Welcome!

SDN Journal Authors: Yeshim Deniz, Elizabeth White, Liz McMillan, Pat Romanski, TJ Randall

Related Topics: @CloudExpo, @DXWorldExpo, @DevOpsSummit

@CloudExpo: Article

Is SaaS Dead? | @CloudExpo #BigData #IoT #IaaS #SaaS #AI #DevOps #FinTech

The rapid growth of hyperscale IaaS platforms such as AWS is changing the SaaS playing field

Is SaaS Dead? Are We Headed to a World of ASP 2.0 or Dedicated SaaS?

The emergence of hyper-scale Infrastructure as-a-Service (IaaS) platforms such as Amazon Web Services (AWS) is challenging the traditional Software-as-a-Service (SaaS) value proposition. SaaS CEOs, investors and SaaS buyers must carefully evaluate the implications of "all-in" migration to hyperscale IaaS platforms that offer value-added platform services that go beyond simple infrastructure services. This article provides a point of view and insights into changes in the SaaS market, which has three principal drivers 1) technical, 2) business, and 3) security and compliance.

History of SaaS
SaaS solutions were born in 2000 with the promise of web-based delivery of hosted software delivered over the internet. SaaS solutions helped reduce the pain and cost associated with upgrades and maintenance headaches of "shrink-wrapped" software. The SaaS business over the last 15 years has disrupted major software platforms and in many ways helped set the stage for wider enterprise adoption of cloud services.

The early success of SaaS helped change customer behavior. Customers became comfortable with data and software running outside of their firewalls. The concept of management by SLAs was in many ways driven by the initial success of SaaS. In the past few years the trend towards SaaS creation and adoption has exploded with the adoption of hyperscale IaaS platforms by SaaS vendors.

This is the great paradox.

The emergence of public cloud IaaS platforms on the one hand is fueling the explosive growth of SaaS, but at the same time it will kill (or dramatically disrupt) SaaS as it destroys the fundamental value proposition.

Will SaaS Work in the Public Cloud Era?
The explosive and constantly accelerating growth of IaaS platforms such as AWS Elastic Cloud Computing (EC2) is changing the economics of SaaS.

Your Cloud or My Cloud?
The trend is unmistakable - public IaaS has won. Major SaaS providers as well as large enterprises that are major SaaS buyers are both migrating and embracing the IaaS cloud by going "all-in." Figure 1 shows the implications of this migration and shift.

Figure 1

As both SaaS providers and enterprise SaaS consumers start to operate on the same platform, the SaaS "value-stack" is significantly diminished. Artificial data silos are created for the enterprise when their "SaaS" data is resident right next to their enterprise data separated just by a contractual boundary. SaaS buyers in many ways "pay" twice for their data. Once for storing it in the SaaS platform and a second time for backing it up into their "cloud environment" for security or analytical purposes. Many enterprises often overlook the hidden costs associated with SaaS. These include hard network and storage costs associated with copying data back and forth for backup/retention or analytics purposes.

Is Upgrading and Updating Software Really That Hard Now?
A key tenet of SaaS was to help reduce the cost and pain associated with upgrading and maintaining "shrink-wrapped" software. In 2016 and beyond, upgrading and managing software and systems is not as hard as it used to be. Continuous integration/continuous delivery (CI/CD), microservices, serverless computing, and managed cloud-native services are making it relatively easy to upgrade and deliver software.

If both the SaaS provider and the consumer have access to the same set of tools on the same cloud platform, what is the real value-add from the SaaS service? Just the software, right?

Upended Revenue Model
Over the years most SaaS services have been delivered on a "per-user" based annual subscription model. If a SaaS license for one user is $100 per year, and the enterprise bought licenses for 100 users, the enterprise would pay $10,000 per year. In the pre-IaaS days, this might have been great economics for both parties - the SaaS vendor and the SaaS buyer. But not anymore.

The customer now wants true pay-as-you-go or consumption-based pricing. This means paying for actual transactions when actual costs are actually incurred. This is especially true now that SaaS providers migrate to IaaS platforms that offer transaction-based pricing. The SaaS providers' underlying cost basis for infrastructure and related components is truly hourly and consumption driven. This has huge implications. Let's see this through a sample scenario.

Assume those 100 users consume 100 GB of space and five cloud servers. The customer adds another 50 users but because of limited usage, the storage and compute requirements did not really change much. Why should the customer pay an additional $5,000 for those additional 50 users, if the underlying incremental costs did not change that much?

SaaS buyers are starting to think real hard when deciding between a "per-user" SaaS annual subscription model versus a "cloud-hosted" managed "shrink-wrapped" solution. Given that in both scenarios the "raw material" is the same cloud platform.

