ROI cost control architecture for enterprise LLM deployment in energy and utilities — 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 specific problem here is total cost of ownership. The shape below holds p95 ≤ 900ms over REST over HTTPS, and ships with a standard-library implementation that runs as written.

Enterprise implementation parameters

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

Risk register with acceptance criteria

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.

Why this blocks production

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 makes total cost of ownership specifically hard

Token price is the smallest line. The cost that moves the number is retrieval infrastructure, human review, evaluation maintenance and the engineering time spent on upgrade regressions.

What resolves it is a cost model with unit economics per transaction, not per token. The failure to design against: a forecast built on token price that misses by an order of magnitude.

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.

Component breakdown

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.

The request path

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)

Review questions and answers

• 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 is the cost per completed transaction, not per token?
  The model prices the whole transaction: retrieval, inference, human review time and rework rate, reconciled monthly against the provider invoice and the review timesheet.
• 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.

Primary sources

This page exists because the problem is reported publicly. The threads below are the primary sources, linked unmodified and not affiliated with this site.

• Solar and batteries can power the world hackernews · hn/comment · 2026-04-03

Related reference architectures

• Part of enterprise llm cost control.
• This pattern also governs P95 cost control architecture for enterprise LLM deployment.
• multi-tenant cost control architecture for enterprise LLM deployment covers the adjacent case.
• Where this interacts with another regime, see HIPAA audit evidence architecture for enterprise LLM deployment.
• air-gapped architecture for enterprise LLM deployment at scale applies the same mechanism under a different obligation.
• If this is the blocker in front of you: enterprise AI infrastructure review.