Engineering Principles for Building a Successful Cloud-Prem Solution

Introduction and Motivation

Cloud-Prem solutions, often called Private Software as a service (SaaS) or Bring Your Own Cloud (BYOC), blend the advantages of cloud software with on-premises control. In a Cloud-Prem model, a vendor’s service is deployed into the customer’s own environment (their cloud account or data center) but remains vendor-managed.

The goal is to offer cloud-like deployment with on-premises security and control. BYOC specifically refers to deploying software in a customer’s own cloud (e.g., their AWS/Azure account) instead of the vendor’s, giving the customer full control over data location. In essence, Cloud-Prem bridges cloud and on-prem: the vendor manages the application, but sensitive data and processing stay within the customer’s infrastructure.

This approach is achieved by separating the service’s control plane (managed by the vendor) from the data plane (running in the customer’s environment). The vendor orchestrates updates and monitoring via the control plane, while all customer data and computation remain local for security.

Several forces are driving interest in Cloud-Prem deployments, especially among AI startups and enterprises in regulated industries, such as data sovereignty and compliance, security and trust, cost and data gravity, and industry trends.

Addressing data sovereignty and compliance issues, organizations face strict regulations (GDPR, HIPAA, financial data rules) that require data to remain under their control. Keeping data on their own cloud or on-premises can simplify compliance and avoid transferring sensitive information to vendor-managed clouds. Traditional SaaS often fails to meet stringent requirements beyond generic standards like Service Organization Control 2 (SOC 2). Cloud-Prem ensures data never leaves the customer’s domain, satisfying sovereignty mandates.

Security & Trust

With rising cloud security incidents and concerns about who can access critical data, companies want greater oversight with respect to their security posture. Cloud-Prem mitigates the “black box” issue of SaaS by letting customers see and control their environment. For example, AI applications often need privileged access to internal systems; running them in a customer’s network reduces exposure of credentials and sensitive outputs. Additionally, if a vendor has no direct access to the runtime environment, the risk of insider threats or cross-tenant breaches is lowered.

Cost & Data Gravity

Many AI and big-data workloads involve massive datasets that are expensive to move, carrying with it cost and data gravity considerations. In pure SaaS, customers may pay high data egress and storage fees to continually ship data into a vendor’s cloud. Cloud-Prem flips this model: it moves the compute to where the data already lives, cutting out data transfer costs and reducing latency. This is crucial for data-intensive industries, such as a large bank with petabytes on-prem can run analytics locally and avoid replicating it to an external cloud at great expense. One study noted enterprises saw up to ten times cost reduction by using a BYOC streaming platform (Redpanda) in their own storage environment, compared to a vendor-hosted alternative.

The industry is trending toward greater control over their data and infrastructure costs. AI startups are adopting BYOC models to win over enterprise customers who demand privacy and control. At the same time, sectors like finance, government, and healthcare – where compliance and scale converge – are leading the Cloud-Prem movement. For example, financial institutions with enormous data (JP Morgan’s ~450 petabytes is far beyond typical SaaS scales) have embraced Cloud-Prem for fraud detection and analytics because it meets their performance needs without compromising data governance. Overall, there is a growing recognition that a “one-size-fits-all” SaaS cloud does not work for every scenario. Cloud-Prem architectures have emerged now as a “middle ground” to capture the best of both worlds: the velocity of cloud innovation with the control of on-premises.

Cloud-Prem vs. SaaS vs. Hybrid

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To clarify the landscape, it is useful to compare Cloud-Prem with traditional SaaS and other hybrid models:

Let’s compare SaaS with BYOC and on-premises deployment scopes. In SaaS, the vendor owns and operates the infrastructure. In BYOC/Cloud-Prem, the software runs in customer-owned infrastructure (their cloud or data center) but is vendor-managed, combining aspects of both models. Traditional on-premises means the customer fully manages the software on their own infrastructure.

Software as a Service (SaaS):

In a pure SaaS model, the vendor hosts the application in its own cloud environment (often multi-tenant) and customers simply access it via the internet. Data and infrastructure are fully under the vendor’s ownership and control. Customers trade control for convenience: the vendor handles all maintenance, scaling, and security of the stack. This offers quick setup and minimal IT burden for the client. However, data residency and compliance are at the mercy of the vendor’s platform. Customers cannot dictate where data is stored or who has access beyond contractual promises. For organizations without strict regulatory concerns, SaaS’s ease-of-use and elastic scalability are very attractive. But highly regulated or privacy-sensitive firms often find pure SaaS insufficient for their needs.

Cloud-Prem (BYOC / Private SaaS)

Cloud-Prem (or BYOC/Private SaaS) deployments are single-tenant instances of the software running in each customer’s own cloud account or data center, but managed (fully or partly) by the vendor. This model is essentially a private SaaS: customers get a dedicated deployment, often with a vendor-run control plane overseeing it. The customer retains ownership of the virtual private cloud (VPC) infrastructure (e.g., the AWS VPC or on-prem servers) and thus full access to their data, satisfying data sovereignty requirements.

Meanwhile, the vendor can still perform operations like updates, monitoring, and troubleshooting within the bounds granted. Cloud-Prem is a middle ground on security and control. The customer’s sensitive data and systems stay within their trust boundary, yet they offload the heavy lifting of managing the software to the vendor. Compared to SaaS, Cloud-Prem provides far more data control and customization (the deployment can be tuned to the client’s environment).

The trade-off is higher complexity, in which setup is more involved and some shared-responsibility is required (the customer might need to provision cloud resources and grant the vendor limited access). Still, for many enterprises this trade-off is worth the peace of mind of having their “own” instance. Cloud-Prem is especially popular for companies that already have significant cloud infrastructure and expertise; they can leverage existing agreements and cloud resources to potentially save on costs while letting the vendor manage the app.

