What Self-Hosted AI Means
Self-Hosted AI refers to artificial intelligence systems that are deployed inside infrastructure fully controlled by the customer.
Inference runs within environments such as private cloud (VPC), on-premise data centres, and restricted or air-gapped networks.
No model execution depends on third-party inference endpoints.
Operational control remains with the organisation deploying the system.
Why Organisations Choose Self-Hosted AI
Many organisations cannot rely on cloud-hosted AI services due to regulatory obligations, data sensitivity, IP protection requirements, and security and audit constraints.
Self-Hosted AI allows teams to benefit from advanced AI capabilities without transferring control of inference, data, or behaviour to external platforms.
This is particularly important where contractual assurances are insufficient substitutes for technical control.
Security Properties of Self-Hosted AI
Deploying AI inside customer-controlled infrastructure changes the security posture fundamentally.
Full control over data access and retention
Clear audit boundaries
Isolation from third-party telemetry
Deterministic operational behaviour
Security policies are enforced by architecture rather than vendor promises.
Deployment Environments
Ava Technologies supports self-hosted AI deployments across a range of environments:
Virtual Private Clouds
On-premise servers
Hybrid private infrastructure
Air-gapped or restricted networks
Deployment is adapted to existing security, compliance, and operational constraints.
Self-Hosted AI vs Cloud AI
The distinction is not feature-based — it is control-based.
Self-Hosted AI
Inference runs inside customer infrastructure
Access, logging, and retention are customer-defined
No external telemetry or data exhaust
Behaviour governed by internal policy and controls
Cloud AI Platforms
Inference runs on vendor-controlled servers
Prompts and outputs leave the organisation
Logging and retention are opaque
Operational changes are vendor-driven
Encryption and compliance frameworks may reduce risk but do not eliminate platform dependency.
The Role of Small Models in Self-Hosted AI
Self-Hosted AI is most effective when built on efficient, task-specific models. Smaller models reduce infrastructure overhead, simplify deployment and maintenance, improve predictability, lower attack surface, and enable clearer auditing and constraints.
Rather than deploying monolithic general-purpose systems, Self-Hosted AI focuses on capability-aligned intelligence.
When Self-Hosted AI Is the Right Choice
Self-Hosted AI is appropriate where data cannot leave organisational boundaries, compliance requires full auditability, latency and availability must be predictable, or platform dependency presents unacceptable risk.
Common environments include:
Self-Hosted AI Within a Sovereign Architecture
Self-Hosted AI often operates alongside other deployment models. In practice, on-device AI provides maximum locality and privacy, self-hosted AI provides controlled scale, and optional encrypted compute supports advanced workloads when required.
The defining principle is consistency of control. Infrastructure may vary. Sovereignty does not.
Ava Technologies' Self-Hosted Approach
Ava Technologies designs self-hosted AI systems that prioritise customer-owned infrastructure, explicit inference boundaries, minimal external dependencies, and long-term operational stability.
We work within existing security and compliance frameworks rather than attempting to abstract them away.
Self-hosting is not a workaround — it is a deliberate architectural choice.