Tokenality Articles
Practical thinking on AI cost governance, PII compliance, audit trails, and building controls that don't slow your team down.
The AI Control Plane category consolidated in H1 2026 — three of four independents got acquired within five months. This is the dated, factual, buyer-persona-aware map of what's left, who serves whom, and where the gaps are. CC-BY 4.0.
MCP servers expose tools. Tools have cost. We metered them, capped them, audited them, and built a 10× anomaly detector — without asking the operator to write SQL.
Spend tokens with a binding-key second factor (AES-GCM, AAD-bound). A leaked token without the key can't be replayed. Here's how it works — and what it means for the audit.
Every AI control plane claims an audit log. Most are enforced at the application layer — the code that writes the row is the same code that could rewrite it. Tokenality enforces at the SQL role: the application database role has REVOKE on UPDATE and DELETE. Here's what that means for the auditor.
Most AI Control Planes ship a SOC 2 Type II logo and call it done. Tokenality ships a continuously-generated, signed evidence pack across SOC 2 + ISO 27001 + ISO 42001 + NIST AI RMF that the auditor verifies offline with a standalone CLI. Here's the difference.
Tokenality's MCP gateway primitives map directly to SOC 2, ISO 27001, ISO 42001, and NIST AI RMF controls. This document is the crosswalk — what each MCP feature implements, which framework + control it satisfies, and how it surfaces in the continuous-evidence pack.
Most MCP gateways stop at OAuth 2.1 + PKCE. Tokenality goes further: ID-JAG-ready (Enterprise-Managed Authorization), `agent_sub` claim signed into the spend-token envelope, MCP invocation log enforced at the SQL role layer for the CC7.3 evidence collector. Here's what that means for the procurement conversation.
How a multi-tenant AI-aesthetics SaaS resells AI to its end clients with per-tag chargeback, hard quotas, and a second-factor binding key that means a compromised UI or API can't replay tokens. A preview case study — technical flow live; quotes and logos awaiting customer sign-off.
How an electricity-brokerage software shop turned AI assistant spend from a fuzzy line item into per-project chargeback their clients pay without a quibble. A preview case study — technical flow live; quotes and logos awaiting customer sign-off.
A plain-English definition of AI cost governance: what it covers, why it's different from general cloud cost management, and what it looks like in practice.
An LLM gateway is a proxy that sits between your applications and AI model providers. Here's what it does, how it works, and when you need one.
The anatomy of an uncapped agent spend event, and the one architectural change that prevents the next one.
AI spend is now material for many organizations. Here's how to build a chargeback model that finance will actually use.
The questions are coming. Here's what auditors are asking, what they're looking for, and how to be ready.
What it actually takes to get per-project cost attribution in place — from someone who's built it.
Embedded AI in your SaaS tools is real spend that never shows up in your API gateway. Here's how to find it and bring it into one view.
The complete walkthrough: from zero to a running Tokenality gateway with your first Spend Token, in under 30 minutes.
Every day, sensitive data enters AI context windows without being logged, reviewed, or governed. Here's what's actually happening and what a real fix looks like.
Most AI budget controls are alerts. A spend token is a hard limit. Here's the difference and why it matters.
Learn the product