Hybrid Approaches

Hybrid can refer to two contexts: (a) a hybrid cloud deployment where parts of a solution run in different environments, or (b) a hybrid offering strategy where a vendor supports both SaaS and on-prem options. In architecture terms, many Cloud-Prem solutions are inherently hybrid (a vendor-hosted control plane plus on-premises data plane is a hybrid SaaS architecture, for example). Some vendors take this further by keeping certain services multi-tenant while deploying only specific components into the customer’s network. For instance, a vendor might run a centralized management console in the cloud, but deliver an agent or appliance that resides on the customer site handling the data (common in analytics and security products). This approach can give partial relief on data control without the overhead of a full BYOC deployment. Another example is Snowflake’s “private link” model: the service stays in Snowflake’s cloud, but customers connect via private, secure network links as if it were within their own network. This hybrid tactic is used to ease data governance concerns.

From a comparative standpoint, the models differ on key dimensions such as a data control and privacy, infrastructure ownership, scalability and updates, support and operations, and hybrid organizational strategy.

Data Control & Privacy

SaaS offers the least customer control (data is in vendor’s realm), Hybrid/ Cloud-Prem offers strong control (data remains in customer’s cloud or on-prem), and traditional fully on-prem (self-managed) offers absolute control. BYOC and hybrid models are attractive for enforcing data residency (e.g., keeping databases in a customer’s region or VPC to meet local laws, whereas this might not be guaranteed by SaaS).

Infrastructure Ownership

In SaaS, infrastructure is vendor-owned and operated. In Cloud-Prem, infrastructure is customer-owned (cloud or on-site), but the vendor typically has some access to manage their software. Hybrid setups split components across both approaches. Owning the infrastructure means customers can utilize existing cloud commitments and configurations, but it also means they must accommodate the software’s resource needs in their environment.

Scalability & Updates

SaaS systems scale transparently; the vendor adds capacity behind the scenes and pushes updates continuously. BYOC deployments require more planning for scaling (each customer’s instance must be sized and adjusted, often using automation scripts or Kubernetes for elasticity). Updates in Cloud-Prem are often coordinated with the customer (or done via a central control plane in off-hours), rather than instant propagation. A hybrid control-plane approach can allow the vendor to still roll out updates to customer instances faster than traditional on-prem, but not quite at the seamless pace of true multi-tenant SaaS.

Support & Operations

Vendors have full observability and control in SaaS, enabling proactive support (they can see logs, metrics, etc. easily). In Cloud-Prem, by default the vendor’s visibility is limited. Troubleshooting may require the customer to share logs or allow remote sessions unless telemetry tools are built-in (more on this challenge later). Hybrid models can be designed to phone-home health data. On the customer side, SaaS requires almost no IT ops effort, whereas BYOC means the customer’s ops team is involved (provisioning cloud resources, ensuring network connectivity, etc.) along with the vendor. Customization is also different. SaaS tends to be one-size-fits-all (with some configurable settings), while a BYOC deployment might be more adaptable (e.g., the customer could integrate it with internal systems or use a custom security configuration). This is why large enterprises often prefer BYOC; it can be tailored to fit their environment and policies, whereas SaaS imposes more uniform constraints.

Hybrid Organizational Strategy

Some software companies adopt a hybrid offering, maintaining both cloud and on-premises product lines. This can capture a wider market but comes at the cost of engineering and support complexity. Atlassian’s journey is a prime example: they offered server (on-prem) and cloud editions of Jira/Confluence for years. Recently Atlassian tried to go “cloud-first” and phase out on-prem, but enterprise customers resisted moving entirely to cloud. Atlassian had to acknowledge that many large clients will remain hybrid (using on-prem data center editions for some needs while gradually adopting cloud for others). The lesson is that vendors may need to support a mix of models during a long transition, or indefinitely, in order to satisfy all customer segments.

Cloud-Prem/BYOC sits between the extremes of SaaS and on-prem. It offers a compromise between control and convenience. Each model has its merits. SaaS maximizes simplicity, scalability and vendor efficiency. On-prem maximizes security and autonomy. Cloud-Prem seeks to marry the two by using modern cloud-native tech on the customer’s turf. Next, we’ll explore how to architect such Cloud-Prem solutions effectively.

Key Architecture Patterns for Cloud-Prem Solutions

Designing a Cloud-Prem product requires robust architecture patterns that ensure deployments are portable, reliable, and manageable across many customer environments. Key principles include a modular microservices design, Kubernetes-native deployments, infrastructure-as-code (IaC) and automation, control plane/data plane separation, keeping a lean footprint, safe defaults, strategies for graceful degradation and simplified package and delivery.

Microservice and Modular Design

Cloud-Prem solutions benefit from a microservices architecture, where the application is split into loosely coupled services. This modularity allows easier updates and customization. Individual components can be upgraded or configured per client without impacting the whole system. Microservices also let you scale parts of the system independently (e.g., if one customer uses the analytics module heavily, you can scale that component alone on their cluster). Moreover, smaller services are easier to containerize and redeploy in diverse environments.

A monolith could work for simple cases, but in practice most vendors find that modularizing the system (with clear API contracts between services) greatly improves flexibility for on-prem installs. Stateless services are especially valuable. By keeping as much state as possible in external data stores (or in the customer’s managed DB), the application services can be restarted or scaled freely, which is important when dealing with unreliable on-prem resources or performing upgrades. If stateful components are needed (e.g., a database or message queue), consider abstracting them behind an interface so they can use customer-provided infrastructure when available. For example, a BYOC app might allow plugging into the customer’s existing database service or object storage, to reduce duplication and resource footprint.

Kubernetes-Native Deployment

Kubernetes has become the de-facto standard for packaging and deploying Cloud-Prem software. By embracing Kubernetes manifests (Helm charts, Operators, etc.), vendors can achieve a consistent deployment method across many environments. Kubernetes provides an abstraction layer over infrastructure differences. Vendors often ship a Helm chart or k8s yaml bundle that defines all the necessary pods, services, ingress, and so on. This not only standardizes installation, but also leverages Kubernetes features like scheduling, self-healing, and resource quotas for safer multi-component operation. Advanced teams build a Kubernetes Operator, which is essentially an application-specific controller that runs in the cluster to automate tasks like backups, scaling, and upgrades of the app. Operators encode operational knowledge and make the deployment more “hands-off” for customers.

For instance, Couchbase’s Autonomous Operator manages a Couchbase cluster on OpenShift, handling failover and scaling, which is crucial for their self-managed Capella offering. The downside is complexity – writing an operator is non-trivial – but it can significantly reduce manual effort needed in each customer environment.

