Deployment Models

Naas supports multiple deployment models to meet diverse enterprise requirements, from cloud-native SaaS deployments to air-gapped government installations. Each model is designed to maintain full platform functionality while meeting specific security, compliance, and operational needs.

Professional Services Implementation

The enterprise capabilities described in this section represent our ability to implement these solutions through our professional services team. Each deployment is customized to your specific requirements and implemented with dedicated support. Contact our enterprise team at [email protected] to discuss your needs and implementation timeline.

Cloud-Native Deployment

Kubernetes-Native Architecture

Naas is built from the ground up as a cloud-native application, leveraging Kubernetes for container orchestration and management.

Core Components:

Control Plane: Manages platform configuration, user management, and orchestration
Agent Runtime: Executes AI agents with auto-scaling based on demand
Data Layer: Distributed storage with automatic replication and backup
API Gateway: Secure API access with rate limiting and authentication

Kubernetes Resources:

# Example Naas deployment configuration
apiVersion: apps/v1
kind: Deployment
metadata:
  name: naas-platform
spec:
  replicas: 3
  selector:
    matchLabels:
      app: naas-platform
  template:
    spec:
      containers:
      - name: naas-api
        image: naas/platform:latest
        resources:
          requests:
            memory: "2Gi"
            cpu: "1000m"
          limits:
            memory: "4Gi"
            cpu: "2000m"

Multi-Region Deployment

Global Distribution Strategy:

Primary Region: Main deployment with full functionality
Secondary Regions: Read replicas with failover capability
Edge Locations: Cached responses for improved performance
Data Residency: Configurable data storage locations for compliance

Regional Architecture:

Auto-Scaling Configuration

Horizontal Pod Autoscaler (HPA):

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: naas-agent-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: naas-agent-runtime
  minReplicas: 2
  maxReplicas: 50
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

Vertical Pod Autoscaler (VPA):

Automatic resource request optimization
Memory and CPU usage analysis
Recommendation-based scaling
Cost optimization through right-sizing

Managed Service Options

AWS Deployment:

EKS: Managed Kubernetes with AWS integration
RDS: Managed database with automatic backups
ElastiCache: Redis caching for improved performance
ALB: Application load balancer with SSL termination

Google Cloud Deployment:

GKE: Google Kubernetes Engine with autopilot mode
Cloud SQL: Managed PostgreSQL with high availability
Memorystore: Managed Redis for caching
Cloud Load Balancing: Global load balancing with CDN

Azure Deployment:

AKS: Azure Kubernetes Service with Azure integration
Azure Database: Managed PostgreSQL with geo-replication
Azure Cache: Redis cache with clustering support
Application Gateway: Layer 7 load balancing with WAF

On-Premises Installation

Air-Gapped Environment Setup

Complete Offline Operation:

No external internet connectivity required
Local container registry for all images
Offline model hosting and inference
Local documentation and support resources

Infrastructure Requirements:

# Minimum hardware specifications
control_plane:
  nodes: 3
  cpu_per_node: 8 cores
  memory_per_node: 32 GB
  storage_per_node: 500 GB SSD

worker_nodes:
  nodes: 5
  cpu_per_node: 16 cores
  memory_per_node: 64 GB
  storage_per_node: 1 TB SSD
  gpu_optional: NVIDIA A100 or equivalent

storage:
  distributed_storage: 10 TB minimum
  backup_storage: 20 TB minimum
  replication_factor: 3

Local Model Hosting:

Ollama Integration: Local LLM hosting with GPU acceleration
Model Registry: Private model repository with version control
Inference Scaling: Automatic model loading and unloading
Custom Models: Support for fine-tuned and domain-specific models

Hybrid Connectivity

Secure External Connections:

VPN Tunnels: Site-to-site VPN for secure data access
API Gateways: Controlled external API access with monitoring
Data Sync: Scheduled synchronization with external data sources
Audit Logging: Complete logging of all external communications

Network Architecture:

Custom Infrastructure Integration

Enterprise System Integration:

Active Directory: LDAP/SAML integration for authentication
Database Connections: Direct connections to enterprise databases
Message Queues: Integration with existing messaging systems
Monitoring Systems: Integration with enterprise monitoring tools

Hardware Optimization:

GPU Clusters: NVIDIA DGX or equivalent for AI workloads
High-Performance Storage: NVMe SSD arrays for fast data access
Network Optimization: 10GbE or higher for inter-node communication
Backup Systems: Integration with enterprise backup solutions

