I Have Given You a Service, If You Can Keep it

In my world of technology operations, two major themes recur again and again (redundantly):

  1. Incentives
  2. Litmus Tests

I have written about incentives extensively on this blog. In short, as the saying goes, “you get what you measure.” Don’t expect extra customer handholding if you measure your support team by time spent on issues or minimizing average ticket time. Sure, you need to operate cost-effectively, but the key word is “operate”.

It is similar to security and compliance. I have very few security and compliance people whom I really respect. Most of them are either too technical to get the business, or so security and compliance focused that they’d be in heaven if we froze everything. No business, but definitely no security issues!

The goal, in both those cases, is to operate a business. If you are 100% secure but serving no customers, the security is worthless. Similarly, if you are closing tickets at the right of 500 per minute but every customer is dissatisfied, you have great metrics… and will be out of business soon enough.

The other theme that recurs is litmus tests, evaluating how well you really operate according to your stated principles.

No matter how much you say you may operate in a given manner, there are litmus tests, evaluations, you can apply to determine if you really are operating in that manner or not.

There are litmus tests for everything from a positive workplace environment to product vs sales driven to service levels.

Companies often struggle between being product-driven and sales-driven. Sales-driven companies chase each sale, and quickly become custom shops with consulting margins, operating costs, scale issues and valuation multiples. Of course, the best product-driven companies evaluate every major deal that does not fit within its parameters to see if it is this customer or a sign of the market, but if it doesn’t fit, they just don’t do it.

The litmus test for product-vs-sales is simple. You are at $10MM and pushing for growth. In your door walks a marquee customer, perhaps Google or JPMChase or GE. They are looking for a deal worth, say, another 5-10% of your top line. The only catch is that their requirements are so onerous, and their specific needs so different from what you have on offer, that you are likely to get sucked into it for months on end. The deal is unlikely to be profitable for some time to come, if ever.

Do you take it?

Product-driven company says “no”, sales-driven company says “yes”.

It is fine to be either kind of company, as long as you are honest about what you are and are structured for it. Which are you?

A great example of the service litmus test came courtesy of CloudFlare and their issues this past week. I respect CloudFlare’s transparency about the issue in their blog post here. However, my favourite line is in the sixth paragraph:

One of the advantages of being a service is that bugs can go from reported to fixed in minutes to hours instead of months.

The litmus test of being a service is precisely that. Without killing ourselves, without downtime, without “all hands on deck”, what is your lead time “from reported to fixed”?

Microservices (or nano-services) and DevOps help tremendously, but the fundamental difference is between being a product firm and a services firm. In a way, that is another litmus test. If we had to switch from monolithic and “walls of separation” to microservices and devops, could we do it? Product companies have great difficulty, services companies much less.

CloudFlare, clearly, not only is selling a service, but they are operating a service.

Conclusion

Ask yourself, do you have a litmus test for every part of your strategy that is crucial to your success? Do you pass it?

If you don’t have it, devise it; if you don’t pass it, change whatever is necessary.

If you need help, don’t hesitate to ask us.

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Amazon: Speed and Ease vs Vendor Lock-In

A few weeks ago, Amazon Web Services held its annual AWS re:Invent conference. Unsurprisingly, they announced, yet again, a slew of new services, all meant to ease adoption and management of technology services.

Yet, something felt a little amiss:

Not only are SaaS firms getting nervous, but plenty of large firms, as well. As Benoit Hudzia pointed out, many on-premise software giants, including Oracle/PeopleSoft and SAP, should be getting nervous (but perhaps are not):

However, as nervous as these companies must be – feeling a lot like many Independent Software Vendors (ISVs) in the 1990s in the face of Microsoft – many other technology builders, including IT departments, are faced yet again with an old dilemma:

Do we move faster and cheaper with AWS services, or do we avoid vendor lock-in?

This is a conversation I have had with technology executives over the last several years; the number of these conversations is increasing. They tend to fall into one of three camps:

  1. “Speedists” are willing to pay the price of vendor lock-in to get faster time-to-market and less infrastructure to manage.
  2. “Optionists” adopt some cloud services to gain cost/speed advantage, but want to keep their vendor options open.
  3. “Balancers” want the speed advantages of Amazon Services, but also want to take a moment to think about what their options will be in a year or three.

I have had discussions with all three types of people, and have learned from all. I believe that all three can be correct, depending on your circumstances. Or, in other terms, there is no 100% correct-all-the-time answer.

One point the optionists and balancers have made is that the gap between “have Amazon run it” and “run it yourself” is much smaller than it used to be.

