learning, and model refinements to continuously improve the accuracy of spend estimates. By establishing a consistent method for understanding spend, HG Cloud Dynamics is able to provide valuable insight into the cloud and data center ecosystem.