In 2000, a pivotal moment in the tech industry occurred when Marc Benioff and salesforce.com declared “The End of Software.” This proclamation led to the dawn of mainstream cloud computing and the SaaS (Software as a Service) business model. Today, nearly every application we interact with runs in a browser, powered by a cloud backend. Just as Marc envisioned, the world largely discarded on-premises software.

Distributing software via the cloud has accelerated software delivery by tenfold or even more. New features or bug fixes can now reach end-users virtually overnight. Centralized, cloud-hosted software simplifies maintenance and can scale to serve millions of users simultaneously. This shift also allowed software companies to monitor usage patterns and interactions, leading to the creation of highly optimized products and services. As a result, the cloud-hosted SaaS business model birthed new service categories, such as AWS and Google Cloud, to meet escalating infrastructure demands. Jeff Bezos launched AWS at the Y Combinator startup accelerator, making an analogy to German brewers outsourcing power generation: "Focus on what makes your beer taste better."

The cloud-hosted SaaS model undoubtedly brought manifold benefits to the Web 2.0 era. However, we're now witnessing the emergence of a new breed of software, propelled by AI models operating behind the scenes. This marks a paradigm shift in how we develop, deliver, and consume software applications and services. It is necessary to invent new types of infrastructure, tools, and security rules to support AI-based software.

When it comes to building an AI model, two things are crucial: robust algorithms and ample high-quality data to characterize the problem space. Today, many of the best algorithms are open source and sufficiently sophisticated to develop commercially viable models when provided with enough high-quality data during training. Companies that possess this data gain a significant competitive advantage, often emerging as leaders in their category. Conversely, companies lacking data resort to various means to acquire it, sometimes even through questionable methods For instance, a renowned language model development company faced multiple lawsuits for unauthorized data usage. The takeaway? AI models crave data, and companies are under pressure to satiate this appetite, regardless of the cost.

This leads to a crucial question: Do you feel comfortable entrusting your company's data to third-party vendors? Digging deeper, there are two layers of third-party involvement. The first is the cloud infrastructure vendors who provide the cloud platform, and the second is the software vendors that provide the applications that run on top of this platform. Both tiers potentially access your data, even if they vow to safeguard it and refrain from its unauthorized use.

The Risks of Third-Party Hosting

• Potential for Data Breaches:

Even reputable third-party vendors aren't immune to cyber-attacks. Outsourced data storage always comes with risks.

• Limited Oversight:

With third-party solutions, you're often in the dark about specific security measures in place. This lack of transparency can be unsettling.

• Data Sovereignty and Compliance Risks:

Using an external vendor might lead to data being stored in a different jurisdiction, with different data protection regulations.

• Shared Responsibility Model:

Many cloud hosted SasS vendors operate under this model, where they're only responsible for the security within a certain perimeter. This can create gray areas regarding responsibility in the event of a breach.

However, there has been a recent trend leaning back towards on-premises or hybrid cloud architectures, especially concerning AI data and operations. This shift is driven by security concerns and the cost implications of cloud hosting. Today, self-hosting software on the private or public cloud can offer a smoother, more robust experience than it did 20 years ago with technologies such as Docker containers, Kubernetes, and Min.io.

Benefit of Self-hosting on Cloud or On-Prem

• Complete Data Control:

At the heart of AI operations is data. Self-hosting grants organizations complete authority over their data, without any dependence on third-party vendors. This ensures that data access, management, and movement align with the company's specific needs.

• Enhanced Data Security:

Storing data in-house can, when done correctly, substantially reduce the risk of breaches or leaks. Companies can enforce their security protocols, without relying on the assurances or potential vulnerabilities of external entities.

• Regulatory Compliance:

Many industries have strict regulations regarding data storage and processing (e.g., GDPR in Europe, HIPAA in healthcare). Self-hosting gives organizations direct oversight, ensuring they can maintain full compliance without relying on third-party attestations.

• Data Sovereignty:

For organizations concerned about where their data resides (due to legal, regulatory, or strategic reasons), self-hosting ensures data remains within chosen jurisdictions.

• Transparent Operations:

Self-hosting provides clear visibility into all operations, ensuring there are no "black boxes." This transparency is crucial in AI, where understanding and explaining processes can be vital.

At LayerNext, we fundamentally believe that every company, whether a 2-person startup or a large enterprise, should wholly own their data, no questions asked. For us, size is irrelevant; control over your data is paramount. That's why we wholeheartedly embrace the self-hosted business model. By default, LayerNext is designed for self-hosting, allowing you to install it on your company's private or public cloud platform.

Some may argue that this approach could become a DevOps nightmare, replete with the usual challenges of on-premises systems, such as deployment issues, diverse OS versions and the challenge of rolling out updates to every customer overnight. We know we are stepping into really difficult waters to swim in. Yet, the inherent challenges are outweighed by the immense benefits we bring to our customers. Our engineering team is on a mission to master the DevOps for self-hosting software to provide smooth operation to all customers regardless of their size.

One last thing, historically, self-hosting was a privilege reserved for large enterprises with deep pockets. However we want to bring that capability to every AI company who is reshaping the future. At LayerNext we are reinventing the way we deploy and run software in this AI era. Please let us know what you think.