Self-Hosting vs. Models-as-a-Service: The Runtime Security Tradeoff

Jessica BrennanSenior Product Marketing Manager

As GenAI systems continue to move from experimental pilots to enterprise-wide deployments, one architectural choice carries significant weight: how will your organization deploy runtime-based capabilities?

Whether you’re building with open-source models hosted on your own infrastructure or integrating APIs from a major provider like OpenAI or Anthropic, the choice between self-hosting and models-as-a-service comes with very different security implications. And for many, those implications may not be immediately visible—but they’re foundational to long-term AI risk management.

Speed vs. Control: Two Paths to Deployment

The model-as-a-service approach offers clear advantages—ease of implementation, rapid scaling, and continuous access to updates. With minimal internal expertise, teams can begin integrating AI into products and workflows almost immediately.

But that convenience comes with tradeoffs: reduced visibility, greater data exposure, and growing dependency on third-party infrastructure. Vendor lock-in and cost unpredictability are just the start. More critically, security teams are left operating in the dark, unable to fully assess how models behave, what protections are in place, or how their data might be handled.

On the other hand, self-hosted models offer maximum control. Organizations can keep sensitive data within their own environments, customize model configurations, and fine-tune deployments to meet unique performance or compliance needs. For those in regulated industries—or those with high-value intellectual property—this autonomy is compelling.

But it’s not without complexity. Self-hosting demands specialized AI and security expertise, along with the operational muscle to manage updates, patch vulnerabilities, and detect emerging threats. Security responsibility doesn’t just shift, it becomes total.

Choosing a Path Without Choosing Blindly

What’s clear is that both deployment models—model-as-a-service and self-hosted—are viable. But neither is without risk. The decision isn’t just about performance or procurement—it’s about where your responsibilities begin, and where they end.

Increasingly, the answer isn’t binary. Many organizations are adopting hybrid approaches, using cloud services for general-purpose applications and reserving self-hosted environments for sensitive use cases. But even hybrid strategies require a clear-eyed understanding of where risk resides and how to architect controls around it.

Our White Paper, Security Risks of GenAI Runtime, dives deeper into this tradeoff, breaking down the security considerations of each deployment model and helping enterprise leaders make informed decisions.

About the Author

Jessica BrennanSenior Product Marketing Manager

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