Illustrative sample: This project page demonstrates the portfolio format. It is a hypothetical case study, not a claim that the platform was built, benchmarked, secured, or operated in production.
Problem statement
AI experiments often begin as isolated scripts with unclear data handling, irreproducible environments, and no shared operational view. The hypothetical project asks how a small self-hosted environment could support repeatable model and retrieval experiments while keeping private data within a controlled trust boundary.
Project objectives
- Provide a consistent path from a versioned model artifact to an internal inference endpoint.
- Separate identity, application, model-serving, storage, and observability responsibilities.
- Capture enough telemetry to diagnose resource, model, and application issues.
- Make destructive changes and upgrades recoverable.
- Document tradeoffs rather than present a homelab as production-equivalent.
Architecture overview
The proposed design places an authenticated gateway in front of separate inference and retrieval services. Model artifacts and source documents live in versioned object storage. A Kubernetes control plane schedules workloads, while telemetry and backups remain distinct platform capabilities.
See Self-Hosted AI Platform Architecture for component boundaries, request flow, and quality attributes.
Technology stack
| Capability | Candidate technology | Reason to evaluate |
|---|---|---|
| Workload orchestration | Kubernetes | Declarative scheduling and isolation primitives |
| Artifact storage | S3-compatible object storage | Versioned, API-driven storage boundary |
| Workload identity | SPIFFE/SPIRE | Short-lived, attestable service identities |
| Observability | OpenTelemetry-compatible pipeline | Portable traces, metrics, and logs |
| Delivery | Git-based declarative workflow | Reviewable and repeatable configuration changes |
Candidates are not final selections. Compatibility, resource demand, upgrade behavior, and recovery would need to be tested before adoption.
Implementation details
The project could be delivered as evidence-producing increments:
- Establish network, storage, backup, and hardware inventory baselines.
- Bootstrap the cluster and a minimal declarative delivery workflow.
- Deploy one versioned model behind an internal-only inference API.
- Add telemetry and record a baseline under a representative test workload.
- Add retrieval with a non-sensitive sample data set and explicit access rules.
- Introduce workload identity following the SPIFFE/SPIRE guide.
- Exercise restore, node loss, identity revocation, and rollback procedures.
Major architectural decisions
Remote administration is tracked in ADR-0001: Selecting a Remote Access Solution. Additional decisions would be needed for the orchestrator, storage protection, artifact approval, accelerator scheduling, and backup objectives.
Challenges and lessons to validate
Because this project is hypothetical, these are anticipated questions rather than claimed lessons learned:
- Does limited accelerator memory make model selection or concurrency the dominant constraint?
- Can the platform be upgraded and restored without undocumented manual state?
- Is identity policy understandable enough to review during an incident?
- Does telemetry help distinguish slow retrieval, model saturation, and client errors without collecting sensitive prompt content?
- Which managed-service conveniences create the greatest self-hosting burden?
Repository and evidence
No GitHub repository is linked because this is a structural example. If the project is implemented, this section should link the source and add reproducible configuration, test results, diagrams, operational runbooks, and dated evidence for any performance or reliability claims.