SOC 2 Type II cost control architecture for enterprise LLM deployment — Reference Architecture

Reference architecture · published 2026-10-16 · regulated enterprise · SOC 2 Type II · layout matrix_first · implementation executed in CI before publish

The control that resolves this is a Pre-Flight Budget Governor, evaluated before the request reaches a model provider rather than audited afterwards. In regulated enterprise the binding constraint is SOC 2 Type II, and the question it actually asks is did this control operate effectively for the whole observation window. The shape below holds p95 ≤ 900ms over REST over HTTPS, and ships with a standard-library implementation that runs as written.

Technical parameters

Every value below is a parameter you can hold a vendor or an internal team to. Adjectives are not acceptance criteria.

Risk mitigation matrix

Each row carries an acceptance parameter rather than an adjective, because "low latency" cannot be signed off. Retention here is bound by per the data class retention schedule, enforced automatically.

Where the rollout actually stalls

LLM spend is discovered in a monthly invoice and defended with a dashboard, so it is reported rather than controlled. Finance asks for a per-team cap and engineering cannot implement one, because nothing in the request path knows a budget exists.

What SOC 2 Type II specifically requires

SOC 2 Type II is not a point-in-time assertion. The auditor samples evidence across a 3-12 month observation window and asks whether the control operated on every day of it, not whether it exists today.

The artifact that satisfies it is a sampled, time-ordered population the auditor can draw from. The way teams most often fail it: the control was added mid-window, so the population has a hole and the observation period restarts.

The parts you have to build

1. Conservative estimator — Deliberately over-estimates; under-estimating makes the ceiling decorative.
2. Two-phase budget — Reserve the estimate, settle the actual — closes the concurrency race.
3. Rate table in config — Prices change; a stale rate in code silently misprices every chargeback.
4. Tier router — Routes to the cheapest tier that meets the quality bar for the task.
5. Chargeback ledger — Per-tenant, per-request actuals — the artifact finance reconciles against.
6. Fail-closed default — A tenant with no budget is refused, never admitted.

Reference architecture

Pre-Flight Budget Governor — the request path, with the decision points that must exist before the first model call:

flowchart LR
    REQ[Request] --> EST[Conservative token estimate]
    EST --> PR[Price at tier rate]
    PR --> RES{Reserve against budget<br/>spent + outstanding + projected}
    RES -->|over ceiling| REJ[429 BudgetExceeded]
    RES -->|no budget| FC[Refuse - fail closed]
    RES -->|ok| TR[Tier router<br/>cheapest tier meeting quality bar]
    TR --> M[Model call]
    M --> ST[Settle actual usage]
    ST --> LED[(Chargeback ledger)]
    LED --> FIN[Finance reconciliation]

The implementation — Two-phase token budget with per-tenant chargeback

Standard-library Python, no external dependencies, runs as written. Executed by the publish gate: if it raises, this page does not ship. Wire it behind REST over HTTPS; the failure mode it guards against is a retried POST creating a duplicate record. The output you hand the assessor is the observation-window evidence export.

"""Pre-flight token budget enforcement with per-tenant chargeback.

Why this shape: LLM spend is discovered monthly in an invoice and defended with
a dashboard, which means it is never actually controlled. The control has to run
BEFORE the call, on an estimate, with a hard ceiling per tenant and per period —
otherwise the first thing finance asks for is a cap you cannot implement.
"""
import math
import time
from dataclasses import dataclass, field


# USD per 1k tokens. Keep rates in config, not code: they change, and a stale
# rate silently misprices every chargeback downstream of it.
RATES = {
    "frontier":  {"in": 0.0030, "out": 0.0150},
    "mid":       {"in": 0.0008, "out": 0.0040},
    "small":     {"in": 0.0001, "out": 0.0004},
}


def estimate_tokens(text: str, chars_per_token: float = 3.8) -> int:
    """Deliberately a conservative OVER-estimate.

