What You Get
- Secure AI environment you can trust
- Cost control without overspending
- Auditable operations
- Informed decisions for your context
Why In-house Deployment (Understanding the Risk)
While training data usage often gets attention, API calls send input data externally. Depending on data sensitivity, regulations, and audit requirements, you may need to keep data boundaries within your organization.
Key Support Areas
- Data Boundary Design: Define classification (confidential/internal/public) and storage/processing boundaries.
- Model Deployment Strategy: Choose between on-premise, private network, or VPC deployment for OSS or commercial models.
- Access Control & Key Management: Integrate identity systems, enforce least privilege, manage key rotation.
- Logging & Audit: Track input/output, mask PII, define retention and access policies.
- Cost Design: Plan inference resources (GPU/CPU), scaling strategy, TCO by model, phased rollout.
Expected Outcomes
- Security & Confidence: Maintain data boundaries while meeting compliance requirements
- Cost Control: Optimize TCO through appropriate deployment choices
- Auditability: Maintain visibility into operations for compliance
- Flexibility: Choose OSS/commercial and deployment options based on your needs