In the old days (say, five years ago), if you wanted to run a performant, distrubuted in-memory key-value store, you had to pick one, configure it on multiple hosts, worry about creating a cluster, configure scaling, etc. Just using newly-announced ElastiCache was much simpler.

Nowadays, with pre-packaged and easy-to-configure container images of most open-source products, and container orchestration like Kubernetes or Swarm or Rancher to scale and schedule it for you, the incremental effort of running that key-value store remains real, but a fraction of what it once was.

It still is easier to just have Amazon run it for you, but it no longer is massively easier.

So what do I tell balancers, executives who ask, “should we launch our own or use AWS services?”

  1. For basic IaaS, go for the cloud. Except in certain circumstances (high-performance computing, low-latency trading, specialized hardware like voice, sometimes massive scale), the cost and flexibility gap between cloud and do-it-yourself remains extremely large. In addition, the lock-in is low as tools have become pretty adept at switching between cloud providers.
  2. For services peripheral to your core, use a service. Don’t build a massive scale communications engine between lightbulbs and your core application, and don’t think you are going to build the next email sender (unless that is your business). Normally, it is faster and cheaper to buy it than to build it, and it is peripheral anyways. That having been said, try to abstract it as much as possible. For example, give the software on those lightbulbs a URL you own and, if possible, use a standard API. If you ever need to switch, you won’t need to update software on 100MM lightbulbs, just a DNS entry!
  3. For core services – message queues between microservices, key-value stores for caching, etc. – well… it depends. This is where you should think long and hard about how important it is to run your app on a laptop, in AWS, in Google Cloud, Azure, Joyent, on your own hardware, and how important it is to be able to switch between them with ease.

I do not have an immediate answer to the tough core questions, but there are people I respect greatly in each of the Optionist and Speedist camps. That, alone, should give pause to run too quickly in one direction.

 

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On to Nano-Services

A few weeks ago, I had the pleasure of meeting Pini Reznik, CTO of container consulting firm Container Solutions, in Berlin. It may appear strange that an independent consultant who spends a lot of time helping companies with development and infrastructure strategies, much of which over the last several years has involved containers, would tout another consulting firm’s services. There is, however, plenty of work to do for all of us, and I am grateful for the thoughts and ideas they shared. Perhaps we will collaborate.

The conversations we had will provide the kernels (unikernels?) of several articles here.

Pini shared the following graphic, providing a (limited) summary of application+infrastructure development trends. Image courtesy of, reprinted with permission of, and copyright Container Solutions Ltd, 2016.

16-03-23-autopilot_timeline_graphic_2_all-01

For the purposes of today’s article, we will focus on the trends on the left-hand-side of the graphic.

The two fascinating parts to me in the graphic are:

  • It ties together developments in application architecture, development processes and infrastructure;
  •  It attempts to show how developments in one area enable developments in another, and so on. Advancements are neither independent nor unidirectional, but mutually reinforcing.

At one point, I said to Pini, half in jest, “so what is the next marketing term after micro-services? Nano-services??” Turns out he and his team had been thinking exactly that.

Calling tiny (smaller than micro) services, composed of one or a few functions, a “nano-services” probably is a better term than serverless. In both micro-services running in an explicit container and functions running in a more hidden one, there is a server, and it is abstracted away. The only differences are: the level and amount of abstraction; and the size of the service. However, serverless is more likely to stick as a catchy name than “vmless” or “instanceless” ever would have been.

I also appreciate that they put “unikernels” with a question mark. I, too, question unikernels outside of a narrow range, for two simple reasons:

  1. Containers may be sufficient, with future advancements, to fully isolate services/processes.
  2. No one really knows what will succeed in the market until it happens. Even with ironclad rock-solid arguments, a little humility goes a long way.

Nonetheless, I do believe that given the constant back-and-forth between application architectures, processes and infrastructure, “nano-services” or “serverless” (or Simon Wardley’s term Framework-as-a-Service/FaaS) will lead to some material advancement in infrastructure, just as containers enabled micro-services and then serverless; rarely is it a one-way process of evolution.

Of course, I have issues with serverless, primarily around packaging, as highlighted here, as well as the mostly closed-source/proprietary nature of Lambda and others. OpenWhisk and Open Lambda may help.

Will nano-services be serverless? Will they be something else entirely? Will they have a better name? How will the packaging issues be resolved? What about networking? How about the open source question?

For most companies, micro-services and containers are sufficiently leading-edge. For newer companies attempting to lay a longer-term path with the advantage of starting in a greenfield, for CTOs and architects at established companies who need to begin clearing the path in the woods, these are important questions.