Infrastructure as Code (IaC) and Automation

Treat each customer deployment as reproducible infrastructure defined in code. Terraform, Pulumi, or Crossplane can be used to script the provisioning of cloud resources (networks, VM instances, Kubernetes clusters) that the product needs. This ensures consistency and enables automation. For example, a vendor might provide Terraform modules that customers run to set up the required AWS resources and IAM roles for the BYOC deployment, reducing setup errors. Infrastructure-as-code (IaC) also helps with compliance. It’s easier to audit and review a declarative config than a manual setup.

Embracing GitOps (using tools like Argo CD or Flux) is another powerful pattern. The desired state of the application (Kubernetes manifests, config maps, etc.) can be kept in a Git repo that acts as the source of truth. The cluster (via Argo/Flux) then continuously applies the changes from Git. This way, upgrades or config changes are as simple as committing new files to the repo, and they can be rolled back if needed. GitOps provides auditable change management – a big plus for enterprise customers – and can let vendors push updates in a controlled, customer-visible way (the customer can see the changes in their Git log).

Control Plane / Data Plane Separation:

As introduced earlier, a hallmark of Cloud-Prem architecture is splitting the system into a vendor-managed control plane and a customer-resident data plane. Concretely, this might mean the vendor runs a central web service (often hosted in the vendor’s cloud) that handles multi-tenant functions, such as a management console, licensing, coordination of updates, and global monitoring. The heavy data-processing components (databases, processing engines, etc.) run in the customer’s environment. The control plane and data plane communicate securely, often with the data plane initiating connections out to the control plane (to avoid needing inbound access through customer firewalls).

This pattern has multiple benefits. The vendor can orchestrate fleets of customer deployments from the control plane (issuing upgrade commands, collecting telemetry) while customer data stays local. Issues with one customer’s data plane don’t impact others (due to strong isolation), yet the vendor can still deliver a unified SaaS-like experience through the control plane UI. The control plane can host multi-tenant services that don’t handle sensitive data (like a metrics aggregator or a configuration database with only metadata), saving effort by not duplicating those for every customer.

This separation also aligns with security best practices. Minimal data is ever in the control plane (ideally just anonymized metrics or configs), and all heavy computing and storage happen in the customer’s zone. Many modern “hybrid SaaS” products (Tecton’s feature store, StarTree’s real-time analytics, etc.) use this model. The SaaS control plane offers user convenience and centralized management, while the data plane (often containerized software) is deployed into the customer’s cloud account.

Lean Footprint and Safe Defaults

When running in a customer’s environment, your software should strive to be a “good citizen” in terms of resource usage and operations. Remember, the infrastructure costs can become bloated leading to a negative ROI on the CloudPrem solution for the customer! Optimize for a minimal footprint..

In other words, allow deploying with small instance sizes for dev/testing, and let components scale only if needed. Idle resource consumption should be low so that customers aren’t surprised by big bills or hardware needs for a pilot installation. This strategy involves providing toggles to turn off unused features or using modular architecture so that optional components (like an add-on service) can be omitted entirely if a customer doesn’t need them. Operational safety means designing for failure isolation and predictable behavior. With this in mind, rate-limit any external calls (so you don’t DDoS an internal API), implement circuit breakers if your service depends on customer-provided endpoints, and prefer stateless retries for resilience.

Graceful Degradation

The system should handle outages gracefully. If the vendor control plane goes down or loses connectivity, the customer’s data plane should continue operating in a degraded but functional mode (perhaps without new updates), rather than crashing. Logging and debugging tools should be built-in (accessible to the customer’s ops team) so that even if vendor engineers can’t immediately log in or using SSH or remote desktop control, the customer can self-service initial troubleshooting. In short, design for autonomy: each instance should be able to run with minimal hand-holding once deployed.

Packaging and Delivery

Delivering Cloud-Prem software often means packaging it as container images and helm charts, plus any auxiliary scripts. Use standard container registries and package managers to distribute updates. Many vendors choose to host a private registry or use a system like Replicated that can package the app for air-gapped installs as well. Using container images with all baked-in dependencies (and OS base images that are secured) avoids requiring internet access at runtime for pulling components. When customers are in air-gapped networks, support them by providing offline installation bundles, such as a downloadable archive containing all images and charts, along with checksums.

Helm charts are a common way to encapsulate all configurable parameters for an install; by adjusting values files, the deployment can target different customer preferences (number of nodes, enabling/disabling certain modules, etc.). Another emerging practice is to use Docker or OCI image bundles for entire application sets (some tools allow an “application image” that includes Kubernetes manifests and images together). Regardless of method, version everything and make it repeatable. The same artifact tested in CI should be what’s delivered to customers.

Cloud-Prem architectures lean heavily on cloud-native principles: containerization, Kubernetes orchestration, infrastructure-as-code, and control-plane/data-plane splits. These patterns ensure that while each customer’s deployment is isolated, they can all be managed in a scalable way.

Next, we’ll discuss the real-world challenges that arise when implementing and operating such solutions.

Key Challenges

Building a successful Cloud-Prem solution is not without significant challenges. By essentially running a mini version of your service in each customer’s environment, you encounter many of the hardest problems of distributed systems and enterprise software. Following are some key challenges and ways to think about them.

Deployment Complexity & Environment Heterogeneity

Each customer environment is unique with different cloud providers, regions, networking setups, security policies, Kubernetes versions, etc. One customer might run on AWS with a flat network and internet access, another on Azure with strict VNet rules, and yet another on a fully air-gapped on-premises Kubernetes cluster. Depending on your potential customer base, your deployment process must be resilient enough to support each of these variations. Such flexibility mandates extensive testing on multiple platforms (AWS, GCP, Azure at minimum, possibly OpenShift, and vanilla K8s versions) to ensure your helm charts or scripts work everywhere.

