Best AI gateway for hybrid AI deployment environments

Enterprises today are under dual pressure: on one side, the need to innovate quickly with the latest AI models, and on the other, the responsibility to protect sensitive data and meet strict compliance requirements. Relying solely on public LLM APIs exposes organizations to regulatory and security risks, while going fully private or air-gapped often slows innovation and creates operational overhead.

Hybrid offers a balance. This approach lets enterprises keep moving fast while staying compliant and secure, making hybrid AI the most practical path forward.

What makes hybrid AI deployments different

For years, enterprises that prioritized security defaulted to air-gapped environments. In these setups, models run entirely disconnected from the internet, ensuring that sensitive data never leaves the organization’s perimeter. While this delivers strong protection, it also creates a rigid system: updates are slow, integrating new models requires heavy lift, and teams often miss out on the pace of innovation happening in the broader AI ecosystem.

Hybrid deployments change this equation. Instead of an all-or-nothing choice, enterprises can decide what runs where. Sensitive data and regulated workloads can stay within private or VPC environments, while non-sensitive or exploratory use cases can rely on public LLM APIs. This flexibility not only reduces security risks but also unlocks faster iteration.

Another key distinction is operational efficiency. Hybrid environments allow teams to roll out new models, patch updates, and experiment with new capabilities without the long lead times of an isolated system. The result is a deployment strategy that gives enterprises both control and agility, securing what must be protected, while keeping the door open to the best tools this AI landscape has to offer.

Key capabilities to look for in an AI gateway for hybrid deployments

Enterprises adopting hybrid AI need guarantees around where data lives, who can access it, and how it flows across environments. The right AI gateway should align tightly with enterprise cloud strategies and regulatory obligations.

1. Data residency and control
A hybrid gateway must ensure requests, logs, and model outputs stay within the required geography. For organizations operating in the EU, healthcare, or financial services, this isn’t optional, it’s a regulatory mandate. The gateway should offer region-specific hosting, with the ability to keep sensitive traffic within AWS, Azure, or GCP regions chosen by the enterprise.

2. Enterprise cloud integrations
Hybrid deployments often span multiple cloud providers. The AI gateway should integrate natively with AWS Bedrock, Azure OpenAI, GCP Vertex AI and more, while also supporting on-prem deployments of open-weight models. This allows enterprises to consolidate access without building custom connectors for each environment.

3. Unified policy enforcement
Security policies shouldn’t fragment across environments. A strong gateway provides a single place to apply RBAC, authentication, rate limits, and budgets, whether the traffic goes to a public API, a VPC endpoint, or an on-prem cluster.

4. Logging and observability in the right place
Enterprises need visibility into usage and errors, but they also need control over where logs are stored. An enterprise-grade gateway ensures observability data can be stored regionally (e.g., EU logs stay in the EU), aligning with compliance requirements.

5. Flexible deployment modes
Some enterprises will prefer SaaS gateways, while others will demand private deployment of the gateway itself inside their VPC. The gateway should offer both, so that data never leaves environments that need to remain sealed.

6. Extensibility for open-weight models
Hybrid AI often includes running LLaMA, Mistral, or other open-weight models privately. The gateway should make these deployments first-class citizens, not second-class add-ons, with the same observability, routing, and policy layers as cloud APIs.

Portkey’s hybrid AI gateway

For enterprises with strict data residency requirements, Portkey offers a hybrid deployment architecture that delivers both control and simplicity. In this model, the AI gateway and data plane run inside your environment, while Portkey manages the control plane for centralized governance and management.

This approach ensures that all sensitive LLM data stays within your infrastructure, meeting compliance requirements without adding operational overhead. At the same time, enterprises benefit from the reduced latency of in-VPC calls and the ease of Portkey’s managed control plane.

Key features of the hybrid deployment model:

• AI gateway deployed directly in your VPC or private environment
• All sensitive prompts, completions, and logs remain within your infrastructure
• Control plane hosted by Portkey to simplify configuration, monitoring, and policy management\
• End-to-end encryption between all components
• Significantly reduced latency for API calls

Every enterprise deployment of Portkey, including hybrid, comes with enterprise-grade security and compliance built in: SOC 2, ISO 27001, GDPR, and HIPAA certifications, PII anonymization, encryption at rest and in transit, and custom data retention policies.

Hybrid deployment with Portkey gives enterprises the best of both worlds: security and residency control where it’s needed, paired with the operational simplicity of a managed platform.

Next steps

Hybrid AI deployment is quickly becoming the standard for enterprises that want the innovation of public LLMs without compromising on security and compliance. By combining private deployments with cloud-based APIs, organizations can decide what runs where, securing sensitive workloads while still moving fast.

The AI gateway is the control point that makes this possible. It enforces data residency, applies consistent policies, and gives teams the observability they need to operate across environments confidently.

Portkey’s hybrid AI gateway is built for this reality: giving enterprises residency control, enterprise-grade security, and operational simplicity in one platform.

Ready to explore hybrid AI for your organization?

Book a demo with Portkey and see how enterprises are using the hybrid AI gateway to scale securely across public, private, and on-prem environments.