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Why Networking is Critical to Serverless

As readers know, I have been thinking a lot about serverless lately (along with all other forms of technology deployment and management, since it is what I do professionally).

Recently, I came at it from another angle: network latency.

Two weeks ago, I presented at LinuxCon/ConainerCon Berlin on “Networking (Containers) in Ultra-Low-Latency Environments,” slides here.

I won’t go into the details – feel free to look at the slides and data, explore the code repo, reproduce the tests yourself, and contact me for help if you need to apply it to your circumstances – but I do want to highlight one of the most important takeaways.

For the majority of customers and the majority of network designs, the choice and its latency impact simply will not matter. Whether your container or VM talks to its neighbour in 25 μsec or 50 μsec is insufficient to have any impact on your application, unless you really are dealing in ultra-low-latency, like financial applications.

Towards the end, though, I pointed out a trend that could make the differences matter even for regular applications.

With monolithic applications, you have 1 app server talking to 1 database. For a moderately complex app, maybe it is 1 front-end app server with 5 different back-ends comprised of databases and other applications. The total number of communications is 5, so a 25 μsec difference adds up to 125 μsec, or 1/8 of a millisecond. It still doesn’t matter all that much for most.

Containers, however, enable and encourage us to break down those monolithic applications into separate services, or “microservices”. Where the boundaries of those services should be is a significant topic; I recommend reading Adrian Colyer‘s “Morning Paper” on it here.

As applications are decomposed, the previous single monolithic application with a single database, and thus one back-and-forth internal communication, now becomes 10 micro services, each with its own back-end. One communication just became ten, and our simple application’s 25 μsec difference just became 250 μsec, or 1/4 of a millisecond. It still doesn’t matter all that much, but it is moving towards mattering.

Similarly, our complex 6-part application became, say, 25 microservices and backends, leading to 625 μsec of additional delay, or almost 2/3 of a millisecond. Again, it doesn’t matter all that much, but it is getting ever closer.

However, with serverless, the unit of deployment no longer is a service, or even a microservice. Rather, it is a function. Even the simplest of applications have a lot of functions. Our simple application that went from 1 app and 1 database to 10 microservices actually has a not-unreasonable 250 functions in it; some of the open-source libraries I have written single-handedly have that many! If each of these is run independently in a FaaS/serverless environment, we now have 250 items communicating with others, a minimum of 250*25 μsec = 6,250 μsec or 6.25 milliseconds delay.

For our simple application, with “just” those 250 functions, the difference of a few tens of microseconds, determined by your inter-function (inter-container, under the covers) networking choice, makes a big difference.

For our complex application, with 6-parts, each of which may have at least those 250 functions, we now have 250*6*25 μsec = 37,500 μsec or 37.5 milliseconds of additional delay. That is real time.

Of course, a serverless provider, like Amazon Lambda or Google Cloud Functions, is expected to invest the engineering effort to optimize the network so that the functions don’t simply run “anywhere” and connect “however”, creating unacceptable latency. To some degree, this is what we pay them for, and a barrier to entry for additional competitors. Packaging up a container image is easy; optimizing it to run with many others in a busy network on busy servers with minimal impact is hard.

As I have written often, PaaS and DevOps and by extension serverless will eliminate many system administration jobs, but it will create fewer but far more critical and valuable systems engineering jobs. The best sysadmins will go on to much more lucrative and, frankly, enjoyable work.

Many others will run serverless environments on their own, using OpenWhisk or other open-source products. Unlike Cloud Foundry or Deis, these will require serious design effort to ensure that applications do not end up with a painful mix of easy-to-manage, performant each part on its own, and impossibly slow in toto.

Hopefully, Amazon and Google are up to the task, as well as those deploying on their own. I hope they, and you, are, but I always am happy to offer my services to assist.

 

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Can rkt+kubernetes provide a real alternative to Docker?

Last week in LinuxCon/ContainerCon Berlin, I attended a presentation by Luca Bruno of CoreOS, where he described how kubernetes, the most popular container orchestration and scheduling service, and rkt integrate. As part of the presentation, Luca delved into the rkt architecture.

For those unaware – there are many, which is a major part of the problem – rkt (pronounced “rocket”, as in this) is CoreOS’s container management implementation. Nowadays, almost everyone who thinks containers, thinks “Docker”. Even Joyent’s Triton, while it uses SmartOS (a variant of Illumos, derived in turn from Solaris), has adopted Docker’s image format and API. You run containers on Triton by calling “docker run”, just pointing it at Triton URLs, rather than docker daemons.

