Every infrastructure decision trades convenience for control. The pattern recurs across cloud, data centers, inference, and enterprise software. - **Convenience side:** Multi-tenant SaaS. Fast to deploy, low operational burden, amortized costs. The tradeoff: [[Noisy Neighbour Problem]], rate limits, unpredictable latency, limited configurability, compliance gaps. You're renting someone else's defaults. - **Control side:** Self-hosted, on-prem, customer-managed VPC. Full ownership of performance, security, and configuration. The tradeoff: brutal operational overhead. You need teams to provision hardware, manage upgrades, handle scaling, maintain availability. Capital and talent intensive, especially at scale. ![[Screenshot 2026-02-14 at 00.09.34.png]] The market keeps trying to collapse this tradeoff. The emerging pattern: **managed isolation.** Logically isolated infrastructure, operated by the vendor. > You own the control plane (agent logic, data, workflows). They own the data plane (model serving, GPU provisioning, scaling). Dedicated resources, no sharing, but someone else does the undifferentiated heavy lifting. ![[Screenshot 2026-02-14 at 00.11.31.png]] This is the same architectural split happening across cloud infrastructure broadly. It showed up in databases (managed Postgres vs. self-hosted), in Kubernetes (EKS/GKE vs. bare metal), and now in [[Inference]] (dedicated model vaults vs. shared APIs vs. self-hosted GPU clusters). ![[Screenshot 2026-02-14 at 00.14.18.png]] ![[Screenshot 2026-02-14 at 00.15.13.png]] The pattern suggests a structural insight: as any infrastructure layer matures, a "managed dedicated" tier emerges between shared SaaS and self-hosted. The enterprises that need both compliance and scale gravititate there. The vendor that nails this tier captures the highest-value customers. ![[Screenshot 2026-02-05 at 00.23.03.png]] For [[AI Inference Infrastructure]], the implication is clear. Reserved compute and inference APIs both survive, but the biggest enterprise contracts go to whoever offers dedicated, isolated, elastically scaled inference without forcing the customer to operate GPUs. --- #firstprinciple #systems Related: [[Noisy Neighbour Problem]] | [[multi-tenancy]] | [[Inference]] | [[AI Inference Infrastructure]] | [[First Principles and Mental Models MoC]] | [[Data Centre First Principles]]