So, you’ve chosen your model. Maybe you’re working with DeepSeek-R1-0528 to keep things open and flexible, or you’re exploring the polished simplicity of Gemini 2.0 API. Either way, the next question is: how do you take your LLM to production, securely, at scale, and in compliance with enterprise standards?

Welcome to post 4 of our LLM-on-GCP series. In this guide, we break down the essential pieces for safely and confidently running LLMs in production on Google Cloud Platform (GCP).

Security Deep Dive

Security is foundational, especially when your LLM might process sensitive customer data or proprietary documents. Regardless of if you’re managing the stack yourself or using a managed API, GCP gives you the building blocks to keep your deployments protected.

Self-hosted DeepSeek: Your model, your rules

When running any LLM on Compute Engine, your data is protected with encryption in transit and at rest, by default. That means everything from API calls to the model to the storage disks where logs live is encrypted using GCP’s security infrastructure.

But because this is a self-hosted setup, you’re responsible for maintaining the broader security posture: IAM configurations, network policies, and ensuring no loose ends in your deployment.

GCP gives you the tools to do this effectively. Teams can implement a Zero Trust Architecture (ZTA) by leveraging GCP’s identity-aware proxies, context-aware access, and BeyondCorp framework. To further isolate workloads, you can use VPC Service Controls and Private Service Connect to enforce data boundaries and minimize exposure between services, especially important for models processing sensitive or regulated data.

Gemini Pro API: Certified and compliant out of the box

If your team prefers a turnkey approach, Gemini Pro API comes with enterprise-grade protections and a long list of compliance certifications, including ISO 27001, SOC 1/2/3, and more. It’s also aligned with HIPAA and GDPR, making it a safe pick for highly regulated industries.

In short, Gemini takes care of the heavy lifting so your team can focus on building without worrying about the security plumbing.

Scalability That Grows With You

LLMs are compute-hungry, and that appetite only grows in production. If you’re expecting steady usage or spiky traffic, GCP gives you flexible options to match demand without maxing out your budget.

Vertex AI + DeepSeek: Autoscale with ease

If you’ve deployed DeepSeek on Vertex AI, you can take advantage of built-in autoscaling, letting the system automatically adjust compute resources based on usage. No need to manually spin up or down GPU instances because Vertex AI handles that for you.

This is ideal for teams looking for balance: the control of open-source with the simplicity of a managed environment.

Advanced Workload Orchestration with GKE

If your team is container-native, Google Kubernetes Engine (GKE) offers an alternative path. With fine-grained autoscaling, node pool flexibility, and native GPU support, GKE is a powerful option for deploying DeepSeek in containerized production environments, especially when you need precise control over workloads across clusters.

Compute Engine + Preemptible VMs: Cost-conscious scaling

Prefer to keep things lean? DeepSeek can also run on Compute Engine using preemptible VMs: temporary virtual machines that cost a fraction of the usual price. These are great for batch jobs or fault-tolerant workloads that can afford interruptions and aren’t latency-sensitive.

Just be prepared to handle the orchestration yourself. This route gives you the most control, but also the most responsibility.

Gemini Pro API: Scalability, solved

No infrastructure to manage, no scaling configs to tune. Gemini Pro is a fully managed API that adjusts behind the scenes, ensuring your apps stay fast and responsive without you ever needing to think about infrastructure.

Compliance Without the Headache

Auditability and compliance are critical for production deployments, especially in finance, healthcare, or enterprise environments.

Vertex AI: Built-in audit trails

Running DeepSeek on Vertex AI gives you access to Cloud Audit Logs, which record user activity, data access, and system events automatically. These logs are fully integrated with GCP’s monitoring tools, giving your compliance team the transparency they need.

Compute Engine: Roll your own logging

In contrast, if you’re self-hosting DeepSeek on Compute Engine, you’ll need to configure and maintain your own logging and monitoring setup (tools like Cloud Logging, Cloud Monitoring, or even third-party observability platforms can help). It’s doable, but it’s your responsibility to ensure logs are secure, centralized, and audit-ready.

Gemini Pro API: Compliance baked in

Because Gemini is part of Google’s managed AI ecosystem, you automatically get access to secure logging, monitoring, and enterprise-grade audit trails. If compliance is a top concern and you’d rather not build from scratch, Gemini has you covered.

Key Takeaway

So there you have it, if you want to go all in on open-source with DeepSeek or tap into the streamlined power of Gemini Pro API, GCP gives you the freedom to choose, without compromising on security, scalability, or compliance.

You don’t have to pick between flexibility and enterprise readiness. You can have both.

Ready to Productionize Your LLM?

This series is all about giving you the clarity and confidence to launch responsibly with AI on GCP. If you’re figuring out how to scale securely or deciding between Vertex AI and Compute Engine, we’re here to help.

Schedule a consultation below and let’s bring your LLM strategy to life.