I was impressed with how far CoreOS had come. I was convinced late last year that they had quietly abandoned the rkt effort in the face of the Docker steamroller. Clearly, they quietly plowed ahead, making significant advances.

As I was listening to Luca’s enjoyable presentation, the following thoughts came to mind:

  1. Docker Inc., in its search for revenue via customer capture, has expanded into the terrain of its ecosystems partners, including InfraKit (watch out Chef/Ansible/etc.) and Swarm (kubernetes). Those partners must view it as a threat. One person called it, “Docker’s IE moment,” referring to Microsoft’s attack on its software provider ecosystem when it integrated IE into Windows.
  2. Docker’s API, as good as it is, is very rarely used. With the exception of a developer running an instance manually, usually locally but sometimes on a cloud server, almost no one uses the docker remote API for real management. Almost all of the orchestration and scheduling systems use local agents: kubernetes runs kubelet on each node, Rancher’s Cattle runs rancher agent, etc.
  3. Docker is really easy to use locally, whether starting up a single container or using compose to build an app of multiple parts. Compose doesn’t work well for distributed production apps, but that is what kubernetes (and Swarm and Cattle) are there for.

As these went through my mind, I began to wonder if the backers of rkt+kubernetes intend to use rkt+kubernetes as a head-on alternative to Docker.

So… what would it take for rkt+kubernetes (or, as Luca called it, “rktnetes” pronounced “rocketnetes”) to present a viable alternative to Docker?

Ease Of Use

As described above, Docker is incredibly easy for developers to use on all three platforms – Linux, Mac and Windows – especially with the latest Docker for Mac/Windows releases. rkt, on the other hand, requires launching a VM using Vagrant, which means more work and installation, which slows the process down, which…. (you get the picture). For rkt to be a serious alternative, it must be as easy to use as Docker for developers.

Sure, in theory, it is possible to use Docker in development and rkt in production, but that is unlikely unless rkt provides some 10x advantage in production. Most companies prefer to keep things simple, and “one tool for running containers everywhere” is, well, simple. Even a company willing to make the change recognizes that the run-time parameters are different (even if rkt supports Docker image format) and the “works for me” problem can return, or at least be perceived to do so (which is as important as the reality).

Docker made headway because it won over developers, then operations and IT types.

At the same time, kubernetes is not as easy to use and has a significant learning curve. To some degree, its power and flexibility once in use make it harder to get to usage. That may (or may not) be fine for an orchestrated complex production deployment; it will not fly on a developer’s laptop or DigitalOcean droplet.

To their credit, the kubernetes team has released minikube, intended to make deployments easier. We will see how well it does. In the meantime, developers by the thousands learn how to do “docker run” and “docker-compose run” every day.

So:

  1. Starting and running containers must be made much easier.
  2. Starting and running container compositions must be made much easier.

Killer Capabilities

However, even if rkt+kubernetes manage to equal docker in ease-of-use and feature set, they still will just be playing catch-up, which is not a good game to play (ask AMD). In order to win over developers and systems engineers, rkt+kubernetes must be as easy to use as Docker and it must offer some unique capability that is not offered by Docker. Preferably, it cannot be offered by Docker without great difficulty due to some inherent market, corporate or technology architecture structure.

It needs to be something that is  inherently doable, even natural, due to rkt’s design, yet difficult due to Docker’s design. The goal would be to make Docker play challenging catch-up.

What would such a feature or capability set be? I have some early ideas, but that is the job of CoreOS’s (or Google’s or CNCF’s) product managers to figure out. That is what they pay them for.

Why Do I Want It?

I love Docker and CoreOS. I use their products on a regular basis. They have made my life, and those of many clients and colleagues, immensely easier. I have met people at both companies and respect their professional skills.

Even more than any one product or company, however, I do love competition. It benefits customers primarily, but even the competitors themselves are driven to better services and products, and hence profits.

I want rkt+kubernetes (or some other combination) to provide a serious, viable alternative to Docker to benefit me, my clients (current and future), my colleagues, all technology firms and IT departments, and especially to benefit Docker, CoreOS and Kubernetes themselves.

 

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DevOps in the 1990s

Last week, I had the pleasure of attending LinuxCon/ContainerCon Europe 2016 in Berlin. Besides visiting a fascinating historical capital – there is great irony, and victory, in seeing “Ben-Gurion-Strasse” – or “Ben Gurion Street” – named after the founding Prime Minister of Israel in the erstwhile capital of the Third Reich. And while I had many a hesitation about visiting, the amount of awareness, monuments and memorials to the activities of the regime in the 1930s and 1940s was impressive.

