G|AI Works G|AI Works

Reference engagement

LLM Cost Tracking & Budget Policies

Control spend without killing quality: per-request cost tracking, routing, caching, and budget gates.

Scope a similar engagement

// Delivery pattern

This page describes a representative engagement of this shape — how the system is scoped, built, and handed over. Specific figures reflect typical outcomes of the pattern when delivered with the operational discipline described on the About page. Named customer engagements are shared under NDA on request.

Engagement shape

Typical outcomes

  • Predictable spend
  • Faster debugging
  • Better quality-cost tradeoffs

Stack

  • Telemetry events
  • Budget gates
  • Routing
  • Caching (optional)

Typical timeline

2–3 weeks

kick-off to handover

Risks & guardrails

  • Over-instrumentation — track at the workflow level first, not every token call
  • Budget gates too aggressive — test thresholds on real traffic before enforcing hard limits

Problem

Costs drift silently: long prompts, hidden context growth, provider retries, and tool calls can multiply spend. Most teams only notice after the invoice.

Solution

  • Per-request cost and token breakdowns (prompt vs completion)
  • Budget policies by workflow/user/role
  • Routing and caching for predictable cost-quality tradeoffs
  • Alerts for spikes, failures, and “context bloat”

What you get

  • Cost telemetry and dashboards
  • Budget gates and safe fallbacks
  • Clear playbooks for cost incidents

CTA

If you want predictable spend without sacrificing reliability, we’ll instrument and harden your stack.

Scope a similar engagement

Does this pattern fit your situation?

Tell me the system you're trying to integrate and the outcome you're measured on. You'll get a clear next step — a readiness audit, a prototype plan, or a delivery proposal.