Case: Scaling AI Without Losing Data Control
An insurer with three AI features in production and nine requests waiting must decide what domains own, what the platform enforces, and which features to admit first.
-
-
Shared AI Platform for Language Models
A fictional company shares model quotas across teams. Its failures show why capacity, versions, fallback, data handling, and costs need platform ownership.
-
AI Platform Incident Response and Recovery
AI systems can fail with every request returning 200. This guide covers detection signals, containment, provider failover, release rollback, and recovery verification, built around one question: which effects can be undone and which cannot.
-
Controlling AI Cost per Successful Task
A cheaper model call does not mean a cheaper result. This guide defines cost per successful task, models what retries, caching, routing, and human review do to it, and shows a simulation where cost per request falls while cost per success rises.
-
AI Platform Security and Tenant Isolation
On a shared AI platform, tenant identity gets lost between the gateway, the index, the cache, and tool calls. This guide shows where, how to fix each path, and how to test isolation.
-
AI Service Objectives and Reliability Engineering
A healthy dashboard can sit above a model that gives wrong answers. This guide defines latency, availability, and task-success indicators, error budgets, and degradation for AI services.
-
AI Observability and Sensitive Data
AI debugging wants prompts and tool results, which are the most sensitive data a request holds. This guide shows what to record by default, how far redaction can be trusted, and how to control access.
-
Agent Runtime Isolation and Execution Policies
When a model can act, the runtime around it decides what actually happens. This guide covers multi-tenant isolation tiers, short-lived delegated tool credentials, a policy engine, approvals bound to one call, idempotent execution, run state and resume, and a hash-chained action audit, with runnable code.
-
Prompt and Model Promotion with Evaluation Gates
A prompt or model change should reach users only with evidence. This guide builds a release gate service: versioned evaluation sets, non-inferiority thresholds, paired bootstrap intervals, slice checks, judge calibration, approvals, canary stages, and rollback, with runnable simulations.
-
Model Gateway Routing, Quotas, and Failover
A model gateway is the single entry point between applications and model providers. This guide covers caller identity, routing, token-based limits and budgets, provider differences, and failover with audit, with a runnable gateway and fake providers.
-
Model Serving Latency, Batching, and Autoscaling
A model server is a queue in front of an expensive machine. This guide explains how batching, replica scaling, latency targets, and overload policy determine tail latency, with runnable simulations and a benchmarking method that avoids coordinated omission.
-
Shared AI Platform Design and Service Boundaries
A shared AI platform works only when it is clear what the platform promises and what each consuming team still owns. This guide covers consumer types, contracts, ownership, tenancy, and the runtime and evaluation services, with a runnable contract checker.