Even basics like storage classes or load balancer definitions can vary between environments. Automation can help (for example, having your control plane detect the cloud and adjust configs), but you’ll likely need a flexible installer that can be tailored. Another facet to consider is dependency management. Unlike a controlled SaaS environment, on-prem installs may have to integrate with customer-managed dependencies (databases, identity providers, etc.). Handling myriad integration scenarios adds complexity. Many vendors mitigate this issue by shipping as much as possible within the deployment (e.g., using an embedded database or bundling necessary services), but that increases resource use.

Striking the right balance is tricky. Ultimately, expect a longer deployment cycle for Cloud-Prem: instead of clicking “deploy” in your own cloud, you’re preparing a package that others will deploy – often involving back-and-forth with their ops team to get it right. Investing in good documentation, pre-flight checks (scripts to validate the environment meets prerequisites), and perhaps a “trial run” sandbox (like a Docker Compose or minikube version) can reduce pain here. Managed offerings like DuploCloud could also help ease this to some extent.

Observability and Monitoring Blind Spots

In a SaaS model, your DevOps team has direct access to monitoring: logs, metrics, traces, you name it. In Cloud-Prem, you by default lose access to that observability. The application is running behind someone else’s firewall, possibly on machines you can’t log into. This lack of access creates a huge challenge in supporting the product. How do you know if it’s healthy? How do you debug performance issues or errors?

To tackle this issue, Cloud-Prem solutions often include built-in observability components that operate within the customer’s environment and then securely share insights with the vendor. For example, you might deploy a metrics collector (like Prometheus or Grafana agents) in each install that captures performance data. This data can be exposed to the customer (so they can monitor their own instance) and selectively sent to the vendor’s control plane or support team – with the customer’s permission.

The key here is not to send any sensitive data; focus instead on metadata and health indicators. Some vendors provide a monitoring dashboard as part of the product that both the customer and vendor can view (e.g., through a secure web portal or by the customer granting temporary access). In practice, many BYOC vendors implement a “phone home” telemetry pipeline: the data plane pushes periodic status info to the control plane. This can include version numbers, resource usage, heartbeat checks, etc. Additionally, when deeper diagnostics are needed, customers may need to generate support bundles – archives of logs and configs – that they can share.

Observability in BYOC is an evolving area; new tools like Insightful and DuploCloud are emerging to help regain visibility without violating privacy by providing SOC2 compliance or using federated monitoring systems. It’s wise to make monitoring a first-class part of your architecture, not an afterthought, so that you’re not flying blind in production. Lack of insight could lead to poor SLA compliance and frustrated customers when issues can’t be pinpointed quickly.

Air-Gapped and Offline Environments

Some enterprise customers (government, defense, critical infrastructure) operate in air-gapped networks, completely isolated from the internet. Supporting these deployments is a challenge on multiple levels. First, delivery of the software must be offline (via encrypted USB drives or file transfers through a secure bridge). Your deployment pipeline needs to accommodate delivering updates as downloadable packages that contain everything needed (no pulling from public registries). Licenses can’t be verified online, so offline license files or dongles might be needed. Second, once deployed, the system can’t reach your control plane or cloud for any managed services. Any feature that normally relies on calling a cloud API must have an on-prem alternative. For example, if your SaaS would normally use a cloud email service to send notifications, the on-prem version might need to integrate with the customer’s SMTP server instead.

Observability in air-gapped mode is also hard: you can’t automatically get metrics out, so you rely on the customer to periodically export logs or allow on-site visits. Designing for air-gap may entail running a local update server or local control plane at the customer site. Some companies deliver a physical appliance or a pre-loaded VM image that can be installed with minimal external dependencies. Testing an offline install path is essential. Nothing is worse than an installer that tries to fetch dependencies from the internet and fails in a locked-down data center.

Security updates are a particular concern: in an isolated environment, the software might not get patches promptly. Vendors should work with the customer’s security team to regularly provide vetted patch bundles. Air-gapped deployments often have long lifecycles (because updating is hard), so plan to support older versions for longer if you sell into these contexts.

Shared Responsibility & Security Model

Cloud-Prem introduces a complicated shared security responsibility between vendor and customer. In SaaS, the vendor secures everything in their cloud. In on-prem, the customer secures everything. In BYOC, responsibilities blur: the customer’s cloud provides network isolation and base infrastructure security, but the vendor’s application has privileged access within that environment. Clear delineation and best practices are critical. For example, a typical BYOC setup might require the customer to create a dedicated VPC or namespace for the application and grant the vendor’s team or control-plane limited access (often via an IAM role or VPN into that environment).

Vendors should enforce least-privilege access – only request the minimal roles needed (e.g., the ability to deploy containers and read CloudWatch metrics, but not carte blanche access to all cloud resources). Many companies implement just-in-time access – the vendor has no standing access to the runtime environment unless the customer explicitly opens a support session or the control plane generates a short-lived credential when performing an automated update. This helps address customer concerns that vendor ops could snoop on data at will. All access should be auditable – use the cloud’s auditing tools or your own logs to record actions taken by the vendor.

Another aspect is data security. If the vendor does have any level of access to systems in the customer environment, you must ensure strong security practices on your side (background checks for engineers, training, 2FA on any remote access, etc.), because a breach on the vendor side could potentially be a breach of many customer environments. Some organizations mitigate this by requiring the software to support customer-supplied encryption keys, so even if the vendor can access the database, the data is encrypted with a key only the customer knows.

In short, trust but verify. Design the system such that the customer can, if they choose, cut off the vendor and still run (maybe in a degraded mode), and give them full control over their data (export, backup, and delete at will). Establishing a clear shared responsibility matrix (similar to cloud providers’ models) is a good practice. Document which security tasks are the vendor’s responsibility (e.g., app vulnerability management, images free of CVEs, etc.) and which are the customer’s responsibility (e.g., securing network perimeter, OS patching if VMs are customer-managed). A well-thought-out security model and transparency will go a long way toward gaining customer trust.

Debugging and Support Hurdles

When something goes wrong in a Cloud-Prem deployment, troubleshooting can be painfully slow compared to SaaS. In SaaS, your engineers can often quickly replicate the issue in a staging environment, or live-debug on the problematic instance (since it’s under your control). In BYOC, you might find yourself on a Zoom call with a customer’s admin asking them to run commands for you if handled poorly.