    Under-estimating defeats the purpose: the request gets admitted, the real
    usage exceeds the budget, and the ceiling is decorative."""
    return max(1, math.ceil(len(text or "") / chars_per_token))


def price(tier: str, tokens_in: int, tokens_out: int) -> float:
    r = RATES[tier]
    return (tokens_in / 1000.0) * r["in"] + (tokens_out / 1000.0) * r["out"]


class BudgetExceeded(Exception):
    def __init__(self, tenant, projected, ceiling):
        self.tenant, self.projected, self.ceiling = tenant, projected, ceiling
        super().__init__(f"{tenant}: ${projected:.4f} would exceed "
                         f"${ceiling:.2f} for the period")


@dataclass
class Budget:
    ceiling_usd: float
    period_seconds: float = 86400.0
    spent_usd: float = 0.0
    started: float = field(default_factory=time.time)
    committed: dict = field(default_factory=dict)   # request_id -> reserved USD

    def _roll(self, now):
        if now - self.started >= self.period_seconds:
            self.started, self.spent_usd = now, 0.0
            self.committed.clear()

    def reserve(self, request_id: str, projected_usd: float, now=None) -> float:
        """Two-phase: reserve the estimate, settle the actual.

        Without a reservation, N concurrent requests each see the same low
        `spent` and all get admitted — the classic race that blows a cap."""
        now = time.time() if now is None else now
        self._roll(now)
        outstanding = sum(self.committed.values())
        if self.spent_usd + outstanding + projected_usd > self.ceiling_usd:
            raise BudgetExceeded("tenant",
                                 self.spent_usd + outstanding + projected_usd,
                                 self.ceiling_usd)
        self.committed[request_id] = projected_usd
        return projected_usd

    def settle(self, request_id: str, actual_usd: float):
        self.committed.pop(request_id, None)
        self.spent_usd += actual_usd

    def release(self, request_id: str):
        self.committed.pop(request_id, None)


class Governor:
    def __init__(self):
        self.budgets: dict = {}
        self.ledger: list = []

    def set_budget(self, tenant: str, ceiling_usd: float, period_seconds=86400.0):
        self.budgets[tenant] = Budget(ceiling_usd, period_seconds)

    def admit(self, tenant: str, request_id: str, prompt: str,
              tier: str = "mid", max_output_tokens: int = 1024) -> dict:
        b = self.budgets.get(tenant)
        if b is None:
            raise KeyError(f"{tenant} has no budget: fail closed, never open")
        t_in = estimate_tokens(prompt)
        projected = price(tier, t_in, max_output_tokens)
        b.reserve(request_id, projected)
        return {"tenant": tenant, "request_id": request_id, "tier": tier,
                "tokens_in_est": t_in, "projected_usd": round(projected, 6)}

    def record(self, tenant: str, request_id: str, tier: str,
               tokens_in: int, tokens_out: int) -> dict:
        actual = price(tier, tokens_in, tokens_out)
        self.budgets[tenant].settle(request_id, actual)
        row = {"tenant": tenant, "request_id": request_id, "tier": tier,
               "tokens_in": tokens_in, "tokens_out": tokens_out,
               "actual_usd": round(actual, 6), "ts": time.time()}
        self.ledger.append(row)     # this is the chargeback record
        return row


if __name__ == "__main__":
    g = Governor()
    g.set_budget("bank-eu", ceiling_usd=0.05)
    adm = g.admit("bank-eu", "req-1", "Reconcile these 400 journal lines." * 20)
    print("admitted:", adm)
    print("settled :", g.record("bank-eu", "req-1", "mid", adm["tokens_in_est"], 310))
    try:
        for i in range(40):
            g.admit("bank-eu", f"burst-{i}", "x" * 4000, tier="frontier")
    except BudgetExceeded as e:
        print("ceiling held:", e)

The SOC 2 Type II control, as code

Type II evidence sampling over the observation window. This is the artifact an assessor asks for under SOC 2 Type II specifically — it is not the same control as the one above, and a system that implements one does not thereby implement the other. It executes under the same publish gate.

"""SOC 2 Type II evidence sampling over an observation window.

