Self-Hosted AI
Private Agents on Your Server

Self-hosted AI means running AI models and agents on infrastructure you own and control — your own server, VPS, Mac Mini, or dedicated hardware — rather than relying on a cloud provider's shared infrastructure. Your data, conversations, files, and AI interactions never leave your machine. You have complete control over which AI models run, what data they process, who can access the system, and where that data is stored. This is fundamentally different from using ChatGPT, Claude.ai, or other cloud AI services where your data is processed on the provider's servers. With self-hosted AI, the entire inference pipeline runs within your controlled environment. We configure the deployment with the same security standards we use for enterprise clients: SSH key-only authentication, encrypted storage, firewall rules, and intrusion prevention.

Yes, with proper configuration — and configuration is where most DIY setups fail. When using local models via Ollama, your data never leaves your server. Every prompt, every response, every document you process stays entirely on your hardware with zero external network requests. When using BYOK (Bring Your Own Key) with cloud models like Claude or GPT, your data goes directly to the AI provider via your own API key, bypassing any third-party intermediary or proxy service. We configure AES-256 encryption at rest for all stored data and TLS 1.3 encryption in transit for all API communications. For maximum security, we offer air-gapped deployments where the server has zero internet connectivity after initial setup — making data exfiltration physically impossible. This level of privacy is not available from any cloud AI service.

Self-hosted AI is one of the strongest approaches to GDPR compliance because you maintain complete data sovereignty — your data stays within your jurisdiction, on infrastructure you control. We configure the full compliance stack: data processing agreements documenting how AI processes personal data, configurable retention policies that automatically purge data after defined periods, right-to-erasure workflows that honor deletion requests across all stored contexts and conversation histories, and data portability exports in standard formats. With fully local models via Ollama, personal data never crosses any network boundary, which simplifies your Data Protection Impact Assessment (DPIA) significantly. We provide compliance documentation templates aligned with Articles 28, 30, and 35 of the GDPR, and can work directly with your DPO (Data Protection Officer) to satisfy audit requirements. For organizations also subject to HIPAA, see our enterprise deployment option.

Hardware requirements depend entirely on your use case and which deployment model you choose. For BYOK setups (using cloud APIs with your own keys), any VPS from $4/month works — the AI models run at the provider's infrastructure, your server just handles orchestration, memory, and MCP integrations. For running local LLMs via Ollama: small models (7B parameters like Phi-4) need 8GB RAM and run well on a Raspberry Pi 5; medium models (70B like Llama 3.3) need 40GB+ unified memory (Mac Mini M4 Pro) or a GPU with 48GB VRAM; and large models (400B+ like DeepSeek V3) need multi-GPU setups or dedicated server hardware. A Mac Mini M4 with 16GB unified memory handles most business use cases smoothly — it runs 14B models with fast inference, stays silent, and uses under 30W of power. See our hardware options table above for detailed pricing and specifications.

Ollama is an open-source tool that makes it easy to download, manage, and run large language models locally on your own hardware. It handles model downloading from the Ollama registry, quantization (compression to fit models on smaller hardware using formats like Q4_K_M and Q8_0), GPU acceleration via Metal (macOS) or CUDA (NVIDIA), and provides a simple REST API that any application can connect to. We install Ollama on your server, select the best models for your hardware and use case (Llama 3.3 for general reasoning, Mistral for multilingual tasks, Qwen for coding, DeepSeek for cost-efficient inference, Phi-4 for constrained hardware), optimize quantization and batch settings for your specific CPU/GPU/memory configuration, and connect it to your AI agent so you can interact through Telegram, Discord, or a web interface. The experience feels like using ChatGPT, but the entire system runs privately on your hardware with zero API costs. For teams that also need workflow automation, Ollama integrates directly with n8n AI workflow nodes.

Yes — this is our recommended hybrid approach. We configure intelligent model routing: simple tasks (summarization, Q&A, formatting) go to fast local models with zero API cost, while complex tasks (advanced reasoning, long-context analysis, creative writing) route to cloud models like Claude or GPT via your own API keys. You get the best of both worlds: privacy and cost savings for everyday tasks, frontier-model power when you need it.