This challenge means you need to plan for supportability. Build diagnostic modes into the app (for instance, a special URL or CLI command that gathers a diagnostics report). Encourage customers to deploy in a way that you can access logs, such as providing an option to forward logs to a vendor-run logging system or at least to an S3 bucket to which you can be granted access. Some vendors ship a small “support agent” that can open a secure tunnel to let the vendor in for emergency debugging with one click from the customer. If you do that, ensure it’s truly optional and auditable. Customers will want to know when you’re in their system.

Another approach for supportability is to maintain a reference environment on your side that mimics each major customer setup, so you can attempt to reproduce issues independently. But given unique data and workloads, that’s not always possible. Expect that debugging will involve more communication overhead. Your team will file more back-and-forth with the customer’s ops, and issues may take longer to pinpoint. This has a cost implication too (support staffing, time).

To mitigate some of these issues, invest in robust testing and QA before releases. Catching bugs in the lab is far better than trying to fix them in a remote customer environment to which you have limited or no access. Also consider a canary or phased rollout strategy If you have a control plane, you could roll updates to a few friendly customers first, see if anything breaks, then proceed to others, rather than hitting everyone at once with a bad update that’s hard to troubleshoot remotely.

Upgrade Management and Version Sprawl

One of the hardest operational challenges is managing many deployments across various versions. In a pure SaaS, you typically run one version (the latest) for all users. In Cloud-Prem, some customers might upgrade immediately, others might skip versions or delay updates for months due to their internal policies. Over time you may have to support multiple active versions of your software in the field. Your engineering and support teams will need to maintain backwards compatibility and knowledge of older releases. It’s wise to implement a strict version support policy (for example: support N and N-1 versions, older ones require special extended support contracts) to avoid an explosion of supported versions.

Automating upgrades as much as possible is also key. If using a control plane, it can coordinate rolling out new container images to customer clusters (possibly at scheduled times customers set). However, you must balance automation with caution. Enterprise customers often want to validate new releases in a staging environment first before letting it upgrade production. Providing a “no upgrade without approval” option is important, even if your system can auto-update, many will turn that off in favor of a manual process.

Another technique is blue-green or canary deployments for on-prem. Deliver a new version alongside the old and allow the customer to switch when ready, or gradually migrate data. This is easier if stateless, but for databases or stateful components it may involve complex data migrations. Testing upgrade procedures in all supported topologies is a must in order to avoid failed upgrades that require manual intervention (a nightmare scenario if it bricks an on-prem system at 3 a.m.). Finally, consider how to deliver security patches quickly. You might need an out-of-band patch mechanism for critical fixes that doesn’t wait for full version releases.

In short, running software “in the wild” of customer environments introduces challenges in deployment variability, monitoring, security responsibilities, and lifecycle management. Awareness of these challenges is the first step. Next I’ll discuss trade-offs and choices in tooling and strategy that can alleviate some of these pains.

Trade-offs and Choices

When engineering a Cloud-Prem offering, teams will face important design choices and trade-offs. Following are key decisions and how they impact the solution.

GitOps vs. Traditional Deployment

One decision to consider is how updates and configuration changes will be delivered. A GitOps approach (using tools like Argo CD or Flux) treats the desired state of the deployment as code in a repository. This approach can be powerful for Cloud-Prem. The vendor can supply update manifests via a Git branch and the customer’s cluster will pull and apply them. Using this approach provides a clear audit trail and reverts are as simple as reverting a commit.

The trade-off is complexity, because not all customers are comfortable setting up GitOps for a third-party application. Some may prefer a more traditional approach (download new installer, run it). GitOps shines in environments where customers demand control and visibility of changes (common in fintech and government). It also enables a sort of “self-serve SaaS” model, in which the vendor publishes new versions to a repo, but the customer decides when to sync. On the other hand, if a customer’s internal process is very manual or they lack GitOps know-how, forcing it could be a barrier. A middle ground is to offer both: a GitOps-based continuous delivery for those who want automation, and a manual step-by-step upgrade path for others.

Kubernetes Operators vs. Simpler Scripts

As mentioned earlier, using a Kubernetes Operator can automate much of the application lifecycle (setup, recovery, upgrades). Writing an operator (or using an operator framework) is an investment that can be like writing a mini-product alongside your product. The benefit is scalability of management. With a good operator, one engineer can oversee dozens of customer deployments because the operator handles routine issues automatically, such as, restarting pods, resizing clusters, and backing up data. Operators can also integrate with GitOps pipelines for a full continuous integration and continuous delivery (CI/CD) solution in customer environments.

The alternative is to use simpler Helm charts or scripts and rely on manual or semi-automated procedures for maintenance. Early in a product’s life, a full operator might be overkill, so some startups start with Helm plus documentation, and later evolve to an operator as they gain more customers. The trade-off is between automation and human effort. More automation (via operators) reduces ongoing toil but has higher upfront development cost and complexity. Automation also introduces another component that could have bugs. A pragmatic approach is often to start as simple as possible (to get initial deployments working and gather knowledge) and then to gradually automate the most painful manual tasks via an operator or additional scripts.

Secure Telemetry vs. Privacy

For observability and license compliance, vendors often want telemetry from on-prem installs. Choices range from completely opting-in to anonymous metrics to more invasive monitoring. There’s a clear trade-off. More telemetry can dramatically improve support and proactive issue detection (and also ensure customers aren’t violating terms), but it can erode trust if not handled carefully. The best practice is to be transparent and secure with any data collected by explicitly documenting what is sent, ensuring it’s encrypted in transit, and allowing customers to turn it off if they desire. Many enterprise customers will scrutinize outbound traffic from the system. A design pattern that works is to have a small, auditable list of metrics (e.g., system up/down status, version, CPU load, and a hash of license key).

These metrics are sent to the control plane. Everything else (detailed logs, etc.) stays on-prem unless the user triggers a support upload. This approach satisfies most security teams and still gives the vendor essential information. The vendor’s control plane should also expose this telemetry back to the customer (a dashboard or health page), so it’s a two-way street and not “phone-home spyware”. In regulated industries, sometimes no telemetry is allowed. In such cases, the vendor must operate blindly or require periodic manual reports. It is a difficult trade-off, choosing between visibility and privacy. The right balance will depend on your customer’s tolerance.

