LLM cost optimization in Denver.

The technical reality of lean-team spend in Denver.

Questions from Denver buyers.

We are a small team. Which cost fixes give the most savings per engineering hour?

Ranked by payback for a lean Denver team: prompt trimming first (an afternoon of deleting accumulated context against an eval set, frequently 20 to 40 percent off the bill, no architecture changes); caching restructure second (a day or two reordering prompts for stable prefixes, collecting the 90-percent input discount on heavy instructions); model right-sizing third (a day of benchmarking your top traffic class on a cheaper model, then a config change wherever parity holds); batch migration fourth (a few days of queue plumbing for overnight work at half price). What we deliberately rank last for small teams: fine-tuning projects, multi-vendor routing meshes, and self-hosted serving, all of which carry maintenance tails a two-engineer team should not adopt for single-digit additional savings. The audit hands you this ranking computed on your own numbers, so the afternoon fixes happen this sprint.

Our documents are long: leases, title chains, well files. Full-context or chunked processing, cost-wise?

Chunk-and-target usually wins on cost, full-context sometimes wins on simplicity, and the crossover is measurable. Sending a 300-page well file in full context pays for every page on every question; extracting and indexing once, then targeting questions at relevant sections, pays the big cost once and pennies thereafter. The math favors full-context only when documents are processed once and never revisited, or when cross-document reasoning genuinely needs everything in view. For Denver's repeat-query workloads (the same lease consulted across a deal's lifetime, title chains queried by multiple examiners), the index-once pattern cuts per-question costs by an order of magnitude. Prompt caching adds a middle path: a document in a stable cached prefix makes repeated full-context queries far cheaper than they look on the rate card. The audit prices all three patterns on your actual document lengths and query frequencies.

Would running a local model on our own hardware save money?

For most Denver-sized teams, no, and we will show the arithmetic rather than the vibes. The honest ledger: a capable open-weight model needs serious GPU capacity (purchased up front or rented monthly), serving software that someone maintains, update and evaluation cycles when models improve, and on-call attention when the box misbehaves, all weighed against API prices that have fallen repeatedly and include batch and caching discounts. At a few hundred dollars a month of API spend, self-hosting is a hobby with an invoice. The cases where local genuinely wins: data policies that forbid external inference even in-tenancy (rare; cloud-tenancy inference satisfies most), sustained high-volume narrow tasks where a small tuned model keeps a modest GPU busy around the clock, and latency or air-gap requirements money cannot route around. The audit runs your volumes against both columns, and the recommendation comes with the spreadsheet attached.

More than the total, we need the bill to stop surprising us. How do we get predictability?

Predictability is four controls, all cheap to install: budgets with alerts at the provider and feature level (a spend pace alarm at 50 and 80 percent of monthly budget, paged to a human), per-feature spend dashboards so growth is attributable within a day rather than at invoice time, hard ceilings where they belong (per-tenant rate limits, per-job token caps, retry budgets, so one runaway loop or one whale customer cannot torch a month), and change discipline (prompt and model changes ship with before-and-after cost-per-request numbers, because most 'mystery' increases are a deploy with a hidden token cost). With those in place, the monthly bill becomes a forecast with a small error bar instead of a quarterly surprise. We install all four as part of any Denver engagement, because for lean teams the panic-prevention is often worth more than the optimization itself.