Most enterprise AI architecture guides start with the wrong question. They ask “cloud or on-prem?” when they should ask “what are we actually trying to protect, and what does our organization need to function?”
The result: teams build infrastructure that doesn’t match how their organization actually adopts AI, or they over-engineer for compliance requirements they don’t have while missing the ones they do.
This guide takes a different approach. We cover three interconnected layers:
- Infrastructure patterns - Where AI physically runs
- Adoption patterns - How organizations actually deploy AI
- Use case architectures - What AI systems actually do
By the end, you’ll understand which combination fits your regulatory environment, organizational maturity, and technical requirements.
The Three-Layer Framework
Enterprise AI architecture isn’t just about servers. It’s the intersection of:
┌─────────────────────────────────────────────────────────────────┐
│ USE CASE ARCHITECTURE │
│ RAG │ Classification │ Generation │ Agents │ Multi-Agent │
├─────────────────────────────────────────────────────────────────┤
│ ADOPTION PATTERN │
│ Shadow AI │ Experimentation │ Artisan │ Augmented │ Production │
├─────────────────────────────────────────────────────────────────┤
│ INFRASTRUCTURE PATTERN │
│ Air-Gapped │ Hybrid │ VPC-Isolated │ Edge │ Multi-Region │
└─────────────────────────────────────────────────────────────────┘
Most failures happen when these layers don’t align. An organization running “shadow AI” (employees using ChatGPT) doesn’t need multi-region sovereign infrastructure. An organization deploying AI agents in healthcare absolutely needs it.
Let’s break down each layer.
Part 1: Infrastructure Patterns
Infrastructure patterns determine where data lives and how it flows. Your compliance requirements typically dictate which patterns are acceptable.
Pattern 1: Fully Air-Gapped
┌─────────────────────────────────────────────────────────────────┐
│ AIR-GAPPED NETWORK │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ INFERENCE TIER │ │
│ │ ┌─────────┐ ┌─────────┐ ┌─────────────────────────┐ │ │
│ │ │ vLLM │ │ TEI │ │ HAProxy/Nginx │ │ │
│ │ │ Cluster │ │Embeddings│ │ Load Balancer │ │ │
│ │ └─────────┘ └─────────┘ └─────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────┘ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ DATA TIER │ │
│ │ ┌─────────┐ ┌─────────┐ ┌─────────────────────────┐ │ │
│ │ │ Qdrant │ │PostgreSQL│ │ Model Registry │ │ │
│ │ │ Vectors │ │ + pgvector│ │ (Harbor/Artifactory) │ │ │
│ │ └─────────┘ └─────────┘ └─────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌────────────────────────┴────────────────────────────────┐ │
│ │ SECURE UPDATE CHANNEL: Physical media / staging env │ │
│ └─────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
Zero internet connectivity. Model weights transfer via physical media or through an isolated staging environment with one-way data flow.
When required:
- Defense/intelligence (classified workloads, NIST 800-171)
- Critical infrastructure (power grids, nuclear facilities)
- Financial trading systems with proprietary algorithms
- Government systems with CUI (Controlled Unclassified Information)
The honest tradeoff: Maximum security, maximum operational burden. Model updates take weeks, not hours. Expect 2-3 dedicated FTEs and $200K-500K annual infrastructure costs. Don’t choose this unless compliance mandates it.
Key components:
- Inference: vLLM or TGI (no external dependencies)
- Embeddings: Hugging Face TEI self-hosted
- Vector DB: Qdrant or pgvector
- Orchestration: Kubernetes or Docker Compose
- Updates: Staged promotion with manual approval
Pattern 2: Hybrid Cloud with Data Classification
┌──────────────────────────────────────────────────────────────┐
│ ON-PREMISE │
│ ┌────────────────────────────────────────────────────────┐ │
│ │ SENSITIVE WORKLOADS (PII, PHI, Financial) │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌────────────────┐ │ │
│ │ │ LLM Inference│ │ Vector DB │ │ Sensitive Data │ │ │
│ │ │ (Llama/Mistral)│ │ (Customer KB)│ │ Store │ │ │
│ │ └─────────────┘ └─────────────┘ └────────────────┘ │ │
│ └────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────┘
│
│ VPN / Private Link
│ (Anonymized/aggregated only)
▼
┌──────────────────────────────────────────────────────────────┐
│ CLOUD │
│ ┌────────────────────────────────────────────────────────┐ │
│ │ NON-SENSITIVE WORKLOADS │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌────────────────┐ │ │
│ │ │ Model Training│ │ Analytics │ │ Monitoring │ │ │
│ │ │ (Anonymized) │ │ Dashboards │ │ (No PII) │ │ │
│ │ └─────────────┘ └─────────────┘ └────────────────┘ │ │
│ └────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────┘
Split workloads by data sensitivity. Sensitive data stays on-premise. Non-sensitive workloads (training on anonymized data, analytics, monitoring) use cloud.