Being a technologist with a love of operations and improving them, I had many conversations with very smart people people about operations and especially DevOps.

While engaged in one of these conversations, I suddenly had a realization: we were doing DevOps in 1995! 

Morgan Stanley IT of the 1990s, for which I have many fond memories, and to which I am grateful (especially Rochelle who convinced me I would be insane to accept the other offers on the table… she was right), was a very cutting-edge, experimental place. We took risks, failed at some of them, and succeeded at others.

Our leaders rewarded risk-taking. Fail reasonably, get rewarded; succeed, get rewarded more. Do something really stupid, of course, let alone the same stupid thing twice, and you paid for it.

How did we do DevOps?

There are lots of definitions of DevOps. One of the premiere practitioners, Jez Humble, is quoted as saying it is:

a cross-disciplinary community of practice dedicated to the study of building, evolving and operating rapidly-changing resilient systems at scale.

Nowadays, that often involves several key elements, including:

  • Developers taking ownership of the entire application lifecycle, right down to pushing the button to deploy to production;
  • Automation to enable developers to deploy to production safely;
  • Automated testing systems – continuous integration pipeline – to guarantee full testing before something goes out to production.

The key element here, though, is the culture. It is one where everyone, not just the operations teams, is responsible for live systems. Developers, the people who actually build the software systems, are the ones to make them live, and they are the ones to be alerted, whether at 2pm or 2am, when something goes wrong.

While we did not have all of the elements, we had two very important ones.

  1. Every developer was responsible for his or her application from building it right through to pushing to production and making it live.
  2. Automation to enable that process.

Twenty years ago, we built a system that told developers, “you own it, you push it, you fix it.”

I wish I could tell you it was my brainchild, or that I was the chief architect. Unfortunately, I was neither. I just played my part. The real architect was Phil Moore.

Here was how it worked.

We had a single, global filesystem namespace. Anywhere you went in the world, if you were connected to the Morgan Stanley network, “/root/whatever” pointed to the exact same thing. And if you went to “/root/dist/something“, well, that something was a local replica, right here in your city (or even building) of a read-only application.

What was a developer’s workflow?

Developers built apps in “/root/dev/something“, where something was unique to their app. They built it in a version directory, e.g. “/root/dev/something/1.2.3/”.

When a developer was convinced it was ready, they’d run a command called “vms” (no relation to DEC VMS), that pushed that version 1.2.3 of something out… everywhere. Every single location in the world now had a local, replicated identical copy of something.

Here was the best part. When ready, the developer could run a different “vms” command to make version 1.2.3 live. From that moment (well, it took a few moments to propagate worldwide), anyone launching something would get that version. Not operations, not some release manager, but the developer who owned it.

Did it break stuff? All the time. I lost track of the number of things I broke myself (often saved by others). But we also delivered stuff at a pace unheard of at the time… and it showed.

We did DevOps at Morgan Stanley 15-20 years before it became popular. And I only realized that what we were doing was DevOps, way ahead of its time… last week!

 

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Why Aren’t Desktops Managed Like Containers?

Containers, the management and packaging technology for applications, are useful for many reasons:

  • Packaging is simpler and self-contained
  • Underlying operating system distribution becomes irrelevant
  • Performance, therefore density, and therefore cost, is much better when working without a hypervisor layer

To my mind, though, one of the most important elements in any technology is how it affects culture and incentives. For example, MVC development frameworks are helpful for many reasons, but the most important is that it encourages (and sometimes forces) a cleaner way of thinking about and building software.

The particular benefit of container packaging, and particularly Docker images and ACI, is the encouragement to think cleanly about immutable applications and mutable data as distinct entities.

When I run an application as a container, I need to think about both of those elements:

  1. My application binary is identical to every other instance of that application running everywhere else in my company and, if a public image, the entire world.
  2. My data is kept outside the container and made available to/from the container.

Practically, this means that I can take any computer instance at all (that supports the container format), tell it to run my given images as containers with a given set of data, and I am precisely where I started before.

Many systems have arisen over the years to simplify this replicability, from cloud-config to chef/puppet to docker-compose/fig.

This near-instant replicability is the basis of the “cattle not pets” paradigm of systems. If I can reconstruct any system with a high degree of reliability and speed, I don’t have to worry about each system; they become disposable.

This begs the question: why are desktops still pets?

This past weekend I upgraded my (very old) MacBook Air from El Capitan (OS X 10.11) to Sierra (macOS 10.12).

