Why AI governance is
so expensive to begin with.
The cost problem isn't the LLM. It's the governance architecture built around it. Here's what that looks like at the token level.
| Standard guardrails | colliga.ai CaiGR™ | |
|---|---|---|
| Input token overhead (conditioning) | ~3,800 added tokens per call | None |
| LLM calls per interaction | 3 sequential calls (typical) | 1 call |
| Retry overhead on failure | 1–2 additional calls | 0 — failures are structurally impossible |
| Cost scaling behavior | Linear with volume | Linear with volume, 53–58% lower base |
| Net per-interaction cost vs. unguarded baseline | 2.1–3.2× higher | Effectively 1× |
SOURCES: REBEDEA ET AL., EMNLP 2023 §7.2 · SEQUOIA CAPITAL AI OUTLOOK 2025 · INTERNAL BENCHMARKS
Post-hoc filtering
Generate first, evaluate after. Bad outputs are caught (sometimes) and retried. Each retry is a full LLM call. The governance cost grows proportionally with deployment scale — and with the percentage of outputs that fail.
Structural envelope
The governance envelope is derived once from policy, then applied structurally to constrain what the model can produce. There is nothing to filter afterward because out-of-bounds outputs are architecturally impossible. One call. No retries.
Audit trail that holds up
under examination.
A probabilistic system that sometimes catches bad outputs cannot produce a deterministic record of what governance decisions were made and why. If asked to replay a specific interaction, it can't — because the decision wasn't rule-based. It was probabilistic.
CaiGR™ governance operates on a deterministic structural envelope. Every constraint applied, every boundary enforced, every governance event is cryptographically recorded with a policy link. Any interaction can be replayed exactly.
The record doesn't say “the model tried to comply.” It says exactly what rule applied, exactly what the output was, and exactly why it was within policy.
“Show us every AI interaction over the last 90 days that touched customer financial data, and the governance record for each.”
colliga.ai: done.
“Produce all AI-generated outputs in this matter and demonstrate they were within your stated policy at the time of generation.”
colliga.ai: done.
“How do you know your AI isn’t making up answers? Can you prove it?”
colliga.ai: done.
The distinction that changes
everything downstream.
Every other AI governance approach is, at its core, a form of negotiation with a probabilistic model. colliga.ai is not.
Asking
Pre-prompt conditioning is a well-mannered request: “Please stay within these guidelines.” The model has every technical capability to comply — and every technical capability to drift. Whether it complies is a matter of probability, not structure.
Post-hoc filtering is a second negotiation: “Did that output pass our filter?” If not, ask again. Each ask costs tokens. Each retry costs tokens. Each failure adds latency. The system is as reliable as the probability of success on any given call — which is less than 1.
At a million interactions a month, “less than 1” is a very large number of failures.
Telling
CaiGR™ derives a structural envelope from your governance policy. That envelope defines what the model can produce — not as a preference, but as an inescapable architectural constraint. The model generates within it. There is no conditional. There is no probability. There is no filter applied afterward, because there's nothing to filter.
Every interaction that completes has, by definition, complied. Every compliance event is recorded. One call. One record. No retries.
Governance that doesn't add cost. Compliance that doesn't require trust.
The result is the same decision twice.
Lower cost because you eliminated the overhead of asking. Stronger governance because you replaced probability with structure. You don't have to choose — they come from the same architectural fix.