An air-gapped deployment means the AI system has zero internet connectivity after initial setup. All models are downloaded and installed during setup, then the server is disconnected from the network (or placed on an isolated network). This is the highest level of data security — nothing can leave the machine, even if the software were compromised. We support fully air-gapped deployments using Ollama with pre-downloaded models for organizations in defense, government, healthcare, and finance.

BYOK means you use your own API keys from AI providers like Anthropic (Claude), OpenAI (GPT), or Google (Gemini). You create an account directly with the provider, generate an API key, and we configure your self-hosted agent to use that key. Your usage goes directly to the provider — no intermediary, no markup, no data routing through third parties. The API keys are stored encrypted on your server and never shared with us.

Self-hosted AI typically costs 50-90% less than cloud AI services over 12 months. A VPS for BYOK costs $4-30/month plus API usage ($1-150/month depending on volume). Running fully local models via Ollama on your own hardware costs $0/month in API fees after the initial hardware purchase. Compare this to managed AI services at $20-500/month per user. The one-time setup fee pays for itself within 2-4 months for most organizations.

For most business tasks, yes. Local models like Llama 3.3 70B, Mistral Large, and Qwen 2.5 72B perform at or near frontier model levels for coding, analysis, writing, and reasoning tasks. For the absolute cutting edge (multi-step scientific reasoning, very long context), cloud models like Claude Opus or GPT-5 still have an edge — which is why we recommend hybrid setups. The gap narrows with every model release.

For BYOK cloud models, updates are automatic — when Anthropic releases a new Claude model, your agent uses it immediately. For local models, we provide a managed update service: we test new model releases on similar hardware, verify compatibility with your agent configuration, and deploy updates during scheduled maintenance windows. Air-gapped environments receive updates via secure transfer (encrypted USB or isolated network sync).

Yes — the Mac Mini is one of our most recommended platforms for self-hosted AI. The M4 chip with 16GB unified memory runs Llama 3.3 7B-14B models smoothly, and the M4 Pro/Max with 36-128GB handles 70B+ models. Apple Silicon's unified memory architecture is ideal for LLM inference because the memory is shared between CPU and GPU. Silent operation, low power consumption (under 30W), and macOS stability make it perfect for always-on private AI.

Yes, with limitations. The Raspberry Pi 5 with 8GB RAM can run small models (Phi-4 3.8B, Gemma 2B, TinyLlama) for basic Q&A, summarization, and simple coding tasks. Response times are slower than cloud AI (5-30 seconds per response), but it works 24/7 silently with minimal power consumption (under 5W). We configure Ollama with optimized quantization (Q4_K_M) for the best quality-to-speed ratio on ARM hardware.

Absolutely. Our enterprise deployment includes multi-user access control with role-based permissions (RBAC), so different team members get different access levels. Audit logging tracks all AI interactions per user. Shared knowledge bases let the AI learn from organizational context. We support 5-100+ concurrent users depending on your hardware, with load balancing and queue management for high-traffic deployments.

Self-hosted AI with local models is inherently HIPAA-friendly because Protected Health Information (PHI) never leaves your controlled environment. We configure BAA-compatible infrastructure: encrypted storage, access controls, audit trails, automatic session expiration, and data retention policies. For organizations that need cloud model access, we configure BYOK with HIPAA-eligible providers (Anthropic offers a BAA for Claude API). Full compliance documentation provided.

Personal self-hosted AI setup takes 2-4 hours including hardware setup, model deployment, messaging integrations, and security hardening. Enterprise deployments take 1-3 days depending on compliance requirements, number of users, air-gap configuration, and integration complexity. Both include thorough testing and documentation. Nothing is handed off until everything works end-to-end.

We configure fallback routing: if a local model produces low-confidence output (detected via response quality scoring), the request automatically routes to a stronger cloud model via your API key. You can also set per-task rules — for example, always use local models for summarization but route complex analysis to Claude. This hybrid approach eliminates the quality gap while maintaining privacy for routine tasks.

Yes — we design all deployments for flexibility. Starting with BYOK (cloud APIs with your own keys) and migrating to fully local models is a common path. Your conversation history, agent configurations, custom skills, and MCP server connections all transfer seamlessly. We handle the migration, model selection, and performance validation. Many clients start hybrid and go fully local as open-source models improve.