TELOSIS-RP-2026-001InfrastructureJune 2026
The decision to self-host core infrastructure is frequently treated as an optional capability, a box to check during procurement or a bullet point on a feature comparison sheet. This framing is fundamentally mistaken. Self-hosting is not a feature that can be bolted onto an otherwise dependent stack; it is a foundational architectural commitment that shapes every downstream decision, from data governance to operational resilience. This paper examines three model organizations that migrated from managed services to self-hosted infrastructure, documenting the full cost profile, the decision frameworks that guided the transition, and the operational patterns that emerged post-migration. We find that self-hosting reduces long-term operational risk only when treated as infrastructure: budgeted, staffed, and governed with the same rigor applied to networking or physical security. Teams that treat self-hosting as a feature, deployed opportunistically without sustained investment, experience higher failure rates and worse cost outcomes than those that remained on managed services.
TELOSIS-RP-2026-002Privacy & SecurityMay 2026
Modern infrastructure systems routinely collect, process, and store vast quantities of user data, often exceeding what is necessary for the services they provide. Privacy-preserving architecture offers a structural alternative: systems designed from the outset to minimize data exposure, enforce purpose limitation through technical controls, and provide verifiable guarantees about data handling. This paper presents a framework for evaluating infrastructure architectures against privacy principles, drawn from three production systems that achieved measurable reductions in data exposure without sacrificing operational performance. We identify five architectural patterns that consistently reduce privacy risk: data minimization at the collection layer, purpose-bound storage, cryptographic access control, ephemeral processing pipelines, and auditable data flows. Each pattern is analyzed for its implementation complexity, operational cost, and the degree of privacy guarantee it provides.
TELOSIS-RP-2026-003Engineering PracticeApril 2026
Long-running computational tasks, ranging from batch data processing to multi-day simulations, present operational challenges that differ fundamentally from request-response workloads. They cannot be served by standard autoscaling groups, they do not fit neatly into container orchestration patterns designed for short-lived processes, and they demand different approaches to fault tolerance, resource allocation, and cost management. This paper examines three distributed execution models that have proven effective for long-running workloads: work queue architectures, actor-based processing, and stateful stream processing. For each model, we document the operational characteristics, the failure modes, and the conditions under which the model outperforms alternatives. We find that the choice of execution model should be driven by task characteristics, namely statefulness, idempotency, and duration, rather than by organizational familiarity or available tooling.