Offline Updates and Patching

Supporting completely offline environments is a decision point, because it greatly increases engineering and process overhead (as discussed in challenges). If your target market includes defense, healthcare, or critical infrastructure, you may have no choice. But if not, you might decide to not officially support air-gap installs in order to simplify operations. Some companies explicitly list internet connectivity as a requirement for their on-prem offering, funneling truly air-gapped prospects toward an older-school licensed software version or no support at all.

If you do support offline, the choice of update mechanism is crucial. Will you supply periodic ISO files/tarballs that contain everything? How will customers apply them: via a CLI tool or by manually replacing containers? There’s a trade-off between frequency of updates and size. Frequent small updates are easy when online, but for offline, customers often prefer larger but infrequent update packages (since each is a hassle to apply). You might end up maintaining two paths, a continuously updated stream for connected deployments and a quarterly offline bundle for isolated ones. Each path adds overhead (testing, packaging), so ensure your team has the capacity for the promises you make.

Multi-Version Support and Compatibility

As mentioned earlier, dealing with multiple active versions is tricky. One strategic choice is whether to enforce all customers to update in lockstep (or within a tight window). Some vendors of on-prem software use license terms or support contracts to require that customers stay within one or two versions of the latest. This can work if you have leverage or if updates are relatively easy to apply. It reduces the burden on engineering (fewer versions to bugfix). However, enforcing upgrades too aggressively might irritate customers who prefer stability, especially if upgrades have historically caused issues.

Another approach is offering long-term support (LTS) versions. Designate one release per year as LTS that will receive backported fixes for, say, two years, whereas other interim releases might only get three to six months of support. This lets conservative customers stick to a stable branch and bleeding-edge customers take the latest. You can then channel your support efforts accordingly. Compatibility is another choice. Will your newer control plane always be backward-compatible with older data planes? If a customer doesn’t upgrade their environment, can your SaaS control still manage it? Maintaining backward compatibility in APIs adds complexity, but it can also allow more flexibility in upgrade timing. Some designs simply refuse to manage out-of-date instances. The control plane UI, for example, might flag an instance as unsupported if too old, forcing the customer to upgrade to regain full functionality. Decide early how “strict” you want to be, and communicate that policy clearly.

Degree of Vendor Hands-On Support

Cloud-Prem often starts with very hands-on support with engineers manually assisting each deployment. Over time, ideally, it follows a more automated, self-service model. There is a strategic decision on how much to lean on services vs. products for ongoing operations. In the highly automated approach, customers invest in systems (control plane, tooling) so that one ops engineer at the vendor can handle, say, fifty customer deployments through central dashboards. This approach looks more like a true SaaS on the ops side, but requires building those internal tools (like Palantir did with Apollo). In the hands-on approach, each customer might require individual attention, and staff a “customer reliability engineering” team to actively manage and support each deployment. This approach is more feasible if you only ever expect to support a few dozen big customers (and indeed, some B2B companies operate this way profitably, essentially as a managed service provider).

The trade-off comes down to scalability vs. immediacy. Early-stage startups might opt for manual support due to limited developer resources, essentially treating initial deployments as projects. This can ensure success and gather feedback. But it doesn’t scale. As you get more customers, that approach will strain engineering unless you pivot to more productized management. Often the path is phased, with heavy hand-holding for first N customers, while building automation, so that by customer N+1, systems are in place for easier onboarding, etc. It is important to capture the knowledge from each manual deployment and feed it back into improving the product or runbooks.

Pricing and Profitability

Cloud-Prem has profound implications on your business model. Unlike SaaS where the vendor incurs the cloud infrastructure costs and rolls those costs into subscription pricing, in BYOC, the customer pays the cloud bills directly. On one hand, this structure relieves the vendor of variable cost – the vendor isn’t paying for the customer’s compute/storage – and customers can even use their negotiated cloud discounts. On the other hand, this structure changes how you must price your product. Typically, BYOC pricing is based on software subscription only, not infrastructure use. This structure can make the sticker price look lower than SaaS, but the customer will need to add their own AWS/Azure costs to calculate the true cost. Vendors must articulate pricing differences clearly to prevent customers being surprised that running the BYOC version still racks up significant cloud charges on their side.

Another factor to consider is that in SaaS, vendors benefit from multi-tenancy and economies of scale (pooling resources among customers). In BYOC, however, each deployment is single-tenant and often sized with spare capacity for safety. Multi-tenant architectures are inherently more cost-efficient at scale, which means that if your software could run as one big service, it would likely use fewer total resources than fifty separate small instances. Therefore, a BYOC vendor must often charge a higher margin on software to make up for the lost efficiency (since the customer is bearing it).

However, competitive pressure might limit how much you can charge. Per-node or per-instance pricing is common in BYOC (e.g., charge per cluster node under management), but that can incentivize the customer to under-provision to save money, potentially hurting performance. Some customers choose per-user or flat annual fees instead. You’ll also need to account for the extra support and engineering cost of maintaining on-prem deployments. Enterprise support contracts or higher list prices are the norm. Profitability can be lower per customer, especially in early stages when tooling isn’t fully developed and support is high-touch. The flip side is that BYOC often unlocks larger deals (enterprise customers who wouldn’t use SaaS might pay big for a BYOC solution). Many successful companies have closed multi-million dollar contracts for on-prem software where SaaS could only have netted small monthly subscriptions.

So, the trade-off can be slower, more expensive sales cycles, but with higher annual contract value (ACV). It’s critical to model these financial aspects, ensuring that the price covers not just the software features, but also the operational overhead of supporting it in varied customer environments. Some startups have struggled by offering BYOC too cheaply and then finding it unprofitable to sustain. Generally, BYOC/Cloud-Prem skews towards an enterprise sales model (larger deals, account managers, and longer sales cycle) as compared to the volume model of SaaS. This must align with your company’s strategy, which is not just an engineering decision, but also a business decision.