Since it is pretty old, and has accumulated a lot of “cruft” over the years, I decided to do a clean install:

  1. Back up all of my data. Actually, back it up twice: once to an online backup service for critical data, and the entire system via Time Machine to a local drive.
  2. Wipe my drive and do a clean Sierra install.
  3. Restore my data from my backup drive.

Unfortunately, I hit a few snags on the way with a poorly-unmounted 1TB USB drive, which led to a very long period of macOS running fsck and me praying the drive was recoverable. (Why they don’t use ZFS to eliminate these problems is a topic for another day…)

This is the way we have been doing desktop migrations and/or reinstalls for decades; very little has changed. Sure, we now have Time Machine to ease backups, online backup services like BackBlaze/Carbonite/CrashPlan, and Migration Assistant to ease the restore. Even so, it shouldn’t be that hard.

Here is what desktop migrations should look like:

  • All applications are immutable images, just like container images.
  • All data is separate from the application and available locally and encrypted online.
  • The entire set is stored as a single configuration, like a compose file.

Restoring my entire system requires just one piece of information: the configuration file.

Upon start, I feed my configuration file to the operating system, which does the following:

  1. Install the standard images of the correct versions of my applications
  2. Replicate my data from the cloud
  3. Finish

To some degree, this is how iOS works with iCloud backup. Of course, iOS has a much more rigid (and enforced) rules as to what an application can and cannot do. This allows it to constrain how apps are packaged; where data resides; and how many pieces of information are required to replicate them (exactly two: app ID+version; and data location).

However, achieving this on a desktop doesn’t require rigid constraints on a general purpose operating system. As long as three simple rules are met, applications will comply:

  1. Make is easier to follow the rules than not.
  2. Make all necessary services that an app could require available.
  3. Make it obvious to customers see how easy it is when an app “behaves”.

It shouldn’t be too hard to get general purpose operating systems like Mac, Linux and Windows there as well. It is high time desktop operating systems caught up to the advancements elsewhere in computing.

The 1990s are calling; they want their operating system management back.

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Is the Real Uber Threat to Hertz?

It has become commonplace to forecast that Uber, Lyft and other ridesharing services are a strategic threat to car manufacturers. After all, if “everyone” uses Uber, why would they bother owning cars?

The problem with that argument is that it assumes that “everyone” lives where Uber and Lyft are headquartered: in a dense urban area with very little parking, going to other places nearby where there is lots of traffic and very little parking. Not everyone lives in San Francisco, New York, Paris and the City of London.

For people who spend the overwhelming majority of their days in such locations, car ownership always was a challenge. In the few years that I was young and single or newly married and working in Manhattan, I didn’t own a car either. I took the subway daily. If I needed a car for a few days, I just rented one.

However, many people live lives that do not quite fit that mold. They live in suburban or rural locations with several miles between places they go every day. To a city dweller, 3 miles is a distance. A suburban dweller can drive 20 miles on a normal day; a rural resident will do double that.

For those people, car ownership never was an expensive pain that supplements public transit. It was a necessary mode of transport, something that makes life livable. These people are highly unlikely to give up car ownership.

If the few urban who have not yet given up their cars will give them up, and the much larger base outside of urban cores will not, to whom is Uber a serious threat?

First of all, Uber poses a major threat to the taxi monopolies, the medallion owners. These groups own the monopolized channels by which a potential taxi driver could earn some income. With Uber and Lyft, these people can use regular (not specialized, and therefore expensive) cars they can afford to own, and do not have to pay exorbitant fees to license a medallion. This is a key reason why ridesharing often is half the price of a “normal” taxi, and why the medallion owners and the commissions and politicians in their pockets fight Uber and Lyft tooth and nail.

To whom else are they a threat?

Hertz. Avis. Enterprise. Car rental agencies.

The majority of the revenue for these companies comes from business and leisure travelers who arrive in SFO or EWR or ORD and rent a car. Public transport is great if you are becoming a temporary version of our urban visitor, staying in a hotel or Airbnb within short distance of all of your meetings.

But if you are covering a broader area, you need to get a car. Everyone who visits Silicon Valley, Northern Virginia, Los Angeles, New Jersey, Westchester County rents a car to get around.

After all:

  1. Taxis are too expensive to cover those distances
  2. Taxis often are unavailable at the desired hours or locations
  3. You need to know the numbers for the local taxi service, and sometimes have cash to pay them.

Ridesharing eliminates all of these issues.