When to use:
- GDPR with EU data residency
- HIPAA with flexibility needs
- Financial services with defined data classification
- Organizations wanting cloud benefits without full exposure
The key requirement: Clear data classification policy. What’s sensitive? What’s not? Technical enforcement (DLP, network segmentation) must match policy.
Cost profile: $50K-150K/year. 2-3 FTEs with hybrid cloud expertise.
Pattern 3: VPC-Isolated Cloud
┌──────────────────────────────────────────────────────────────┐
│ YOUR VPC (AWS/Azure/GCP) │
│ ┌────────────────────────────────────────────────────────┐ │
│ │ PRIVATE SUBNET (No Internet Gateway) │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌────────────────┐ │ │
│ │ │ GPU Instances│ │ Vector DB │ │ Application │ │ │
│ │ │ + vLLM │ │ (Qdrant) │ │ Services │ │ │
│ │ └─────────────┘ └─────────────┘ └────────────────┘ │ │
│ └────────────────────────────────────────────────────────┘ │
│ ┌────────────────────────────────────────────────────────┐ │
│ │ VPC ENDPOINTS (PrivateLink) │ │
│ │ S3 │ Secrets Manager │ CloudWatch │ ECR │ │
│ └────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────┘
Everything runs in private subnets. No internet gateway. Traffic to cloud services goes through VPC endpoints. Data never traverses public internet.
When to use:
- PCI-DSS cardholder environments
- SOC 2 Type II with network isolation
- FedRAMP authorized workloads
- Teams already on cloud wanting better isolation
Critical caveat: VPC isolation is NOT sovereignty. US CLOUD Act still applies to American cloud providers regardless of region. For true sovereignty, you need non-US infrastructure or on-premise.
Cost profile: $30K-100K/year. 1-2 FTEs with cloud security expertise.
Pattern 4: Edge-Distributed
┌─────────────────────────────────────────────────────────────┐
│ CENTRAL CONTROL PLANE │
│ Model Registry │ Config Management │ Fleet Monitoring │
└─────────────────────────────────────────────────────────────┘
│ │ │
┌─────────┘ │ └─────────┐
▼ ▼ ▼
┌───────────┐ ┌───────────┐ ┌───────────┐
│ FACTORY │ │ HOSPITAL │ │ RETAIL │
│ ┌───────┐ │ │ ┌───────┐ │ │ ┌───────┐ │
│ │Phi-4 │ │ │ │Mistral│ │ │ │Llama │ │
│ │14B │ │ │ │7B │ │ │ │3B │ │
│ └───────┘ │ │ └───────┘ │ │ └───────┘ │
│ Local data│ │ PHI stays │ │ POS data │
│ stays here│ │ at clinic │ │ stays here│
└───────────┘ └───────────┘ └───────────┘
Distributed inference at edge locations. Central control plane manages models and configuration. Data never leaves the edge. Only model updates and anonymized metrics flow centrally.
When to use:
- Manufacturing with plant-level AI
- Healthcare with clinic-level PHI
- Retail with store-level inference
- Low-latency requirements (sub-10ms)
- Offline operation requirements
Model selection: Edge requires small models. Phi-3-mini/Phi-4 (3.8-14B), Mistral 7B, Llama 3.2 3B. GPU per node: RTX 4090 or A10G.
Cost profile: $10K-50K per node. 0.5 FTE per 10 nodes for fleet management.
