How Enterprises Can Scale AI Securely with BYOC and On-Prem Deployments

As an AI leader, you often face a conflicting set of priorities: deploying new models faster than ever, while ensuring strict governance, compliance, and data ownership.

Both are crucial in today’s AI arms race. Every month brings a new frontier model, a competitor rolling out another product, or a customer expecting more innovative services. This means falling behind on deployment speed isn’t just a technical delay; it’s a competitive disadvantage. At the same time, the governance mandate has never been stronger. Regulators, customers, and boards expect sensitive data to remain protected and auditable, and the costs of non-compliance can be substantial.

Fortunately, the days of having to choose between speed and stewardship are quickly coming to an end. With Bring Your Own Cloud (BYOC) and on-prem deployment, your enterprise can scale AI inference securely, efficiently, and in full compliance.

Why Traditional Model Inference Falls Short#

After years of accelerated adoption, with over 78% of enterprises implementing AI across business functions, most AI leaders have come to rely on fully managed inference API services or DIY on-prem deployment setups.

While both approaches often work well in early experimentation, such as proof-of-concept demos or hackathon prototypes, their limitations become apparent once workflows move into production.

Inference APIs compromise control and flexibility#

While serverless inference APIs like GPT-4o and Claude 4 promise agility by removing infrastructure overhead, they shift critical data and workflows into vendor cloud environments. That means customer records, intellectual property, and internal knowledge bases all live outside your environment. Not only is this an auditability and compliance nightmare across highly regulated industries, but it also creates risk for any enterprise entrusted with the handling, storage, and auditing of sensitive data.

Flexibility is another challenge. Inference APIs are designed for broad adoption, which makes them difficult to integrate into bespoke systems. This means that teams often run into friction when trying to plug APIs into their own CI/CD systems, custom monitoring dashboards, or access policies. The rigidity of these vendor-managed systems also impacts inference optimizations, as tradeoffs between cost savings and latency are often dictated by the vendor — not by the enterprise.

And despite inference APIs saving enterprises money from an engineering perspective, egress fees, storage charges, and premium add-ons can quickly eat into those savings. In fact, cloud waste accounted for 30% of enterprise cloud budgets in 2021, according to Flexera, and rose to 32% in 2022. That 2% jump alone represents a massive amount of wasted spend, highlighting just how quickly inference APIs can become unsustainable at enterprise scale.

DIY on-prem deployments create operational drag#

The shortcomings of serverless inference APIs might push teams toward self-hosting, such as DIY on-prem deployments. While this path provides enterprises with complete control over their models and data, that ownership comes at a steep operational cost.

Standing up production-grade infrastructure requires a hefty capital investment, in-house expertise, months of GPU provisioning, manual configuration, and ongoing maintenance. Even when these needs have been met, idle capacity is common, leaving expensive hardware underutilized.

To fully utilize your compute, you need fast autoscaling that adapts to changing demand. But scaling adds further complexity. Building reliable autoscaling, seamless upgrade paths, and robust monitoring requires significant engineering investment. Additionally, features that are now expected as table stakes, like prefix caching or prefill–decode disaggregation, can also take entire teams months to develop and test internally.

And if standing up infrastructure wasn’t intensive enough, establishing a baseline for compliance is another hurdle. Enterprises are often forced to reinvent encryption, secret management, and authentication from scratch. While these are essential for meeting baseline compliance standards, the reinvention process is time-intensive and often results in patchwork systems that don’t integrate cleanly with the rest of the enterprise stack.

Over time, the costs of maintaining this infrastructure climb sharply. What starts as a path to sovereignty often turns into a maintenance treadmill, draining engineering bandwidth while slowing innovation.

BYOC: Control and Agility in One Unified Package#

Today’s AI leaders require the speed of a managed service while maintaining control of their data — that’s where BYOC comes in. By combining self-hosting control with the convenience of a managed platform, BYOC deployments offer operational agility without sacrificing ownership or oversight.

The core advantage of BYOC is data residency and ownership. Models and sensitive datasets remain within your virtual private cloud (VPC), which also hosts third-party applications and software. This structure keeps customer records, internal knowledge, and IP under your governance, while accelerating model deployment.

BYOC is also designed for compliance readiness. Because all workflows execute within your environment, you can enforce the same policies you use elsewhere, including RBAC, SSO, encryption, and audit logs. This also makes aligning with enterprise data compliance standards such as SOC 2 Type II, HIPAA, and ISO 27001 considerably easier. Instead of rebuilding controls from scratch, teams adopt an operating model that’s engineered for compliance and governance.