  1. At half the cost of a “normal” taxi, ridesharing can be very competitive to car rentals
  2. With a very large network, often composed of local residents, it is far easier to hail an Uber than a taxi
  3. With a single app connected to your card, it is as easy to get and pay for a taxi in Madison as Manhattan, Prague as Palo Alto

This past week, I visited Silicon Valley, and for the first time in decades did not rent a car. I simply used Uber to get around, and life was better. To boot, it was cheaper. It wasn’t significantly cheaper, but the same price for more convenience wins out every time.

We must be careful not to extrapolate from a single case to broad market impact, but the market is made up of millions of single cases. If we can survive a visit to Silicon Valley without going to Hertz, who else is doing the same thing in thousands of places around the world every day?

 

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Amazon Pricing Should Be Customer-Centric

Today, I had a very interesting discussion with Rich Miller, a consulting colleague who has been around the block more than a few times.

One of the interesting points he raised is that Amazon’s AWS pricing doesn’t quite work for enterprises.

Let’s explore how it is a problem and why it is so.

At first blush, Amazon’s pricing is intuitive: use an hour of an m4.xlarge, pay $0.239; use 2 hours, pay $0.478; use a whole month’s worth, pay $0.239*720 = $172.08.

Of course, if I know I am going to use that m4.xlarge (or a lot of them) for a whole year, I should be able to get a discount for committing. Indeed, Amazon offers that type of commitment pricing, with varying discounts that depend on if you commit for one year or three years.

This type of pricing seems to make sense. Amazon knows it will get paid to provide 8,760 hours of that m4.xlarge for the year. In exchange for that knowledge and its ability to plan, it can afford to give the committer (you) a decent discount.

What’s the problem with it?

If you are a small startup or business, not much. You figure out that you need 2x m4.large, 5x t2.small, several others, and just commit.

But enterprises don’t work that way. Enterprises are orders of magnitude larger than those small companies. They use hundreds, if not thousands, of each instance type.

To provide for these “elephant” use cases, Amazon has a sales team that is authorized to negotiate appropriately-priced deals, much larger discounts on the listed prices, or “rack rates”.

However, the pricing remains built around the instance types you order.

The reason is that Amazon sets its prices to suit the vendor, rather than to fit the customer.

An enterprise is not a small business with an application that just happens to be 3 orders of magnitude larger. An enterprise is a diverse conglomerations of multiple divisions and their many applications, some of which are quite large, others very small, and everything in between.

Enterprises are not a large business; they are a dynamic ecosystem of businesses.

As a dynamic ecosystem, their needs change over time, sometimes from day-to-day, but certainly within the timeframes of AWS commitments. It is nearly impossible for an enterprise to know upfront that it will need 100,000 hours of m4.xlarge and 2,500,000 hours of t2.small.

What they do know is that they will spend roughly the equivalent of 100,000 * m4.xlarge + 2,500,000 * t2.small over the next year. However, from the enterprise’s perspective, as a customer, they want to buy those as units of committed general usage, not committed specific usage.

What would buyer pricing look like? Surprisingly, it is much simpler: For $100,000 in annual committed total spend, get a 10% discount; for $1,000,000, get a 25% discount; etc.

The actual thresholds and discount ratios need not be those listed above, but the principles hold. This has several distinct advantages:

  • Like all transparent pricing, it eliminates a lot of the effort, or friction, of signing up large customers
  • It makes it possible for Amazon to eliminate a lot of the sales labour effort
  • Most importantly, it makes the pricing model fit the customers

So why doesn’t Amazon do it? I suspect several reasons:

  1. It involves bearing the usage risk
  2. It requires a lot of effort to migrate that risk
  3. It requires thinking as a customer, rather than as a vendor

Mindsets can be changed over time. Risk of instance supply-demand mismatch is an issue, but the reality is that customers are absorbing this risk every single day. It is costing them time and money to figure out how much of each to buy. Make it easy for them, and they will buy more.

Of course, every service for a customer – of which removal of risk and effort is one – is a business opportunity. Amazon can – and should – build it into the pricing. Enterprises happily will pay a slightly lower discount in exchange for the flexibility such a model provides.

Summary

Your pricing should match your customers’ needs, not your supply structure. If providing that kind of pricing model means you absorb risk and effort from our customers, it is a revenue opportunity; build it into your pricing.

Does your pricing reflect your customers’ needs? Ask us to help.

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Architect Your Product Before It Holds You Back

Architecture determines capabilities.

This is not new. Anyone who has planned and architected a new product, or has tried to retrofit capabilities for which a platform has not been architected, knows it first-hand.

Yet, time and again, I come across products that have not been planned, and therefore architected, around reasonably expected capabilities.

Sometimes I see these as a user.

Last week, a client wanted to give me access to their Dropox Team account, so we could share information. They did the apparently smart thing: they sent me an offer to join their Dropbox Team.