Pattern 5: Multi-Region Sovereign
┌─────────────────────────────────────────────────────────────────┐
│ GLOBAL ROUTING (GeoDNS) │
└─────────────────────────────────────────────────────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ EU REGION │ │ US REGION │ │ APAC REGION │
│ Frankfurt │ │ Virginia │ │ Singapore │
│ ┌─────────┐ │ │ ┌─────────┐ │ │ ┌─────────┐ │
│ │Full Stack│ │ │ │Full Stack│ │ │ │Full Stack│ │
│ │LLM+Vector│ │ │ │LLM+Vector│ │ │ │LLM+Vector│ │
│ │+App+Data │ │ │ │+App+Data │ │ │ │+App+Data │ │
│ └─────────┘ │ │ └─────────┘ │ │ └─────────┘ │
│ EU data only│ │ US data only│ │APAC data only│
│ GDPR/EU AI │ │ CCPA │ │ PDPA/PIPL │
└─────────────┘ └─────────────┘ └─────────────┘
Complete, self-contained infrastructure in each region. User requests route by location. No user data crosses regional boundaries. Only model weights and anonymized metrics sync globally.
When to use:
- Global enterprises with GDPR + CCPA + PIPL simultaneously
- Multinational financial services
- Global healthcare organizations
- Any organization serving users in countries with strict data localization
Cost profile: $300K-1M/year. 4-6 FTEs. This is the most complex pattern. Don’t choose it unless you truly need multi-regional sovereignty.
Part 2: Adoption Patterns
Infrastructure is only half the story. How organizations actually adopt AI matters just as much. Based on research from Scott Logic and enterprise deployments, five patterns emerge:
Pattern A: Shadow AI
Individual employees using ChatGPT, Claude, or Gemini without organizational oversight. No governance. No data controls. High innovation speed but significant risk.
Reality check: 75% of enterprises have shadow AI usage according to recent surveys. You probably do too.
What to do about it:
- Acknowledge it exists (pretending otherwise doesn’t help)
- Provide sanctioned alternatives
- Establish clear policies on what data can/cannot go to external services
- Monitor for data leakage
Infrastructure implication: None directly. But shadow AI often precedes formal adoption, and understanding usage patterns informs architecture decisions.
Pattern B: Experimentation
Formal POCs and pilots testing AI feasibility. Small teams, bleeding-edge models, novel architectures. Goal is learning, not production.
Characteristics:
- Time-boxed (3-6 months)
- Limited data exposure (synthetic or anonymized)
- High failure rate (expected and acceptable)
- Success measured by learning, not ROI
Infrastructure implication: Cloud or VPC-isolated is usually fine. Don’t over-engineer infrastructure for experiments. If the POC succeeds, you’ll rebuild anyway.
Pattern C: Artisan AI (Self-Hosted Open Models)
Enterprise-controlled deployment of open-source models (Llama, Mistral, Phi) on self-hosted infrastructure. Emphasis on data sovereignty and model customization.
Characteristics:
- Open models (Llama 3.3, Mistral, Phi-4)
- Self-hosted inference (vLLM, TGI)
- Full control over data flows
- Fine-tuning for domain-specific tasks
- “Deterministic spine” - business logic controls AI, not vice versa
Infrastructure implication: Requires on-premise, hybrid, or VPC-isolated. Cannot use external APIs for core inference.
This is where most regulated enterprises should aim. Control over models, control over data, control over behavior.
Pattern D: Augmented SaaS
AI features integrated into existing enterprise platforms. Salesforce Einstein, Microsoft Copilot, ServiceNow AI. Team-wide adoption through familiar interfaces.
Characteristics:
- AI embedded in tools employees already use
- Vendor manages model infrastructure
- Limited customization
- Fast deployment
- Vendor lock-in risk
Infrastructure implication: Vendor’s infrastructure. Your data policies must align with vendor’s data handling. Review BAAs, data processing agreements, and regional deployment options.
Pattern E: API-Integrated Production
Cloud-hosted models (OpenAI, Anthropic, Google) integrated via APIs with custom application frameworks. RAG for knowledge grounding. Guardrails for output control.
Characteristics:
- API calls to external model providers
- Custom application logic
- RAG for domain knowledge
- Content filtering and guardrails
- Variable costs based on usage
Infrastructure implication: Your application infrastructure + vendor API. Data flows to external providers for inference. Acceptable for non-sensitive data; problematic for PII/PHI.
