Why Self-Hosted AI Matters More Than Ever

The Regulatory Landscape

The regulatory environment around data and AI has shifted dramatically. Three major frameworks are now shaping how European enterprises handle AI workloads, and all of them push in the same direction: more control, more accountability, more sovereignty.

GDPR (General Data Protection Regulation)

GDPR has been in effect since 2018, but enforcement around AI use cases has intensified. The core issue: when you send employee data, customer data, or any personal data to an external AI service, you’re performing a data transfer that needs a legal basis. If that service runs outside the EU, you also need to comply with Chapter V transfer restrictions.

Recent enforcement actions have made it clear that “we use a US-based AI provider” is no longer defensible without comprehensive data processing agreements, transfer impact assessments, and technical safeguards. Self-hosting eliminates the transfer question entirely.

DORA (Digital Operational Resilience Act)

DORA took full effect in January 2025 and applies to all financial entities in the EU — banks, insurance companies, investment firms, and their critical ICT providers. DORA requires:

• ICT risk management — every third-party service must be risk-assessed, including AI providers
• Concentration risk limits — you can’t depend too heavily on a single provider for critical functions
• Exit strategies — you must have a documented plan to migrate away from any critical provider
• Incident reporting — AI-related incidents must be reported to regulators within tight timelines

For financial institutions using cloud AI services for anything beyond experimentation, DORA compliance essentially requires either self-hosting or working with an EU-based provider that meets DORA’s requirements.

NIS2 (Network and Information Security Directive)

NIS2 expanded the scope of EU cybersecurity requirements to cover a much broader set of “essential” and “important” entities — including healthcare, energy, transport, water, digital infrastructure, and public administration. The directive requires these organizations to implement security measures that include supply chain risk management.

Using an external AI service creates a supply chain dependency. Under NIS2, that dependency must be assessed, documented, monitored, and include contractual security guarantees. Self-hosted AI removes this dependency from your supply chain risk profile.

Risks of Cloud-Only AI

Beyond regulatory compliance, cloud-only AI carries operational risks that many organizations underestimate:

• Data exposure — every prompt you send contains information. Over time, the aggregate of your prompts paints a detailed picture of your business operations, strategies, and vulnerabilities
• Model deprecation — cloud providers regularly deprecate models. The model your workflows depend on today might not exist in six months
• Pricing changes — you have zero control over pricing. A 50% price increase on your primary model could blow your AI budget overnight
• Outage dependency — when OpenAI goes down, your AI-powered features go down. You inherit someone else’s reliability problems
• Training data concerns — some providers may use your data to train future models unless you opt out (and verify that opt-out is effective)

None of these risks are theoretical. All of them have happened to real companies in the past 12 months.

What Self-Hosted Means in Practice

Self-hosted AI doesn’t mean running everything on a single server in your basement. It means running AI infrastructure within your control perimeter — whether that’s your own data center, a private cloud VPC, or a dedicated cluster in a managed Kubernetes environment.

A complete self-hosted AI stack includes:

• Language models — open-source models like Llama 3.3, Mistral, or Mixtral running on your GPUs
• Knowledge management — document processing, indexing, and retrieval (this is what StellarBase provides)
• Vector storage — embeddings stored locally for semantic search
• Orchestration — agent frameworks, workflow engines, and routing logic
• Monitoring — usage tracking, cost allocation, quality metrics, and audit logs

The good news: the open-source ecosystem has matured to the point where self-hosted AI is genuinely viable for production workloads. Llama 3.3 70B competes with GPT-4 on many benchmarks. vLLM makes inference fast. And platforms like StellarBase handle the knowledge layer so you don’t have to build it from scratch.

Cloud vs. Self-Hosted: A Comparison

┌─────────────────────┬──────────────────┬──────────────────┐
│                     │   Cloud-Only AI  │  Self-Hosted AI  │
├─────────────────────┼──────────────────┼──────────────────┤
│ Data residency      │ Provider's DC    │ Your DC / VPC    │
│ Data access         │ Provider + you   │ Only you         │
│ Regulatory control  │ Limited          │ Full             │
│ Model selection     │ Provider's list  │ Any model        │
│ Latency             │ Variable         │ Predictable      │
│ Uptime dependency   │ Provider's SLA   │ Your SLA         │
│ Cost at scale       │ Per-token fees   │ Fixed infra cost │
│ Customization       │ API parameters   │ Full fine-tuning │
│ Audit trail         │ Provider's logs  │ Your logs        │
│ Vendor lock-in      │ High             │ None             │
└─────────────────────┴──────────────────┴──────────────────┘

The tradeoff is clear: cloud-only AI is easier to start with but gives you less control. Self-hosted AI requires more upfront investment but gives you full sovereignty over your data, models, and costs.

For most enterprises, the right answer is a hybrid approach — use cloud models for non-sensitive workloads (marketing copy, general research) and self-hosted models for anything involving proprietary or regulated data. StellarBase supports both modes through StellarCloud (cloud) and direct deployment (self-hosted).

StellarBase’s Approach

StellarBase was designed from day one to run on your infrastructure. The entire platform ships as a Docker image that you deploy wherever you need it.

Docker Deployment

# Pull the StellarBase image
docker pull ghcr.io/stellarbase/stellarbase:latest

# Run with local storage and a local LLM
docker run -d 
  --name stellarbase 
  -p 8080:8080 
  -v /data/stellarbase:/app/data 
  -e LLM_PROVIDER=local 
  -e LLM_MODEL=llama-3.3-70b 
  ghcr.io/stellarbase/stellarbase:latest

Kubernetes / Helm

# Add the StellarBase Helm repository
helm repo add stellarbase https://charts.stellarbase.ai

# Install with default configuration
helm install stellarbase stellarbase/stellarbase 
  --namespace stellarbase 
  --create-namespace 
  --set persistence.size=100Gi 
  --set llm.provider=local 
  --set llm.model=llama-3.3-70b

Both deployment methods give you the full StellarBase platform — DSM engine, knowledge graph, agent framework, and API — running entirely within your infrastructure. No external calls, no telemetry, no phone-home.

When paired with a local LLM (Llama, Mistral, or your own fine-tuned model), you get a completely air-gapped AI knowledge platform. No data ever leaves your perimeter. If you need access to commercial models for specific tasks, StellarGate ensures your data is anonymized before it crosses the boundary.

Who Should Self-Host

Self-hosted AI makes the most sense for organizations that meet one or more of these criteria:

• Regulated industries — finance, healthcare, legal, government, defense. If you’re subject to GDPR, DORA, NIS2, HIPAA, or similar frameworks
• Sensitive data — companies handling trade secrets, M&A documents, personnel files, or client-privileged information
• High volume — if you’re spending more than $5,000/month on AI APIs, self-hosting typically breaks even within 3-6 months
• Predictable workloads — if you can forecast your AI usage, fixed infrastructure costs beat variable per-token pricing
• Existing infrastructure — if you already have GPU capacity or Kubernetes clusters, the marginal cost of adding AI workloads is low

If you’re exploring self-hosted AI for your organization, we’d love to help. Visit stellarbase.ai/platform to try the self-hosted deployment or reach out to our team for a guided walkthrough.