Tag Archives: capex

Cloud Gravity & Shards

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This post is the final post laying out a rethinking of how we view user and buyer motivations for public and private clouds.

In part 1, I laid out the “magic cube” that showed a more discrete technological breakdown of cloud deployments (see that for the MSH, MDH, MDO, UDO key).  In part 2, I piled higher and deeper business vectors onto the cube showing that the cost value of the vertices was not linear.  The costs were so unequal that they pulled our nice isometric cube into a cone.

The Cloud Gravity Well

To help make sense of cloud gravity, I’m adding a qualitative measure of friction.

Friction represents the cloud consumer’s willingness to adopt the requirements of our cloud vertices.  I commonly hear people say they are not willing to put sensitive data “in the cloud” or they are worried about a “lack of security.”  These practical concerns create significant friction against cloud adoption; meanwhile, being able to just “throw up” servers (yuck!) and avoiding IT restrictions make it easy (low friction) to use clouds.

Historically, it was easy to plot friction vs. cost.  There was a nice linear trend where providers simply lowered cost to overcome friction.  This has been fueling the current cloud boom.

The magic cube analysis shows another dynamic emerging because of competing drivers from management and isolation.  The dramatic saving from outsource management are inhibited by the high friction for giving up data protection, isolation, control, and performance minimums.  I believe that my figure, while pretty, dramatically understates the friction gap between dedicated and shared hosting.  This tension creates a non-linear trend in which substantial customer traction will follow the more expensive offerings.  In fact, it may be impossible to overcome this friction with pricing pressure.

I believe this analysis shows that there’s a significant market opportunity for clouds that have dedicated resources yet are still managed and hosted by a trusted 3rd party.  On the other hand, this gravity well could turn out to be a black hole money pit.  Like all cloud revolutions, the timid need not apply.

Post Script: Like any marketing trend, there must be a name.  These clouds are not “private” in the conventional sense and I cringe at using “hybrid” for anything anymore.  If current public clouds are like hotels (or hostels) then these clouds are more like condos or managed community McMansions.  I think calling them “cloud shards” is very crisp, but my marketing crystal ball says “try again.”  Suggestions?

Cloud Business Vectors

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In part 1 of this series, I laid out the “magic cube” that describes 8 combinations for cloud deployment.  The cube provides a finer grain understanding than “public” vs. “private” clouds because we can now clearly see that there are multiple technology axis that create “private IT” that can be differentiated. 

 The axis are: (detailed explanation)

  • X. Location: Hosted vs. On-site
  • Y. Isolation: Shared vs. Dedicated
  • Z. Management: Managed vs. Unmanaged

Cloud Cost Model

In this section, we take off our technologist pocket protectors and pick up our finance abacus.  The next level of understanding for the magic cube is to translate the technology axis into business vectors.  The business vectors are:

X. Capitalization:  OpEx vs. CapEx.  On the surface, this determines who has ownership of resource, but the deeper issue is if the resource is an investment (capital) or consumable (operations).   Unless you’re talking about a co-lo cage, hosting models will be consumable because the host is leveraging (in the financial sense) their capital into operating revenue.

Y. Commitment: PayGo vs. Fixed.  Like a cell phone plan, you can pay for what you use (Pay-as-you-go) or lock-in to a fixed contract.  Fixed is generally pays a premium based on volume even though the per unit cost may be lower.  In my thinking, the fixed contract may include dedicated resource guarantees and additional privacy.

Z. Management: Insource vs. Outsource.  Don’t over think this vector, but remember I not talking about off shoring!  If you are directly paying people to manage your cloud then you’ve insourced management.  If the host provides services, process or automation that reduces hiring requirements then you’re outsourcing it IT.  It’s critical to realize that you can’t employee fractional people.  There are fundamental cloud skillsets and tools that must be provided to operate a cloud (including, not limited to DevOps).

THE 3 VECTORS ARE NOT EQUAL!

If you were willing to do some cerebral calisthenics about these vectors then you realized that they are not equal cost weights.  Let’s look at them from least to most.

  1. The commitment vector is very easy to traverse this vector in either direction.  It’s well established human behavior that we’ll pay more for to be more predictable, especially if that means we get more control or privacy.  If I had had a dollar for everyone who swoons over cloud bursting I’d go buy that personal jet pack.
  2. The capitalization vector has is part of the driver to cloud as companies (and individuals) seek to avoid buying servers up front.  It also helps that clouds let you buy factional servers and “throw away” servers that you don’t need.  While these OpEx aspects of cloud are nice, servers are really not that expensive to lease or idle.  Frankly, it’s the deployment and management of those assets that drives the TCO way up for CapEx clouds, but that’s not this vector so move along.
  3. The management vector is the silverback gorilla standing in the corner of our magic cube.  Acquiring and maintaining the operations expertise is a significant ongoing expense.  In many cases, companies simply cannot afford to adequately cover the needed skills and this severely limits their ability to use technology competitively.  Hosts are much better positioned to manage cloud infrastructure because they enjoy economies of scale distributed between multiple customers.  This vector is heavily one directional – once you fly without that critical Ops employee in favor of a host doing the work, it is unlikely you’ll hire that role.

The unequal cost weights pull our cube out of shape.  They create a strong customer pull away from the self-managed & CapEx vertices and towards outsourced & OpEx.  I think of this distortion as a cloud gravity well that pulls customers down from private into public clouds. 

That’s enough for today.  You’ll have to wait for the gravity well analysis in part 3.