That is where the problem began. If I join their team, my entire account is part of their team. Sure, I get all of their account permissions, and they pay if I use too much storage, but my entire account is open to them. I have some personal materiel there, but far more importantly, I have confidential client information there. Many clients have used Dropbox to share plans, materiel, even code with me.

This client should not have access to that data. It might even violate my confidentiality agreements to join their Dropbox Team.

Obviously, I am not the only person with this problem. What does Dropbox recommend?

Set up a personal and a business dropbox account and link them.

Of course, you only can do that for two accounts. They are asking me to limit how many to whom I can connect. Making it worse, each device on which I use Dropbox is connected to just one account.

The workflow I want is simple: let me remain me, and let a subset of me (a folder) become part of this client’s Team space. Dropbox does noted cannot provide this feature.

Essentially, Dropbox is designed and architected around the idea that a person’s account belongs to a team, or company, in its entirety. The idea that multiple people collaborate independently is foreign to how they first built Dropbox users, or at least teams. Put in other terms, Dropbox users and teams are built around 1970s or 1980s work environments.

  • You have a job; you work there, and everything you do there belongs to the company.
  • You have a home; you live there, and everything you do there belongs to you.

Based on the feedback and company responses in the forums, it seems clear that they started this way, and now are constrained from doing anything different without significant re-architecture.

Other times I see this issue from within companies with whom I have worked. It always is unfortunate to see CEOs frustrated that a relatively simple change is difficult to nearly impossible, requires multiple “hacks”, and challenges the stability and scalability of the product. Product says, “we need this feature/capability/flexibility to grow the business”; engineering says, “implementing this feature/capability/flexibility will risk the very service we need to run the current business, let alone grow it.”

Part of the root cause is the severe constraints inherent in the early days of a company. These manifest themselves in two ways:

  1. Plans: Some companies know that they need to plan for reasonably expected use cases, yet do not have the time to do so. As the saying goes, “Having scale (or usage, or diverse market, or ….) problems is a good thing to have. It means we are making it. Why waste energy beforehand?”
  2. Skills: Many companies, on the other hand, simply do not have the product management capabilities and architectural chops to think these issues through. They plow ahead, gain some success, and become frustrated when bolting on additional capabilities becomes expensive and even dangerous to product growth and stability. (When the founding team for a tech startup has no engineer, watch out.)

Even if you have the skills, and you think it through, it is a real challenge. How do you build for the future, without sacrificing too much precious early time, blood and treasure, to those potential future plans?

The answer is specific to each and every case. But a simple rule of thumb is to plan sufficient flexibility that you do not constrain yourself in key areas, while implementing only those parts that you need for the immediate future.

A simple example is complex access control. Your future targeted enterprise customers will insist on complex levels of role-based control and auditing; your early customers, the ones who get you enough traction to go after the whales, just want simple access.

  • If you build a simple binary yes/no for access or sharing, you may have to rip out the entire thing and replace it with something that can do enterprise-level controls and audits while replicating on top of it the exact same interface and methods you had before, all without causing disruption. Worse, other parts of the system will start to assume this design, making future changes a massive undertaking.
  • If you build a full-blown access-control system, with audits, management interfaces, APIs, you may never get to early stage customers, let alone enterprises.

I dealt with this precise issue with a company. My recommendation? Build in enough flexibility for the future, but only implement the parts you need now.

  1. Access:
    1. Build a role-based access control system now using off-the-shelf (or off-the-github) components.
    2. Implement precisely one role right now, “owner”. Provide no management interface, no cross-account controls, nothing.
    3. In the future, add new interfaces and new roles, but rip out nothing.
  2. Audits:
    1. Log every activity somewhere.
    2. Do absolutely nothing with these logs now. In the future, we can add review capabilities.

Unsurprisingly, it worked. It took, at most, a few weeks longer to get the first version, but we had bought ourselves a sufficiently flexible platform to do what we needed in the future.

Far more importantly, we had forced every other part of the system to be aware of access and auditing as they were built, making the future transition several orders of magnitude easier.

Summary

It is critically important to plan, design and architect your systems to be able to support reasonably expected future capabilities, while only implementing the minimum you need at each stage. Minimum Viable Product must be viable, not just minimum.

If you have reached the point where your early designs and their implementations struggle to deliver the capabilities you need now, find where you need to rearchitect, but get those areas you can with minimum disruption… and do it in a way that you plan for the next stages as well.

Struggling to plan for that future? Challenged by your current platform’s ability to deliver your requirements? Ask us to help.

 

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