How AI actually helps with cloud bills (and where it doesn't)

"Use AI" is a sentence you say to a board. It's not a feature. So we want to be specific about what an LLM is and isn't good at when you point it at a cost & usage report — because we get this question on every demo call.

What an LLM is genuinely great at

• Translation. Cloud SKU codes are a foreign language. An LLM can turn USE1-EBS-GP3-Bytes-Used into "general-purpose SSD volume storage in us-east-1, billed per GB-month" without missing a beat.
• Pattern recognition across line items. A bill where 70% of "data transfer" is intra-region and 30% is internet egress tells a different story from the inverse. An LLM can read that shape and write the sentence describing it.
• Stitching context together. Your one-paragraph app description, the line items, and the rough shape of the cost curve combine into a story. Telling that story is exactly what language models do.
• Drafting communication. The report you'd send your CFO, your team lead, or your investors is a writing task. The numbers are evidence; the readout is prose.

Where an LLM is the wrong tool

• Forecasting. An LLM is not a time-series model. We use real numerical methods for projections, not the language model.
• Exact arithmetic at scale. Computing your bill is a database query, not a prompt. The LLM never sees raw multiplication tasks — it sees aggregates we computed up front.
• Decision authority. The LLM does not push the button to delete your snapshots. We surface the action; you take it.

The split is straightforward: deterministic code does the math, the LLM does the reading and writing. Anyone using AI to do arithmetic on a million-row CUR file is going to have a bad month.

The unfair advantage: your context is text

The thing that makes AI genuinely useful for billing — and what was impossible five years ago — is that the most valuable input is unstructured. "We're a B2B analytics SaaS, our peak is Tuesday 9am ET, we just acquired a customer that 5x'd our ingest." That paragraph is pure language, and language is exactly what an LLM eats for breakfast. The same paragraph, twenty years ago, would have been a 60-minute call with a consultant.

Why we still write our own retrieval

An LLM with no grounding will confidently invent SKU codes, hallucinate prices, and misremember which region runs what. So we don't ask it to remember anything. We pre-compute the bill aggregates, look up the public price book ourselves, and hand the model a tight, factual context window. The model writes the sentence; the facts are pinned down.

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