Self-Hosted AI: Complete Control Over Your AI Infrastructure

What "Self-Hosted AI" Means

Self-hosted AI means running AI models inside your own cloud account or on-premises servers. No prompts or answers leave your control. This setup avoids public SaaS endpoints and keeps data under the same security rules you already use for the rest of your workloads.

Unlike public AI services where your data traverses the open internet and potentially trains someone else's models, self-hosted AI keeps everything within your security perimeter. Your organization maintains complete control over the infrastructure, data flow, and model access.

Why Companies Want It

The growing demand for self-hosted AI solutions stems from several critical enterprise requirements:

Data Protection

Proprietary data never trains someone else's model. When you use public AI services, your inputs may be used to improve the underlying models—potentially exposing sensitive information or competitive insights. Self-hosted solutions ensure your data remains exclusively yours.

Compliance

Self-hosted AI enables easier audits and regional data residency. For organizations in regulated industries like healthcare, finance, or government, maintaining clear data boundaries is non-negotiable. Self-hosted solutions allow you to implement the precise controls required by GDPR, HIPAA, FINRA, and other regulatory frameworks.

Network Control

Private endpoints mean no open internet traffic. Self-hosted AI can operate entirely within your virtual private cloud or corporate network, eliminating exposure to public networks and reducing attack surfaces. This network isolation is particularly valuable for sensitive workloads.

Flexibility

Pick the model, GPU size, and scaling rules you need. Self-hosted solutions give you granular control over your AI infrastructure, allowing you to optimize for your specific use cases rather than accepting the one-size-fits-all approach of public services.

AWS Option: Amazon Bedrock

Amazon Bedrock provides a fully managed service that makes leading foundation models available through a unified API, while maintaining strong data privacy guarantees.

Bedrock suits firms that already rely on AWS IAM, KMS encryption, and regional isolation. The service integrates seamlessly with existing AWS security controls, making it particularly attractive for organizations with established AWS footprints.

Amazon's strong commitment that customer data will never be used to train the base models represents one of the clearest data protection guarantees in the industry. This policy extends to all aspects of Bedrock usage, including prompts, outputs, and fine-tuning datasets.

Azure Option: Azure OpenAI Service

Microsoft's Azure OpenAI Service brings OpenAI's powerful models into Azure's enterprise-grade cloud environment, with robust security and compliance features.

Isolation Modes

Azure offers both single-tenant and shared deployment options, but both keep data inside your subscription. This flexibility allows organizations to balance performance, cost, and isolation requirements based on their specific needs.

No Training by Default

Microsoft never uses your content to retrain OpenAI models. This commitment provides clarity around data usage and helps organizations maintain control over their intellectual property when using Azure OpenAI Service.

Regional Deployment

Choose any Azure region that meets your compliance needs. This regional flexibility helps organizations address data sovereignty requirements and regulatory constraints specific to their industry or geography.

Integration

Azure OpenAI Service plugs into Azure AI Studio, Cognitive Search, and Microsoft security tooling. This deep integration with the broader Microsoft ecosystem makes it particularly valuable for organizations already invested in Microsoft technologies.

Azure fits enterprises deep into Microsoft 365 and Azure Active Directory. The seamless identity and access management integration simplifies deployment and reduces security risks by leveraging existing authentication mechanisms.

Google Cloud Option: Vertex AI

Google Cloud's Vertex AI provides a unified platform for building, deploying, and scaling machine learning models, including foundation models like Gemini.

Data Retention Controls

By default, inputs and outputs for Gemini models cache for up to 24 hours; set zero-retention if required. This granular control over data retention helps organizations implement precise data lifecycle policies based on their security and compliance requirements.

Private Services Access

Reach models over internal IP addresses via VPC peering. This network isolation capability ensures that traffic between your applications and Vertex AI never traverses the public internet, reducing exposure to potential threats.

Model Choices

Vertex AI offers access to Gemini 1.5 Pro, Imagen 2, and third-party OSS models. This diversity of model options allows organizations to select the right capabilities for their specific use cases, from text generation to image creation.

Governance

Cloud DLP, Audit Logs, and CMEK encryption extend to Vertex AI. These governance capabilities ensure that AI workloads benefit from the same robust security controls as other Google Cloud services, simplifying compliance and risk management.

Vertex works well for teams already using BigQuery, Looker, and Google Workspace. The integration with Google's data analytics stack makes it particularly valuable for organizations looking to combine AI capabilities with data-driven insights.

Picking the Right Cloud

Selecting the optimal self-hosted AI platform requires careful consideration of several key factors:

The right choice often aligns with your existing cloud investments. Organizations with substantial AWS deployments will find Bedrock's integration with familiar services valuable, while Microsoft-centric enterprises may benefit more from Azure OpenAI Service's seamless connection to their existing identity and productivity tools.

Best Practices for Any Self-Hosted AI

Regardless of which cloud provider you choose, implementing these best practices will enhance the security and effectiveness of your self-hosted AI deployment:

Use Private Networking

Implement VPC endpoints, PrivateLink, or VPC Service Controls to ensure AI traffic never traverses the public internet. This network isolation represents one of the most fundamental security controls for self-hosted AI deployments.

Encrypt Everything

Apply encryption at rest with KMS/CMEK and in transit with TLS 1.2+. Comprehensive encryption ensures that data remains protected throughout its lifecycle, from storage to processing to transmission.

Log and Monitor

Implement comprehensive logging with CloudTrail, Azure Monitor, or Cloud Audit Logs. Effective monitoring allows you to detect unusual patterns, troubleshoot issues, and maintain an audit trail for compliance purposes.

Apply Least-Privilege IAM

Limit who can invoke models or upload training data. Implementing the principle of least privilege reduces the risk of unauthorized access and helps contain the impact of potential security incidents.

Set Data-Retention Rules

Disable caching where policies require zero retention. Clear data lifecycle policies ensure that information is retained only as long as necessary, reducing both security risks and compliance concerns.

Key Takeaways

Self-hosted AI keeps control, compliance, and flexibility in your hands. By deploying AI capabilities within your existing security boundaries, you maintain complete oversight of your data and infrastructure while still benefiting from advanced AI capabilities.

AWS Bedrock offers the strongest "no data training" guarantee and private networking out-of-the-box. This clear commitment to data privacy makes Bedrock particularly attractive for organizations with stringent intellectual property protection requirements.

Azure OpenAI pairs tightly with Microsoft identity and office ecosystems. This integration makes it an efficient choice for organizations already invested in Microsoft technologies, reducing implementation complexity and improving user experience.

Vertex AI gives granular retention settings and deep ML tooling. These capabilities make it well-suited for organizations with sophisticated machine learning requirements or those looking to combine AI with advanced analytics.

Pick the platform that aligns with your existing cloud footprint and security controls, then layer on the practices above to protect your proprietary data. This strategic approach ensures that your self-hosted AI deployment enhances your capabilities while maintaining the security posture your organization requires.