Government and Defense Deployments

FedRAMP Compliance

FedRAMP Moderate Requirements:

Security Controls: Implementation of NIST SP 800-53 controls
Continuous Monitoring: Real-time security monitoring and reporting
Incident Response: Automated incident detection and response procedures
Documentation: Complete system security documentation

FedRAMP High Requirements:

Enhanced Controls: Additional security controls for high-impact systems
Cryptographic Standards: FIPS 140-2 Level 3 or higher
Personnel Security: Background check requirements for system access
Physical Security: Enhanced physical security controls

DoD Compliance

Security Requirements Guide (SRG) Compliance:

STIG Implementation: Security Technical Implementation Guides
Risk Management Framework: NIST RMF implementation
Continuous Monitoring: Real-time security posture assessment
Vulnerability Management: Automated vulnerability scanning and remediation

Classification Level Support:

Unclassified: Standard security controls
Controlled Unclassified Information (CUI): Enhanced data protection
Classified: Support for classified data processing (with appropriate accreditation)

GovCloud Deployment

AWS GovCloud Integration:

Isolated Regions: Physically and logically separated infrastructure
Compliance: FedRAMP High and DoD SRG compliance
Personnel: US persons-only access to infrastructure
Audit: Enhanced audit logging and compliance reporting

Azure Government Integration:

Government Regions: Dedicated government cloud regions
Compliance Certifications: FedRAMP, DoD SRG, and other government standards
Data Residency: Guaranteed US data residency
Support: Government-cleared support personnel

SCIF Compatibility

Sensitive Compartmented Information Facility Requirements:

Physical Security: Compliance with ICD/ICS 705 standards
Electromagnetic Security: TEMPEST compliance for classified processing
Access Control: Biometric and multi-factor authentication
Audit Trail: Complete audit trail for all system access and operations

Deployment Patterns:

# SCIF deployment configuration
scif_deployment:
  physical_security:
    - tempest_compliance: true
    - acoustic_protection: true
    - visual_protection: true
  
  network_security:
    - air_gapped: true
    - encrypted_storage: "AES-256"
    - secure_communications: "Suite B cryptography"
  
  access_control:
    - biometric_authentication: true
    - multi_factor_authentication: true
    - continuous_monitoring: true

Deployment Planning

Requirements Assessment

Security Requirements:

Compliance frameworks (FedRAMP, SOC 2, ISO 27001)
Data classification and handling requirements
Network security and segmentation needs
Authentication and authorization requirements

Performance Requirements:

Expected user load and concurrent sessions
Data processing and storage requirements
Response time and availability targets
Disaster recovery and business continuity needs

Integration Requirements:

Existing system integration points
Data source connections and formats
API requirements and rate limits
Monitoring and logging integration

Architecture Design

Reference Architectures:

Small Deployment: Single region, 100-500 users
Medium Deployment: Multi-region, 500-5000 users
Large Deployment: Global, 5000+ users with high availability
Government Deployment: Air-gapped or GovCloud with enhanced security

Capacity Planning:

# Capacity planning template
deployment_size: medium
expected_users: 2000
concurrent_users: 400
data_volume: 10TB
growth_rate: 20% annually

infrastructure:
  kubernetes_nodes: 12
  cpu_cores: 192
  memory_gb: 768
  storage_tb: 50
  network_bandwidth: "10 Gbps"

Implementation Timeline

Phase 1: Foundation (Weeks 1-4):

Infrastructure provisioning
Network configuration and security setup
Basic platform installation and configuration
Initial testing and validation

Phase 2: Integration (Weeks 5-8):

Enterprise system integration
Data source connections
Authentication and authorization setup
Security hardening and compliance validation

Phase 3: Deployment (Weeks 9-12):

Production deployment and cutover
User onboarding and training
Performance optimization and tuning
Go-live support and monitoring setup

Phase 4: Optimization (Weeks 13-16):

Performance monitoring and optimization
User feedback incorporation
Additional feature enablement
Long-term support transition

For detailed deployment planning assistance, contact our enterprise team at [email protected].

Cloud-Native Deployment​

Kubernetes-Native Architecture​

Multi-Region Deployment​

Auto-Scaling Configuration​

Managed Service Options​

On-Premises Installation​

Air-Gapped Environment Setup​

Hybrid Connectivity​

Custom Infrastructure Integration​

Government and Defense Deployments​

FedRAMP Compliance​

DoD Compliance​

GovCloud Deployment​

SCIF Compatibility​

Deployment Planning​

Requirements Assessment​

Architecture Design​

Implementation Timeline​