To summarize, Cloud-Prem solutions require navigating trade-offs between automation and manual effort, visibility and privacy, rigidity and flexibility in updates, and how to capture value given a different cost structure. There is no one-size-fits-all answer; successful teams make conscious choices that align with their customer needs and company capabilities. Next, we’ll look at some case studies to see how these principles and challenges manifest in real organizations.

Case Studies

To ground these concepts, let’s examine a few real-world case studies – both success stories and cautionary tales – of companies dealing with Cloud-Prem/BYOC deployments. We’ll see what worked, what didn’t, and lessons learned.

Redpanda – Streaming Data Platform BYOC Success

Redpanda Data (a Kafka-compatible streaming platform) embraced a BYOC model early on. They offer Redpanda BYOC, a fully managed streaming service that deploys into the customer’s own cloud account. This approach resonated strongly with financial and tech companies needing low-latency data streaming without handing over their data infrastructure. Redpanda’s architecture separated control and data planes.

The Redpanda service runs on object storage in the customer’s VPC, while Redpanda’s team manages it through a control plane. The outcome was striking, because their customers saw up to ten times cost reduction compared to using Kafka via a SaaS, largely from eliminating data egress and optimizing resource use in their environment. Redpanda’s success forced the incumbent (Confluent) to react. Confluent lacked a BYOC option and found some big clients preferring Redpanda. This culminated in Confluent acquiring a startup called Warpstream to bolster their BYOC capabilities.

• Key lessons: Redpanda showed that if your architecture truly leverages customer infrastructure efficiently (in their case, using cloud object storage as the log store), BYOC can beat multi-tenant SaaS on performance/cost for large workloads. They also validated the importance of data-plane/control-plane split for streaming systems. Another lesson is market timing. They tapped into a demand (data sovereignty in streaming) that the market leader wasn’t addressing, giving them an edge.

Couchbase Capella – Hybrid Cloud Database

Couchbase, a NoSQL database vendor historically known for on-prem enterprise software, launched Capella, their fully managed DBaaS, to compete in the cloud era. Capella runs Couchbase clusters for customers on AWS, Azure, and GCP. Interestingly, Couchbase recognized that many customers would want integration with their existing environments, so they built Capella with flexible deployment modes. For example, Capella supports VPC peering and PrivateLink to connect with a customer’s cloud network, and the Couchbase Autonomous Operator allows customers to run Couchbase on Kubernetes in their own data centers or clouds.

Capella essentially offers a spectrum of services from multi-tenant (Couchbase-hosted) to customer-managed clusters with Couchbase cloud orchestration. The result has been positive. Couchbase managed to bring its legacy customers along to cloud by offering them a comfortable middle ground (they can migrate data gradually, use hybrid cloud replication between Capella and self-hosted clusters). Capella is seen as a success in enabling an “opt-in Cloud-Prem”. Customers who require it can run the database in their own environment (like on Red Hat OpenShift with the Operator), while others can let Couchbase host it.

• Key Lessons: Even a traditionally on-prem vendor can pivot to a cloud-served model if they make it hybrid-friendly. A key takeaway is the need for robust automation (the Operator in this case) to manage deployments across environments. Also, by supporting hybrid replication between on-prem and Capella, Couchbase acknowledged that many enterprises will run in a hybrid cloud mode for a long time, and turned that into a feature rather than a problem.

DeltaStream – Private SaaS from Day 1

DeltaStream is a startup offering a serverless stream processing platform (real-time analytics on streaming data, leveraging Apache Flink). From the outset, DeltaStream offered both a multi-tenant cloud service and a Private SaaS (BYOC) deployment for customers’ own clouds. It is notable because many startups hold off on on-prem options, but DeltaStream used it as a selling point to target enterprises hesitant to send streaming data off-prem. Customers choosing their BYOC option can deploy DeltaStream’s processing engine in their VPC and still have it fully managed by DeltaStream. This model likely helped them in industries like fintech or telecom that have data locality concerns. The company recently raised a Series A, citing “broad adoption of streaming data” and the need for tools that can run securely in any environment.

• Key Lessons: DeltaStream’s approach highlights that starting with a BYOC mindset can be a differentiator for new companies, not just an add-on. However, it also means a heavier engineering lift early on. Building a serverless platform that can deploy in arbitrary cloud accounts is non-trivial. Their success remains to be fully realized because they are still early-stage, but their approach underscores an emerging trend. New data platform startups (especially post-2020) often design for BYOC from day one, reflecting the market’s expectation of deployment flexibility.

Palantir – On-Prem to SaaS-Hybrid Evolution

Palantir is an exemplar of successfully managing on-prem software at scale (albeit with a very high-touch model). Palantir’s platforms (Foundry, Gotham) are used by governments and large enterprises that often demand on-prem or dedicated cloud deployments for security. In its early years, Palantir proactively sent engineers on-site to deploy and customize each instance (famously, “forward-deployed engineers” instead of salespeople). It made operation services-heavy and not very scalable, but it allowed them to deeply understand customer needs and refine the product.

Over time, Palantir productized much of the deployment process. They built an internal platform called Apollo to automate software deployment and updates across on-prem, cloud, and hybrid environments. Their deployment automation enabled them to roll out features and fixes to customer installations much faster than traditional manual processes. Apollo can reportedly update Palantir software across dozens of sites with minimal human intervention, a process that would otherwise be extremely slow. Palantir’s investments paid off. They managed to increase their gross margins up to SaaS-like levels (approximately eighty-one percent), while supporting on-prem, by standardizing deployments and focusing only on large deals that justified the effort. They now advertise that Foundry can be deployed “in the cloud, on-prem, or hybrid” seamlessly, and big clients like Airbus and Morgan Stanley run it in various modes.

• Key Lessons: Palantir shows that supporting on-prem at scale is possible with enough engineering investment (automation, tooling) and by being selective with customers (they go after huge contracts so the economics work out). One lesson learned is the importance of building a robust internal delivery platform. Their Apollo product is essentially a precursor to many of the commercial tools now available for cloud-prem software management. Another lesson is that a services model can transition to a product model. Early on, it required a great deal of custom work, but Palantir leveraged that work to create a more repeatable solution, gradually reducing the per-customer effort. Finally, Palantir’s story highlights the reality that enterprises will pay a premium for software that meets their deployment constraints. It is not necessary to chase a low-cost SaaS model if your value supports a higher-touch approach.

