LLM cost optimization in Raleigh.

The technical reality of research-economy spend in the Triangle.

Questions from Raleigh-Durham buyers.

Our institution has GPU clusters. Should our workloads run there instead of on APIs?

Run the arithmetic per workload, because campus GPUs change the ledger in both directions. What campus clusters offer: marginal compute that is often subsidized or pre-paid (grant-purchased nodes, institutional allocations), data that never leaves institutional infrastructure (decisive for IRB-governed or pre-publication work), and queue time as the real currency. What they cost: open-weight model quality on your actual tasks (benchmark before assuming; the gap on complex reasoning is real), serving and maintenance attention from people whose job is research, and contention during crunch seasons when everyone's deadline shares the same nodes. The pattern that wins across Triangle institutions: sensitive and sustained pipelines (instrument output, corpus embedding) on campus hardware where allocation economics favor them; bursty, quality-critical, or unrestricted work on APIs with batch and caching discounts. The audit prices both columns per workload, with the allocation subsidy made explicit rather than treated as free.

Grant budgets need predictable line items. How do we forecast AI costs for a proposal?

From measured per-unit costs and stated volumes, the same way you budget sequencing or core-facility time. The method: instrument current usage (or run a two-week pilot on representative work), derive cost per unit of research activity (per thousand documents processed, per analysis run, per student-project-month), then multiply by the proposal's stated scale with a modest contingency, 15 to 20 percent covers model-price drift, which has historically trended down rather than up. The artifacts that make grant officers comfortable: a per-unit basis documented in the budget justification ('document analysis at approximately $X per thousand based on measured pilot usage'), caps wired into the tooling so a runaway loop cannot eat the budget line, and per-project attribution so reporting matches accounting. We set up exactly that instrumentation and write the budget-justification language with you, because a defensible AI line item in a proposal is becoming a competitive advantage rather than an oddity.

Literature and corpus processing dominates our spend. What does the optimized version look like?

Batch-first, hash-aware, and right-sized, in that order. Batch-first: literature ingestion, abstract screening, extraction across paper corpora, and metadata enrichment are never latency-bound, so the whole pipeline belongs on batch endpoints at roughly half synchronous price (or on smoothed campus queues where the GPU math won). Hash-aware: re-processing unchanged papers is the silent leak in long-running research pipelines; a content-hash skip layer typically eliminates the majority of embedding and extraction calls in mature corpora, since literature accumulates but rarely mutates. Right-sized: screening and extraction tasks clear quality bars on small models (gated by a sample your researchers grade), with the strong model reserved for the synthesis and reasoning passes where it earns its rate. Stacked, these take per-thousand-paper costs down by 60 to 80 percent against naive pipelines, which at systematic-review or CRO scale converts directly into scope: more corpora processed on the same grant line.

Evaluation harnesses re-run on every change. Does the eval itself become a meaningful cost?

It can, and the fix is engineering the eval like any other workload rather than skipping it. The costs: a thorough eval set re-run on every prompt or model change multiplies quickly when the set is large, the model is frontier, and CI triggers on every commit. The disciplines that keep it cheap: tiered eval (a fast smoke set of 30 to 50 cases on every commit, the full set nightly or pre-release), batch submission for full runs (evals are the definition of latency-insensitive), cached shared context (instructions and rubrics as stable prefixes), and right-sized graders (LLM-as-judge on a mid-tier model agrees with frontier judges far more often than its price suggests, verifiable against a human-graded sample). A typical result: eval overhead drops from a noticeable line item to low single-digit percent of workload spend, which removes the temptation to economize by evaluating less, the one saving that always costs more than it returns.