Mapping Adoption to Infrastructure
| Adoption Pattern | Air-Gapped | Hybrid | VPC-Isolated | Edge | Multi-Region |
|---|---|---|---|---|---|
| Shadow AI | N/A | N/A | N/A | N/A | N/A |
| Experimentation | Overkill | Good | Best | Overkill | Overkill |
| Artisan AI | Possible | Best | Good | Good | Good |
| Augmented SaaS | N/A | Possible | Good | N/A | Possible |
| API-Integrated | N/A | Possible | Good | N/A | Possible |
Key insight: Artisan AI (self-hosted open models) is the only adoption pattern that works with all infrastructure patterns. If you need air-gapped or edge deployment, artisan is your only option.
Part 3: Use Case Architectures
What AI systems actually do determines architecture requirements. Five core patterns:
Architecture 1: Retrieval-Augmented Generation (RAG)
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Query │────▶│ Embedding │────▶│ Vector DB │
│ │ │ Model │ │ Retrieval │
└─────────────┘ └─────────────┘ └──────┬──────┘
│
▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Response │◀────│ LLM │◀────│ Context + │
│ │ │ Generation │ │ Query │
└─────────────┘ └─────────────┘ └─────────────┘
Query against knowledge base. Retrieve relevant documents. Generate response grounded in retrieved context.
Use cases: Customer support Q&A, internal knowledge search, documentation chat, compliance lookup.
Infrastructure requirements:
- Embedding model (BGE-M3, nomic-embed-text)
- Vector database (Qdrant, pgvector, Milvus)
- Generation model (Mistral 7B sufficient for most RAG)
- Low latency requirement for interactive use
Data sensitivity: High. Knowledge base often contains sensitive internal information. RAG should typically run on artisan/self-hosted infrastructure.
Architecture 2: Classification and Routing
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Input │────▶│ Small │────▶│ Structured │
│ (Ticket) │ │ LLM │ │ Output │
└─────────────┘ └─────────────┘ └─────────────┘
│
▼
{department: "billing",
priority: "high",
intent: "complaint"}
Classify input into predefined categories. Route to appropriate handler. Constrained output space.
Use cases: Ticket routing, document classification, intent detection, sentiment analysis.
Infrastructure requirements:
- Small model sufficient (Phi-3-mini, Mistral 7B)
- Low latency critical for real-time routing
- High throughput for volume processing
Data sensitivity: Moderate to high. Classification often processes PII. On-premise or hybrid typically required.
Architecture 3: Generation and Drafting
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Input │────▶│ Larger │────▶│ Draft │
│ Context │ │ LLM │ │ Output │
└─────────────┘ └─────────────┘ └─────────────┘
│
▼
Human review
before sending
Generate draft content for human review. Responses, summaries, reports, code.
Use cases: Email drafting, report generation, code completion, content creation.
Infrastructure requirements:
- Larger models for quality (Llama 3.3 70B, Mistral Large)
- Human-in-the-loop workflow
- Version control for drafts
Data sensitivity: Varies by content. Customer-facing drafts containing PII need on-premise. Internal drafts may tolerate cloud.
Architecture 4: Single-Agent Workflows
┌─────────────┐ ┌─────────────────────────────────────┐
│ Goal │────▶│ AGENT │
│ │ │ ┌─────────┐ ┌─────────┐ │
└─────────────┘ │ │ Plan │ │ Execute │ │
│ │ │──▶│ │──┐ │
│ └─────────┘ └─────────┘ │ │
│ ▲ │ │
│ └─────────────────────┘ │
│ Iterate │
│ ┌──────────────────────────────┐ │
│ │ Tools: Search, Calculate, API │ │
│ └──────────────────────────────┘ │
└─────────────────────────────────────┘
Autonomous agent with tool access. Plans, executes, iterates. Bounded autonomy with approval gates for high-risk actions.
Use cases: Research tasks, data analysis, workflow automation, investigation.
Infrastructure requirements:
- Larger models for reasoning (Llama 3.3 70B+)
- Tool integration layer (function calling)
- Audit logging for all actions
- Approval workflow for sensitive actions
Data sensitivity: High. Agents access and act on enterprise data. Requires artisan infrastructure with strong governance.