There’s a clear cost benefit as well. Processing data within your own accounts minimizes egress charges, and autoscaling keeps GPU utilization high to avoid paying for idle capacity. If you’re involved in a startup or incubator program and qualify for cloud credits, they can also be applied within BYOC deployments, improving ROI without duplicating spend.

And for enterprises with multi-cloud requirements, BYOC supports inference deployment on AWS, Azure, or GCP, helping distribute workflows evenly to meet regional data requirements, avoid single-cloud lock-in, and serve global customers with consistent performance.

Far from being experimental, BYOC has already been proven in production. Yext uses BentoCloud’s BYOC to serve multi-cloud/global customers while keeping data compliant, reducing compute costs by up to 80% while shipping 2× more models. Today, they run more than 150 models in production, supported by efficient autoscaling and multi-region deployments.

On-prem Deployments for Full Sovereignty without the Overhead#

For enterprises operating within sensitive industries such as defense, healthcare, and government, on-prem deployments ensure complete ownership of both AI workflows and the physical systems that power them.

On their own, however, most DIY on-prem deployments are notoriously resource-intensive, from managing compliance frameworks to standing up scalable infrastructure. That’s why many AI leaders turn to the Bento Inference Platform to make on-prem viable in practice. With Bento, enterprises can achieve production-ready inference at scale without having to reinvent the wheel.

On-prem deployments through Bento provide the highest levels of data control, flexibility, and security, while also eliminating common bottlenecks with autoscaling, distributed serving, and observability. Instead of piecing together disparate tools, enterprises gain a resilient, efficient system capable of supporting mission-critical workloads.

The cost equation improves as well. Efficient GPU scheduling reduces idle time, while standardized upgrade paths and monitoring minimize the need for constant engineering intervention. And uniquely, the Bento Inference Platform supports hybrid bursting: enterprises can run steady workloads on in-house GPUs while seamlessly extending into the cloud during peak demand. This balance of sovereignty and elasticity helps enterprises meet strict regulatory requirements without locking themselves into brittle, high-maintenance infrastructure.

How Bento Exceeds Enterprise Speed and Compliance Requirements#

Bento helps enterprises deploy and operate AI models in their own cloud or on-prem environments with full data ownership, strict access controls, and built-in scalability, automation, and auditability. Its BYOC architecture is designed for security-first enterprises and is already trusted by Fortune 500 companies and global teams with complex compliance needs.

Data privacy and ownership#

Bento’s architecture ensures your sensitive data and models never leave your cloud’s secure environment and is compatible with all general-purpose cloud providers. Control and data planes are separated, with full VPC encapsulation to guarantee isolation. The Bento Inference Platform control plane is also SOC 2 Type II certified, and additional certifications such as HIPAA and ISO 27001 are underway. The result of these benefits? Provable stewardship and data compliance, without vendor lock-in.

Access controls and auditability#

With Bento, you retain fine-grained control over who can access models and data. Role-based access control (RBAC), SSO integration, and audit logs are supported natively, while authentication and encryption are enforced across AI workflows. Usage tracking and cost analysis can be broken down by team or project, helping demonstrate compliance externally while uncovering optimization opportunities internally.

Secrets management#

Managing sensitive credentials securely is often a weak spot in enterprise AI deployments. Bento addresses this with a dedicated secrets feature that stores and injects API keys, authentication tokens, and credentials safely. Secrets can be mounted as environment variables or read-only files and managed via CLI or YAML. This reduces the risk of credential leakage while supporting custom access policies and automated rotation.

Sandboxes for risk reduction#

Bento also provides isolated, ephemeral sandboxes where you can safely test untrusted or dynamically generated code. These environments give teams a controlled way to validate new code paths before they reach production. This reduces risks such as prompt injections or misuse of third-party tools, while keeping core systems secure.

The Solution for Speed, Cost Savings, and Compliance#

Today’s AI leaders know that successful AI deployments are defined by more than technical performance alone. The ultimate goal is balancing speed, cost efficiency, and compliance in a way that scales, whether you’re serving patients, processing financial transactions, or powering consumer apps.

With the right infrastructure, it’s possible to accelerate time-to-market, cut GPU costs dramatically, and prove governance at every step. These gains don’t just improve ROI; they give teams the confidence to innovate without sacrificing oversight.

Bento delivers that balance, helping enterprises scale LLM workflows confidently and securely to stay ahead in a highly competitive market.

Want a custom BYOC or on-prem deployment solution for your use case? Schedule a call with our experts.