Comparison
AI governance SaaS vs policy-as-code you own
An AI-governance SaaS observes AI usage from outside your stack and alerts you when something looks wrong. Policy-as-code governance enforces the rules inside your own accounts — which models, which data, which regions, what spend — so a disallowed call fails instead of getting logged. The SaaS is a per-seat subscription whose control leaves when you stop paying; the policy-as-code is Terraform and OPA you keep and change yourself.
Every team is wiring up an LLM, and the market has two answers for governing it. They are not the same product with different logos. They are different categories.
| AI-governance SaaS | Policy-as-code you own | |
|---|---|---|
| Where it runs | A vendor dashboard outside your stack | Inside your accounts (Terraform/IaC, OPA, gateways) |
| What it does | Observes and alerts | Enforces — a disallowed call fails |
| Survives cancellation? | No — the control leaves with the subscription | Yes — it is your code on standard tools |
| Commercial shape | Per-seat recurring | A one-time build you maintain |
| Best for | A reporting layer across many tools | Actual guardrails on your own platform |
Observing is not enforcing
A dashboard that tells you, after the fact, that a team sent customer PII to an unapproved model has not governed anything. It has produced an incident report faster. Real governance is a control in the request path: the call to the unapproved model, or with the wrong data classification, or from the wrong region, does not succeed. That control has to live where the requests are — in your cloud, your gateway, your IAM — not in a SaaS that watches from the side.
The subscription problem
The deeper issue with rented governance is what happens at renewal. The control is the subscription. The day you stop paying, the enforcement (or even the observation) stops, and you are back to ungoverned AI usage with a gap you may not notice for months. Anything load-bearing in your security posture should not have an off-switch held by a third party’s billing system.
What “owned” actually buys you
Policy-as-code governance is unglamorous and durable:
- A paved path — a module teams consume to get sanctioned model access, so the easy way is also the governed way.
- Guardrails as policy — allowed models, data classifications, regions, spend and rate limits — enforced at the gateway and in IAM, versioned in your repo.
- A data-flow threat model — what data can reach which model, and the boundaries that stop the rest.
- Cost and usage controls as code, not a bill you reconcile later.
When the models change (and they change monthly), you edit the policy yourself. No vendor roadmap, no per-seat math, no export.
Where SaaS still fits
A governance dashboard can be a reasonable reporting layer across many teams and tools, especially in a large org that wants one pane of glass. It is a poor enforcement layer for your own platform. Use it for visibility if you like; don’t mistake it for a control you own.
The AI Platform Governance engagement builds the enforcing version: guardrails as Terraform and policy-as-code in your accounts, yours to keep and change.