Architecture 5: Multi-Agent Orchestration
┌─────────────────────────────────────────────────────────────┐
│ ORCHESTRATOR │
│ ┌───────────────────────────────────────────────────────┐ │
│ │ Task decomposition │ Agent selection │ Aggregation │ │
│ └───────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
│ │ │
┌─────────┘ │ └─────────┐
▼ ▼ ▼
┌───────────┐ ┌───────────┐ ┌───────────┐
│ RESEARCH │ │ ANALYSIS │ │ WRITING │
│ AGENT │ │ AGENT │ │ AGENT │
│ ┌───────┐ │ │ ┌───────┐ │ │ ┌───────┐ │
│ │Search │ │ │ │Compute│ │ │ │Generate│ │
│ │Tools │ │ │ │Tools │ │ │ │Tools │ │
│ └───────┘ │ │ └───────┘ │ │ └───────┘ │
└───────────┘ └───────────┘ └───────────┘
Multiple specialized agents coordinated by orchestrator. Each agent has specific capabilities. Complex tasks decomposed and distributed.
Use cases: Complex research, multi-step workflows, enterprise process automation.
Infrastructure requirements:
- Multiple model instances (potentially different models per agent)
- Orchestration layer (LangGraph, CrewAI, custom)
- Shared memory/context management
- Comprehensive audit trail
- Graduated authority levels
Data sensitivity: Very high. Multi-agent systems access broad enterprise data. Requires strongest governance controls. Artisan infrastructure strongly recommended.
Use Case to Infrastructure Mapping
| Use Case | Minimum Infrastructure | Recommended for Regulated |
|---|---|---|
| RAG | VPC-Isolated | Hybrid or On-Premise |
| Classification | VPC-Isolated | Hybrid or On-Premise |
| Generation | VPC-Isolated | Hybrid |
| Single Agent | Hybrid | On-Premise |
| Multi-Agent | Hybrid | On-Premise |
Compliance Requirements Mapping
| Regulation | Air-Gapped | Hybrid | VPC-Isolated | Edge | Multi-Region |
|---|---|---|---|---|---|
| HIPAA (PHI) | Best | Good | Possible | Best | Good |
| PCI-DSS | Good | Possible | Best | Possible | Good |
| SOX | Good | Good | Good | Possible | Good |
| GDPR | Good | Good | Possible* | Good | Best |
| NIST 800-171 | Best | Possible | Possible | Good | Possible |
| FedRAMP | N/A | Possible | Best | N/A | Good |
| PIPL (China) | Best | Possible | Possible | Good | Best |
| DORA (EU Finance) | Good | Best | Possible | Possible | Best |
*VPC-Isolated with US cloud providers has CLOUD Act exposure even in EU regions.
Platform Comparison
For teams evaluating deployment platforms, here’s how major options compare:
| Platform | Deployment Options | Models Supported | Strengths | Limitations |
|---|---|---|---|---|
| vLLM | Self-hosted (any) | Open models | High throughput, production-ready | Requires ML ops expertise |
| TGI (HuggingFace) | Self-hosted, cloud | Open models | Good docs, enterprise support | Slightly lower throughput than vLLM |
| Ollama | Self-hosted (any) | Open models | Simple setup, great for dev | Limited production scaling |
| NVIDIA NIM | On-premise, cloud | NVIDIA optimized | Best GPU utilization | NVIDIA ecosystem lock-in |
| Red Hat OpenShift AI | On-premise, hybrid | Open models | Enterprise Kubernetes | Complex setup, Red Hat ecosystem |
| Ray Serve | Any | Any | Distributed scaling | Requires Ray expertise |
| Prem Studio | Self-hosted, managed | Any open model | Turnkey deployment, Swiss option | Managed component |
For regulated industries wanting turnkey deployment:
Prem Studio handles infrastructure complexity while maintaining data sovereignty:
- Deploy Llama, Mistral, Phi on your infrastructure
- Autonomous fine-tuning from seed examples
- Swiss jurisdiction for managed option (GDPR-compatible, outside US CLOUD Act)
- SOC 2, GDPR, HIPAA compliance documentation included
Book a technical call to discuss your requirements.
Cost Analysis
| Pattern | Infrastructure/Year | Ops Team | Time to Deploy |
|---|---|---|---|
| Air-Gapped | $200K-500K | 3-5 FTEs | 3-6 months |
| Hybrid | $50K-150K | 2-3 FTEs | 1-3 months |
| VPC-Isolated | $30K-100K | 1-2 FTEs | 2-4 weeks |
| Edge (per node) | $10K-50K | 0.5 FTE/10 nodes | 2-4 months |
| Multi-Region | $300K-1M | 4-6 FTEs | 4-6 months |
Build vs Buy calculation:
Building requires 2-4 FTEs (ML infra, DevOps, security) at $150K-250K each = $300K-1M/year in people alone, plus infrastructure.
