Is Your SaaS Stuck in 2018? The Rise of the Cloud-Prem Requirement

One of the largest banks in the world is evaluating your competitor’s product. You know that your software has a richer feature set and is more performant, more reliable, and more secure. But you can’t even get them to take your call. After weeks of outreach, you finally get a response:

“We won’t consider your product because we don’t want to store our data in your environment. You’re locked into a multi-tenant architecture on AWS and we see that as a risk.”

But this isn’t just a one-off objection, it’s something you’ve been hearing more and more of lately. It is a recognizable pattern. More enterprise buyers, especially those in regulated industries, are shying away from software vendors that only offer a vendor-hosted solution. What was once a technical preference, is now a business blocker. You’re losing revenue, burning developer time on workarounds, and watching once-qualified leads slip away.

A tectonic market shift is underway. Enterprises now demand application delivery flexibility and total control over their data, driven by strict data residency, security, and privacy requirements. This is not your typical broad objection to cloud software anymore. It’s a rejection of exposing data through single-tenant or multi-tenant SaaS and AI platforms. The new requirement is to run these platforms under customer control, whether in a cloud-prem (in a customer VPC), on-prem (on customer infrastructure), or other Bring-Your-Own-Cloud (BYOC) model. The demand for control isn’t a passing fad, but a strategic imperative.

Timeline of the increasing requirement for customer-owned deployments due to concerns over data privacy, security, and regulatory compliance.

Today’s shifting market presents a massive opportunity for forward-looking SaaS and AI  software providers. 

A New Software Delivery Paradigm

You instantly remove a massive blocker when you embrace a cloud-prem delivery model. The enterprise no longer needs to compromise on data sovereignty, security posture, or operational control. You are now aligning with their existing procurement and compliance frameworks, not fighting against them. You have completely sidestepped the endless legal reviews and red flags from the security team.

Let’s dig into the enterprises driving this demand for a minute. Financial institutions can’t allow regulated data to leave their own cloud, governments require sovereign clouds, major retailers refuse to run on multi-tenant solutions on AWS, and the healthcare industry must safeguard sensitive data. Forward looking vendors in AI, such as Pinecone, Zilliz, and Predibase, and analytics/streaming, such as Clickhouse, Redpanda, and Ververica, have proven that a cloud-prem model not only removes these barriers, but opens up new categories of customers that were previously out of reach. 

A cloud-prem delivery model changes everything. You eliminate the need for buyers to ask for risky exceptions or fight internal battles just to justify working with you. Instead, you offer them choice and control. This alignment with their existing procurement, security, and compliance models builds trust early, shortens sales cycles, and removes the legal friction before it starts. 

More importantly, you reposition your company from being “just another software vendor” to a long-term strategic partner. Enterprise buyers don’t simply consider the current feature set, they evaluate whether or not they can trust you to evolve with them over the next five-plus years. Offering flexible delivery options sends a clear signal that your SaaS or AI platform is built to last, your company understands enterprise requirements, and you understand what it means to operate across multiple cloud environments, securely and at scale.

The Harsh Reality: Why SaaS and AI Teams Struggle

For SaaS and AI companies, true portability means running in a customer’s own cloud account or on a different cloud provider entirely—a critical but difficult requirement to meet. Most engineering teams approach the issue already assuming it will be difficult to build, so they continually put it off. Unfortunately, many end up choosing to completely rearchitect their original product or to build an entirely new one with more flexible delivery options.

The typical approach relies on fragmented tooling: shell scripts for provisioning, hand-coded Ansible playbooks, environment-specific Terraform modules, and customer-specific delivery playbooks. Such workflows introduce significant operational fragility. They’re brittle, poorly abstracted, and almost always tightly coupled to specific infrastructure assumptions, such as regions, instance types, IAM models, and more. Software platforms built to rely on cloud-specific services, such as AWS Lambda, S3, Neptune, and DynamoDB, are particularly difficult to deploy to different environments.   

Manual intervention becomes the norm. Engineers get stuck SSH’ing into customer environments to debug broken installs, hardcoding configs, or modifying YAML files to meet inconsistent VPC layouts. Every manual activity introduces subtle drift that compounds over time and erodes maintainability.

Manual Processes Break Cloud-prem Delivery Models

Security becomes a constant concern. Credentials are often managed manually, secrets management is inconsistent across environments, and infrastructure is provisioned without effective guardrails. Infrastructure variability leads to weak isolation boundaries, non-compliant configurations, and even environments that may not be able to pass a basic security audit. It’s not that your software is insecure, it’s that the delivery model can’t enforce uniform security policies.

Versioning is another major failure point. Without a robust control plane to manage state, lifecycle, updates and upgrades, customers will run different versions of your stack on top of different infrastructure configurations, undoubtedly leading to some environments that may not be compatible with upstream changes. This creates deep ongoing challenges for support and maintenance. Pushing out hot updates or zero-day patches becomes a time-consuming, manual process requiring painstaking customer coordination and hands-on engineering effort. 

The cumulative effect is massive engineering overhead. Developers who should be focused on building innovative products are occupied maintaining delivery logic, troubleshooting customer installs, and reverse engineering infrastructure that’s drifted from the original spec. These cycles are expensive, they aren’t earning new customers and new revenue, and they don’t scale.

Traditional approaches to customer control of data don’t just slow you down. They create real security, compliance, and operational liabilities that cost you revenue and erode trust.

The New Way: Abstracting Application Delivery with Tensor9

Tensor9 introduces a new approach to operationalizing SaaS and AI platforms: a delivery abstraction layer designed to work across multiple environments. Designed to solve for security, ongoing management, observability, and scale, Tensor9 enables software delivery in a customer cloud or on-prem, preserving vendor management capabilities and customer usability.

We’ve written previously about the architectural philosophy behind Tensor9 and how we’re using it to unify and simplify SaaS and AI delivery across environments. Here, we’ll summarize how it works and why it revolutionizes application portability.         

A Universal Software Delivery Layer

Tensor9 orchestrates software delivery across heterogeneous environments using digital twins, miniature representations of your application stack and its operational context. This includes infrastructure assumptions, runtime requirements, networking dependencies, service dependencies, and security posture.

Tensor9 reads your IaC, translates high-level intent, and orchestrates consistent, automated delivery in any customer environment.

Declarative models in Tensor9 define the desired state of your app and its environment, including services, dependencies, policies, without tying it to a specific stack. Digital twins are runtime instances of these models, mapped to real deployments, tracking configuration and state drift.

Tensor9 doesn’t replace your existing IaC tools like Terraform. Instead, it works with them. It reads your IaC, translates high-level intent into deployable artifacts, and orchestrates consistent, automated delivery across customer environments. Tensor9 automates much of the repetitive configuration for storage, ingress, secrets, and other common services, helping teams achieve reliable, reproducible outcomes.

Deploy Anywhere and Win Everywhere

SaaS and AI vendors are embracing application delivery abstraction to move faster, close larger deals, and scale more efficiently. Tensor9 reduces time to market and the amount of engineering required per customer to unlock high-value opportunities in regulated and infrastructure-constrained markets.

Cloud-prem and on-prem aren't niche edge cases, they are the new enterprise baseline. Adopting a platform like Tensor9 signals to your customers that your SaaS or AI platform is designed as future-ready, built for flexibility, and architected for operational scale.

Take the next step towards making your SaaS future-ready. Don’t let delivery challenges hold you back from cloud-prem or on-prem software delivery. Explore how Tensor9 works in our customer playground, a free way to deploy a sample app in minutes.