Atlassian – Phasing Out On-Prem and Customer Backlash

Not all cases are pure success. Atlassian’s experience is a cautionary tale about how you transition from hybrid to cloud. Atlassian long offered on-premises versions of their tools (Jira Server, Confluence Server) alongside a growing cloud offering. In 2020, Atlassian announced end of life for server editions (with Feb 2024 as end of support) to push customers to cloud or their intermediate data center (self-managed cluster) edition.

While strategically understandable (cloud provides recurring revenue and easier support), many customers, especially large enterprises and government agencies, were unhappy. They either didn’t want to move to cloud for data reasons or found that Atlassian Cloud was not as feature-rich for complex use cases. Atlassian later softened its stance and shifted messaging to “enterprise-first”, acknowledging that many big customers will remain hybrid (and would therefore use data center on-prem for a long time). Atlassian indicated a commitment to support those customers’ needs and not force cloud migrations on a short timeline.

In the end, Atlassian ended the server product, but still offers their data center product (basically a private-instance deployment) for those who need it. In effect, they offer a Cloud-Prem model fully managed by the customer. Atlassian’s cloud growth is strong, but they had to provide extensive migration help, discounts, and even reintroduce some new on-prem capabilities to appease hesitant customers.

• Key Lessons: If you plan to phase out an on-prem offering in favor of cloud, expect resistance and plan for a lengthy hybrid period. Customers invest a lot in on-prem customizations and won’t switch overnight. It’s crucial to communicate early and often, to provide a compelling reason (beyond “we want you on cloud”), and to make sure your cloud product can truly handle all use cases or you’ll have gaps. Atlassian learned that an “all or nothing” approach risked alienating their largest clients. The takeaway is that supporting a Cloud-Prem or hybrid model might be necessary for the foreseeable future for certain customer segments. You can’t simply shut it off without offering a robust alternative.

Early-Stage Startups – Caution on Premature BYOC

For very young companies, supporting Cloud-Prem can be a heavy lift. Many early-stage startups initially focus on a single-tenant SaaS for simplicity, even turning away on-prem requests until they have the resources. The challenge is, if your first big potential customer demands an on-prem deployment, do you divert development to accommodate it or stick to your roadmap? Some startups that chased early on-prem deals found themselves slowed down by custom work and support, potentially missing out on scaling the core product. On the other hand, ignoring on-prem entirely can shut you out of lucrative markets, like government or finance, until much later.

One strategy is to leverage third-party platforms to handle the on-prem packaging. For example, companies can use Replicated, Portainer, or Glasskube. These companies offer frameworks to package a SaaS app into an easy-to-install on-prem product (handling licensing, updates, etc.). Using such tools can save time. Several startups have successfully used Replicated to deliver an on-prem version with a small team. The trade-offs are cost (these platforms aren’t cheap) and that you still need to test and support the deployments. An alternative approach is to designate on-prem as a milestone to complete once the cloud product is stable.

• Key Lessons: Early stage companies should weigh the market carefully. If on-prem/BYOC is a huge differentiator for your domain (e.g., dev tools for enterprises and data platforms), it might be worth investing early. Otherwise, it might be prudent to achieve product maturity with SaaS first, then expand. If you do take on an early on-prem customer, make it a collaborative design partner and generalize the solution for future reuse, rather than a one-off custom hack. Price it in a way that justifies the extra effort (enterprise pricing). Many founders have been lured by a big logo requesting on-prem, only to realize later the deal wasn’t profitable after accounting for the engineering time. It’s a balancing act and success often means either saying “not yet” until you’re ready, or using stop-gap solutions like managed hosting in the customer’s cloud (essentially a manual BYOC) to bridge the gap.

Case study summary

Case Study	Delivery Model	Outcome/Status	Key Lessons
Redpanda	BYOC streaming platform (customer cloud deployment, vendor managed)	Won enterprise adoption; customers saw ~10× cost savings vs. SaaS Kafka. Confluent reacted via acquisition.	BYOC can outperform multi-tenant SaaS at scale. Control/data plane separation and customer storage integration offer major efficiency and sovereignty wins.
Couchbase Capella	Managed NoSQL DBaaS with hybrid options (cloud service + customer-managed operator)	Successfully migrated on-prem customers to cloud. Supports cloud, on-prem, and hybrid replication.	Deployment flexibility eases transitions. Kubernetes automation (Operator) is essential. Hybrid support is a competitive feature, not a legacy burden.
DeltaStream	Private SaaS/BYOC stream processing (Flink-based)	Early-stage; gained traction with BYOC-first model. Raised $15M Series A.	BYOC from day one attracts enterprises, but requires early investment. Focus on security and simplicity to reduce friction in customer environments.
Palantir	On-premise and hybrid analytics platform (Apollo deployment system)	Achieved ~81% margins while supporting on-prem, hybrid, and cloud for clients like Airbus and Morgan Stanley.	Initial services model evolved into automated delivery via Apollo. Internal tooling is key to scaling. High-quality enterprise focus pays off.
Atlassian	SaaS and on-prem (Server/Data Center); attempted cloud-only migration	Phased out Server edition in 2024; retained Data Center for hybrid clients due to migration resistance.	Forced cloud migration caused backlash. Hybrid support remains essential. Communicate transitions clearly and provide long-term options.
Early-stage Startups	SaaS-first, then on-prem later (or via third-party tools like Replicated)	Many delay BYOC until Series B or beyond. Some use Replicated to ship early on-prem with small teams.	On-prem is costly early on. Weigh distraction vs. revenue. Use packaging platforms to reduce friction. Price BYOC to reflect support overhead.

Cloud-Prem can unlock enterprise adoption by letting customers keep their data at home while still reaping the benefits of a managed cloud experience, but only if providers treat it as a first-class product. It has great potential to become a large revenue generator with only a handful of customers, although it comes at an automation and maintenance cost. Done right, Cloud-Prem marries cloud convenience with on-prem control and can produce stellar product experiences while providing compliance and data security guarantees, which is rarely possible with SaaS solutions.

References