Managed solutions typically cost $100K-300K/year for equivalent capability.
Break-even depends on team’s existing capabilities and long-term infrastructure strategy.
Decision Framework
Step 1: Map your compliance requirements
List all regulations that apply: HIPAA, PCI-DSS, GDPR, NIST, FedRAMP, industry-specific. Use the compliance mapping table to identify acceptable infrastructure patterns.
Step 2: Identify your adoption pattern
Where is your organization? Shadow AI, experimentation, artisan, augmented SaaS, or API-integrated? This determines what infrastructure you actually need today vs. what you’re planning for.
Step 3: Define your use cases
RAG, classification, generation, single-agent, multi-agent? More autonomous use cases require stronger infrastructure controls.
Step 4: Match the three layers
Find the intersection that satisfies all three:
- Infrastructure pattern that meets compliance
- Adoption pattern that matches organizational maturity
- Use case architecture that delivers business value
Step 5: Build or partner
Do you have ML platform engineering capability? If yes, build. If no, partner with managed solutions for appropriate components.
Implementation Checklist
Universal requirements (all patterns):
- Data classification policy documented
- Access control matrix defined (RBAC)
- Audit logging enabled for all AI interactions
- Model versioning and rollback capability
- Incident response playbook for AI failures
- Cost monitoring and alerting
- Performance SLOs defined
Air-gapped specific:
- Physical media workflow for model updates
- Staged environment for testing before production
- Local model registry (Harbor, Artifactory)
- Offline documentation and runbooks
Hybrid specific:
- VPN or Private Link configured
- Data classification enforcement (DLP)
- Clear boundary definition (what goes where)
- Cross-environment monitoring
Edge specific:
- Fleet management tooling
- Centralized configuration management
- Offline operation testing
- Update coordination across nodes
FAQs
Q: Which pattern should I start with if I’m new to enterprise AI?
VPC-isolated for experimentation. Evolve to hybrid or artisan as you move to production with sensitive data.
Q: Do I really need air-gapped for HIPAA?
Not necessarily. HIPAA requires appropriate safeguards but doesn’t mandate air-gapped. Hybrid or VPC-isolated with proper BAAs often suffices. Consult your compliance team.
Q: What’s the difference between data residency and data sovereignty?
Residency: where data is physically stored. Sovereignty: what legal jurisdiction governs access. US cloud providers offer EU residency but US sovereignty (CLOUD Act applies).
Q: How do I handle the “shadow AI” problem?
Acknowledge it exists. Provide sanctioned alternatives. Establish clear data policies. Monitor for violations. Prohibition doesn’t work; channeling does.
Q: Is Artisan AI (self-hosted open models) really production-ready?
Yes. Llama 3.3 70B, Mistral, and Phi-4 match or exceed GPT-4 on many benchmarks. vLLM and TGI are production-grade inference servers. The tooling has matured significantly.
Q: What’s the minimum viable team for self-hosted AI?
1 ML engineer + 1 DevOps engineer for VPC-isolated or hybrid. Add 1-2 more for air-gapped or multi-region. This assumes existing infrastructure skills in the organization.
Q: How do I evaluate whether my team can handle air-gapped?
Questions: Have you operated air-gapped systems before? Do you have physical security infrastructure? Do you have GPU expertise? If mostly no, consider hybrid or managed alternatives.
Q: When does multi-agent make sense?
When you have complex workflows requiring multiple specialized capabilities AND strong governance infrastructure. Most enterprises aren’t ready. Start with RAG and single-agent, evolve carefully.
Q: How do I justify enterprise AI infrastructure investment?
Frame around risk reduction, not just capability. What’s the cost of a data breach? What’s the regulatory penalty risk? What’s the reputational impact? Compare to infrastructure investment.
Q: Can I migrate between patterns later?
Yes, with planning. VPC-isolated → Hybrid is straightforward. Hybrid → Air-gapped is harder. Design for portability: containerize, use abstraction layers, avoid vendor lock-in where possible.
Top comments (0)