A GoSpend agent on your stack that forecasts cloud and token demand as a distribution, attributes every dollar in real time, optimizes the waste out, and pre-buys the floor.
Prices per token keep falling and bills keep climbing, because usage grows faster than prices drop — and one agentic workload burns what a chatbot never could.
You cannot control a real-time cost with a monthly report — and you cannot cut a bill you can't predict or attribute.
Cost platforms see infrastructure but not tokens. AI gateways govern tokens but can't commit cloud capacity. This covers both.
Compute, storage and tokens projected hours to quarters ahead as P10/P50/P90, with a breach probability per budget ceiling.
Each dollar and token mapped to a person, team and project in real time, inferred from the cost graph — no tagging debt to pay down first.
Savings ranked by dollar value: right-size, cache, reroute, kill idle. One click to approve, or fully automated.
Reserved capacity, savings plans and provisioned throughput bought for the demand the forecast is certain about.
A distribution tells you when the expensive version of next quarter becomes likely — the only moment intervention is still cheap.
Pre-built connectors for hyperscalers, Kubernetes, GPU fleets, LLM APIs and self-hosted models — one schema, real-time tagging.
Live burn per owner, forecast vs budget, dollar-ranked actions. Separate views for the engineering manager, the CTO and the CFO.
Everything the dashboard shows is also an API. Alerts land in chat and ticketing; approvals enforce your thresholds before a dollar moves.
Four compounding levers, then the cheapest instrument for the certain demand. In a representative model, $63.9k → ~$22.4k before any commitment discount.
| Lever | What it does | Effect |
|---|---|---|
| Semantic + context caching | Similar prompts served from cache; repeated context billed at a fraction of fresh input. | first lever |
| Model routing & right-sizing | Straightforward calls go to a smaller model; escalates only when quality demands it. | ~80% / answer |
| Batch & off-peak scheduling | Latency-tolerant traffic moves to asynchronous endpoints. The agent decides what can wait. | ~50% cheaper |
| Provisioned throughput | The forecast sets your 24/7 baseline; the floor is pre-bought and bursts fall back to pay-per-token. | forecast floor |
Caching is first because it is the only lever that gets better with popularity. Every other lever trades something — a smaller model trades headroom, batching trades latency, a commitment trades flexibility. Cache hits trade nothing.
Routing is second because it is measurable per answer: the agent scores routable traffic against your quality bar before it moves any of it, and the split is a number your team can read.
On a representative $10M/year cloud + AI budget, the build typically pays for itself inside the first quarter.
A blended waste cut plus the steady demand band pre-bought at commitment rates — dropping straight to margin.
An agent that sees every workload, team and dollar is not a system to rent from a vendor whose roadmap you don't control.
Source, prompts, thresholds and approval logic. Your team changes what an agent may buy without raising a ticket.
Quantile demand and token models fitted on your realized bill. Weights, features and the recalibration pipeline stay in your account.
Every integration and the attribution graph that resolves a line item to a person — yours to extend when you add a cloud or a BU.
Nothing meters your engineers, and no renewal can price you out of your own cost control. Savings become margin, not subscription.
Cost, usage and workload metadata never leave your boundary. The agent runs under your IAM and your keys from sprint one.
We embed with your platform, data and GoSpend team and transfer sprint by sprint, with run-books. The day after handover looks like the day before.
Three steps, one quarter, measured against your live numbers.
One business unit on your cloud and AI stack.
Two weeks: baseline, target, and a build plan for your environment.
20%+ savings measured in your environment, not in a model.