Security and Compliance
Most newer entrants to the SaaS market underestimate the cost and complexity associated with the "Ops" part of the business. Many SaaS providers struggle with issues around vulnerability management, security scanning, compliance reporting, backup and recovery and service uptime. There are many factors for this. One of them is the changed cybersecurity environment. Buyers are demanding evidence of security best practices. Industry recognized certifications such as HIPAA, FFIEC, FedRAMP, or ISO 27001 are increasingly required by customers from SaaS providers. These come at a heavy price.

Further, as enterprises get better with cloud services, they are increasingly seeking access to their data stored on SaaS platforms. Data residency, access control and enterprise risk management are difficult questions that must be addressed. Now that the enterprise and the SaaS service are operating on the same cloud platform, the argument for "dedicated" SaaS is increasingly coming up.

Fast Forward to the Past?
As enterprises get comfortable with hyperscale cloud platforms such as AWS, there is a serious need to carefully evaluate the SaaS value proposition. New distribution models such as cloud marketplaces are making it easier for cloud buyers to find and deploy enterprise software within their own cloud environments. Wanting control on the data and emergence of data science are causing enterprises to view their data more strategically.

Will we see Application Server Providers (ASP2) that focus on delivering great software hosted within "dedicated" cloud environments or within the clients' cloud environment? Will the SaaS revenue model change from a per user annual subscription to a pay-per-call model as serverless computing takes shape?

Making the right strategic decisions is critical for both cloud services buyers and providers to ensure their ability to sustain and thrive in the cloud computing age. The emergence of serverless computing, cloud marketplaces and pay-per-call transaction pricing offer entrepreneurs a rich canvas to build the next generation of cloud-native services.

Industry Perspective
"Pricing, governance and security are key for enterprises consuming multiple SaaS services. It becomes challenging to understand the security footprint of the system as a whole as the number of SaaS services consumed increases." - Derek Collison, CEO and Founder, Apcera.

Call-to Action
Cloud executives both on the vendor and enterprise buyer sides must carefully evaluate and understand new cloud deployment and consumption patterns. Taking a strategic look at Managed IaaS, PaaS, Container-as-a-Service or Micro Marketplace deployment models is critical.

More Stories By Gaurav Pal

Gaurav “GP” Pal is CEO and Founder of is stackArmor. He is an award-winning Senior Business Leader with a successful track record of growing and managing a secure cloud solutions practice with over $30 million in annual revenues focused on US Federal, Department of Defense, non-profit and financial services clients. Successfully led and delivered multi-million-dollar Amazon Web Services (AWS) cloud migration and broker programs for US Government customers including the Department of the Treasury, and Recovery Accountability & Transparency Board (RATB) since 2009.

GP is the Industry Chair at the University of Maryland’s Center for Digital Innovation, Technology and Strategy (DIGITS). He has strong relationship-based consultative selling experience with C-level executives providing DevOps, Managed Services, IaaS, Managed IaaS, PaaS and SaaS in compliance with US FedRAMP, FISMA, HIPAA and NIST Security Frameworks. He has a successful track record of delivering multiple cloud solutions with leading providers including Amazon Web Services (AWS), Microsoft, Google and among others.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


CloudEXPO Stories
While some developers care passionately about how data centers and clouds are architected, for most, it is only the end result that matters. To the majority of companies, technology exists to solve a business problem, and only delivers value when it is solving that problem. 2017 brings the mainstream adoption of containers for production workloads. In his session at 21st Cloud Expo, Ben McCormack, VP of Operations at Evernote, discussed how data centers of the future will be managed, how the public cloud best suits your organization, and what the future holds for operations and infrastructure engineers in a post-container world. Is a serverless world inevitable?
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-centric compute for the most data-intensive applications. Hyperconverged systems already in place can be revitalized with vendor-agnostic, PCIe-deployed, disaggregated approach to composable, maximizing the value of previous investments.
Wooed by the promise of faster innovation, lower TCO, and greater agility, businesses of every shape and size have embraced the cloud at every layer of the IT stack – from apps to file sharing to infrastructure. The typical organization currently uses more than a dozen sanctioned cloud apps and will shift more than half of all workloads to the cloud by 2018. Such cloud investments have delivered measurable benefits. But they’ve also resulted in some unintended side-effects: complexity and risk. End users now struggle to navigate multiple environments with varying degrees of performance. Companies are unclear on the security of their data and network access. And IT squads are overwhelmed trying to monitor and manage it all.
Machine learning provides predictive models which a business can apply in countless ways to better understand its customers and operations. Since machine learning was first developed with flat, tabular data in mind, it is still not widely understood: when does it make sense to use graph databases and machine learning in combination? This talk tackles the question from two ends: classifying predictive analytics methods and assessing graph database attributes. It also examines the ongoing lifecycle for machine learning in production. From this analysis it builds a framework for seeing where machine learning on a graph can be advantageous.'
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.