Type II is not "the control exists" — it is "the control operated throughout
the period". An auditor samples days from the window and asks for proof the
control ran on each sampled day. A gap of one day with no evidence is an
exception, so the population must be built from operational records rather
than asserted.
"""
import hashlib
from datetime import date, timedelta


class ObservationWindow:
    """The population an auditor samples from, plus deterministic selection."""

    def __init__(self, start: date, end: date, control_id: str):
        if end < start:
            raise ValueError("window end precedes start")
        self.start, self.end, self.control_id = start, end, control_id
        self._evidence: dict[date, list[str]] = {}

    def record(self, day: date, artifact_ref: str) -> None:
        if not (self.start <= day <= self.end):
            raise ValueError(f"{day} outside observation window")
        self._evidence.setdefault(day, []).append(artifact_ref)

    def population(self) -> list[date]:
        n = (self.end - self.start).days + 1
        return [self.start + timedelta(days=i) for i in range(n)]

    def gaps(self) -> list[date]:
        """Days with no evidence. Each one is a Type II exception."""
        return [d for d in self.population() if not self._evidence.get(d)]

    def sample(self, size: int) -> list[date]:
        """Deterministic, reproducible selection — an auditor must be able to
        re-derive the same sample from the same seed rather than trust ours."""
        pop = self.population()
        size = min(size, len(pop))
        seed = f"{self.control_id}:{self.start}:{self.end}"
        scored = sorted(
            pop, key=lambda d: hashlib.sha256(f"{seed}:{d}".encode()).hexdigest())
        return sorted(scored[:size])

    def evidence_for(self, day: date) -> list[str]:
        return list(self._evidence.get(day, []))

    def attestation(self, size: int = 25) -> dict:
        sampled = self.sample(size)
        missing = [d for d in sampled if not self._evidence.get(d)]
        return {
            "control_id": self.control_id,
            "window": f"{self.start}..{self.end}",
            "population_days": len(self.population()),
            "sampled_days": len(sampled),
            "exceptions": [str(d) for d in missing],
            "opinion": "unqualified" if not missing else "qualified",
        }


if __name__ == "__main__":
    start, end = date(2026, 1, 1), date(2026, 3, 31)
    w = ObservationWindow(start, end, "CC6.1-tenant-isolation")
    for d in w.population():
        w.record(d, f"s3://evidence/{d}/isolation-check.json")
    clean = w.attestation()
    assert clean["opinion"] == "unqualified", clean
    assert clean["population_days"] == 90, clean

    # One missing day is one exception — and it must survive sampling to matter.
    w2 = ObservationWindow(start, end, "CC6.1-tenant-isolation")
    sampled = set(w2.sample(25))
    for d in w2.population():
        if d != sorted(sampled)[0]:
            w2.record(d, "ok")
    assert w2.attestation()["opinion"] == "qualified", w2.attestation()
    assert len(w2.gaps()) == 1, w2.gaps()

    # The sample must be reproducible from the same seed.
    assert w.sample(25) == ObservationWindow(start, end,
                                             "CC6.1-tenant-isolation").sample(25)
    print("SOC 2 Type II evidence sampling: OK")
    print(f"  population {clean['population_days']} days, "
          f"sample {clean['sampled_days']}, opinion {clean['opinion']}")

What the auditor will ask

• Show me the control-execution population for the full window, and the days where it is empty.
  Every request emits an execution record, so the population is continuous by construction. A gap is an alertable incident, not something discovered during the audit.
• What refuses a request that would exceed this team's monthly cap, and where in the path does it run?
  The budget governor, at admission — before the model call. It prices a conservative token estimate against the tenant's remaining ceiling and refuses the request if the projection would exceed it.
• If 50 requests arrive at once against a nearly-exhausted budget, how many are admitted?
  Only those that fit. Admission counts outstanding reservations as well as settled spend, which closes the race where concurrent requests all read the same low figure and are all admitted.
• How does your chargeback figure reconcile to the provider invoice, and what is the variance?
  The chargeback ledger records per-request actuals priced from a versioned rate table with an effective date; reconcile it monthly and track the variance against the provider invoice.
• What happens to a tenant with no budget configured?
  It is refused. A missing budget fails closed — admitting an unbudgeted tenant is how the cap becomes decorative.
• Which rate table priced last month's chargeback, and when was it last updated?
  The rate table in versioned config, with its effective date recorded. A rate older than 90 days raises an alert, because a stale rate silently misprices every chargeback downstream.

Related architectures

• Part of enterprise llm cost control.
• Where this interacts with another regime, see SOC 2 Type II data governance framework for enterprise LLM deployment.
• Where this interacts with another regime, see SOC 2 Type II model release gate and golden set evaluation for enterprise.
• Where this interacts with another regime, see SOC 2 Type II provider failover and degradation architecture.
• The same control appears in SOC 2 Type II tracing and token accounting architecture for enterprise.
• If this is the blocker in front of you: enterprise AI infrastructure review.