- HIPAA
- GDPR Data Residency
- SOC 2 Type II
- Air-Gapped Support
- ISO 27001
Full Data Sovereignty. Zero Compromises
Deploy the complete AI Hive platform entirely within your own infrastructure. Every agent execution, model inference call, data access, and audit log remains within your perimeter. No traffic to AI Hive after deployment. Complete data sovereignty — enforced by architecture, not by policy.
Complete data isolation
zero traffic to AI Hive's infrastructure from the moment deployment completes
Local LLM inference
run Llama 3, Mistral, or Deepseek R1 on your own GPU hardware — no per-token API cost, no external data flows for model calls
Air-gapped support
fully offline deployment for classified environments with physical media update delivery
Custom update cadence
you control exactly when and how platform updates are applied — no forced upgrades
SIEM integration
all platform and agent logs forwarded to your own SIEM in real time
Your encryption keys
all data encrypted with keys you own and manage (BYOK)
When the Cloud Is Not an Option
For heavily regulated industries and security-sensitive enterprises, sending data — even encrypted — to a third-party cloud is not commercially, legally, or ethically acceptable. The risk is not technical; it is contractual, regulatory, and reputational. AI Hive’s on-premise deployment delivers every SaaS feature running entirely on your servers, under your control, auditable without any AI Hive dependency.
Three Deployment Architectures
Private Cloud
deploy inside your own VPC (AWS, Azure, or GCP). Customer-owned compute and storage. AI Hive manages the application lay
On-Premise Data Centre
full deployment on your own physical or virtualised hardware. Containerised via Kubernetes. Zero internet egress for age
Air-Gapped
fully isolated, zero internet connectivity. Local LLM inference using Llama 3 or Mistral. Updates via physical media. Be
Local LLM Inference: No External API Calls Required
For organisations where even the model API call must stay within the infrastructure perimeter, AI Hive supports local LLM inference on your own GPU hardware. Full feature parity with cloud LLM usage. No per-token API cost. No external data flows — ever.
Llama 3
8B for speed-critical use cases, 70B for balanced performance, 405B for maximum capability — all deployable on NVIDIA A100/H100 or equivalent GPU hardware
Mistral
7B and 8x7B MoE variants — optimal for organisations with constrained GPU budgets requiring solid multi-task performance
Deepseek R1
strong reasoning performance for complex workflow orchestration and code generation use cases
BYOM
any model exposing an OpenAI-compatible API endpoint integrates directly — includes fine-tuned and proprietary models
Baseline Infrastructure Requirements
Baseline requirements for a mid-size enterprise deployment supporting up to 100 active agents. Right-sized for your specific deployment during the free assessment phase with our solutions engineers.
Compute
Kubernetes 1.24+ cluster, minimum 3 nodes at 16 vCPUs/64 GB RAM each. GPU nodes with minimum 80 GB VRAM per Llama 3 70B instance for local LLM. Horizontal scaling supported
Storage
500 GB SSD per node for application data, 1 TB+ for vector database (RAG knowledge engine), object storage for documents and audit logs. NFS and cloud object storage both supported
Networking
internal DNS, TLS/SSL certificate management (internal CA supported), load balancer (HAProxy or cloud-native), NTP synchronisation, firewall rules for service mesh
Security
Kubernetes RBAC and namespace isolation, HashiCorp Vault or Kubernetes Secrets for key management, network policies for pod-to-pod communication, log aggregation to SIEM
Typical Deployment Timeline: 4 to 14 Weeks
Assessment
infrastructure review, architecture design, security requirements, integration planning. Detailed technical specification before any infrastructure work begins
Infrastructure Build
Kubernetes cluster provisioning, AI Hive application deployment, local LLM setup (if required), SIEM integration, security hardening per your policy baseline
Deploy and Test
end-to-end testing in your infrastructure, agent deployment, integration testing against live systems, security testing, UAT with your team
Handover
production go-live with dedicated engineer on call for 2 weeks, full documentation package, team trained on update management and horizontal scaling
Zero Back-Doors. Independently Verifiable
On-premise deployments contain zero telemetry, zero phone-home mechanisms, and zero remote access capabilities. Your security team can verify this through network traffic inspection and code review of deployment packages before go-live. All deployment packages are signed and include full integrity verification checksums.
Implementation Questions Answered
What makes AI Hive different from other AI agent platforms?
Three things no competitor offers together:
(1) a fully modular, enterprise-grade multi-agent orchestration platform,
(2) transparent, accessible pricing that doesn’t require a $300K/year commitment, and
(3) AI Engineers for Hire — a dedicated team of AI architects and engineers who build and deploy your agents alongside you. You get the platform and the people. Most vendors give you only one.
What makes AI Hive different from other AI agent platforms?
Most clients have their first production AI agent live within days of onboarding. Our pre-built industry agent packs, Agent Studio’s no-code workflow builder, and hands-on AI engineering team eliminate the 6–18 month implementation timelines typical of legacy platforms.
What makes AI Hive different from other AI agent platforms?
Most clients have their first production AI agent live within days of onboarding. Our pre-built industry agent packs, Agent Studio’s no-code workflow builder, and hands-on AI engineering team eliminate the 6–18 month implementation timelines typical of legacy platforms.
Is AI Hive model-agnostic?
Completely. AI Hive connects to all leading commercial LLMs — OpenAI, Gemini, Grok — and open-source models including DeepSeek, Kimi, and Qwen. You are never locked into a single model vendor. As LLM capabilities evolve, your AI Hive platform evolves with them.
Do I need an internal AI team to use AI Hive?
No. That’s the point of AI Engineers for Hire. AI Hive gives you a dedicated team of specialists who design, build, deploy, and optimize your agents. Your operations, marketing, or IT team sets the business requirements — we handle the AI execution. No internal AI headcount required.
Ready to Deploy Within Your Own Infrastructure?
Free 30-minute discovery call to understand your architecture, security requirements, and compliance obligations — before any commercial discussion.