Engineering writing from the Kimss team — how to govern, isolate, and audit enterprise AI on Azure AI Foundry without rebuilding the plumbing yourself.
Articles
Separate execution from workspace identity, routing, Kimss Credits, and auditable usage.
Read the guideA compatibility-first path from legacy assistant routes to the preferred universal gateway.
Read the guideConnect workspace pools, group budgets, and usage attribution without hiding the Azure economics underneath.
Read the guideInstall the supported SDK, authenticate a workspace, and understand the focused MCP tool surface.
Read the guideHigh-intent evaluation pages: control plane versus execution plane, frameworks, and cloud-parallel alternatives.
Open comparisonsWhy unsanctioned LLM usage creates security and spend risk—and how a paved control plane eliminates it.
Read the guideAuth, routing, metering, and audit at the edge of model and agent traffic on Azure.
Read the guideIntegrating the model is the easy part. The real engineering challenge is governance, tenant isolation, and multi-tenant management. We break down four things raw Foundry can't do for a B2B SaaS — token quotas vs. infrastructure telemetry, agent-level RBAC, identity translation, and actionable vs. raw audit logs — and why a Day-1 control plane beats six months of custom plumbing.
Read on LinkedInLet your engineers focus on your AI product, and let Kimss handle the gateway — secure, multi-tenant, day one.