Ask three vendors for a quote on the same sequencer and you will get three numbers, none of which you can repeat to a colleague at another institution. Instrument pricing in genomics is negotiated individually, bundled with service contracts and trade-in credit, and covered by terms that specifically prohibit disclosure. That is normal commercial practice, and it means almost nothing written about whole genome sequencing cost in a press release or a marketing page reflects what any specific lab will actually pay.

University and hospital core facilities operate under a different rule. Many are required, as a condition of institutional or grant funding, to publish a public rate card and to charge every user, internal or external, against it. That requirement makes core facility pricing the closest thing genomics has to a public price list, and it is the benchmark this article uses throughout. Figures below are drawn from a live rate card at a mid-size US academic medical center core facility, current as of mid-2026, and every figure has been independently recalculated from the source’s published run pricing rather than simply copied. Program-level cost modeling and the buy-versus-outsource decision are covered in the Building an NGS Program: Strategy, Budget, and ROI, and the operational cost-per-genome model this article’s figures get compared against comes from the main guide to next-generation sequencing in the lab.

Why Published Cost Figures Rarely Match Your Invoice

Three different numbers all claim to describe the same thing, and reconciling them is most of what this article does. The first is the production-cost figure NHGRI has tracked at its funded sequencing centers since 2001, which reflects large-scale specialist production and excludes library prep markup, QC, and downstream analysis. The second is a vendor marketing figure, quoted at maximum theoretical throughput on the newest instrument, which no working lab actually achieves run after run. The third is a core facility rate card, and it sits closer to a real number than either of the other two, because a core facility has to charge enough to recover its costs and is legally required to publish what it charges.

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Even the rate card is not the whole story. It is priced to recover the facility’s direct costs, sometimes with a modest margin and sometimes at a loss subsidized by the institution, and it typically excludes long-term data storage beyond the run itself, dedicated bioinformatics analyst time, and the cost of repeated or failed runs, which are rebilled separately when they occur. NHGRI’s own cost-per-genome dataset carries the same caveat for the same reason: production cost at a specialist center and fully loaded cost for a working lab answer different questions, and neither number should be quoted as if it answers the other.

Capital Cost by Platform Class

New-instrument pricing is not public, and any figure claiming to be a current list price for a specific sequencer should be treated with suspicion unless it comes directly from a vendor quote. What is public is the secondary market, where used and refurbished instruments are bought and sold openly, and while resale price is not a substitute for a new-instrument quote, it is a genuine, dated, public data point on relative capital cost by platform class. It also tends to understate new pricing substantially, since used instruments have typically depreciated for several years and may carry reduced or absent service coverage.

Table 1. Platform classes by typical role. Secondary-market figures are directional reference points from lab equipment resale listings, not new-instrument quotes; get a current vendor quote before budgeting a purchase.

The pattern that matters more than any single price point is the spread itself. The difference between platform classes runs into a full order of magnitude or more, which means the platform-class decision, covered in the readiness and program-strategy content linked above, is a substantially bigger lever on capital cost than negotiating within a class.

Reagents and Library Prep

Library preparation cost depends far more on assay complexity than on sequencer choice, and this is where a published rate card is most directly useful, because library prep pricing is comparatively stable across institutions and less dependent on negotiated capital deals. At the CWRU Genomics Core’s published rate card, a PCR-free whole-genome DNA library runs $125 per sample in batches under 24 and $100 per sample at 24 or more, reflecting the fixed per-batch overhead that drops out once a run is filled. A standard whole-genome library, not PCR-free, runs $100 and $75 per sample at the same batch thresholds. A human exome library, which requires a hybridization capture step the whole-genome protocols skip entirely, runs $250 under 12 samples and $200 at 12 or more, roughly double the whole-genome figure at comparable batch size.

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That exome premium is a direct answer to a common question: exome sequencing is not cheaper because exome libraries are simpler. It is cheaper overall because the sequencing volume required is far smaller, a few gigabases of coding sequence rather than the roughly 90 or more gigabases needed to cover a whole genome at 30x. The library prep step itself costs more per sample for exome. The sequencing step costs far less, and the sequencing step dominates the total.

Sequencing Cost per Gigabase

Flow cell size, not platform generation, is the single biggest driver of cost per gigabase within a platform family. The same NovaSeq X instrument, run on progressively larger flow cells, shows a clear and substantial economy of scale.

Table 2. Sequencing-only cost per 30x human whole genome by flow cell tier, calculated from the CWRU Genomics Core’s published July 2026 run pricing, assuming approximately 93 Gb of PE150 output per 30x genome. Excludes library prep, QC, and all costs beyond the sequencing run itself.

Storage, Compute, and Analyst Time

None of the figures above include what happens after the run completes, and that is the largest gap between a rate card and a real program budget. Storage for the full set of key files from a single 30x genome runs into the hundreds of gigabytes, at a cloud cost that compounds every year data is retained rather than resetting annually, a dynamic modeled in detail in the main hub. A rate card typically bills only for the run; it does not, and generally cannot, price your institution’s long-term retention policy, because that policy is yours to set, not the core facility’s.

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Bioinformatics analyst time is the other omission, and it is usually the larger one in practice. A core facility rate card prices getting you a set of files. It does not price turning those files into an answer, and that step routinely takes longer and costs more, in staff time, than the sequencing run that produced the data in the first place. Any budget built entirely from rate-card figures will look complete and will not be.

A Worked Cost per Sample

Put the pieces from this article together for a single 30x human genome, produced as efficiently as the published rate card allows: a PCR-free whole-genome library at the large-batch rate, $100. Sequencing on the largest flow cell tier at the midpoint of its range, roughly $231. Basic QC, a TapeStation run on the extracted DNA and a second on the final library plus two Qubit checks, roughly $42. Total: approximately $374 per genome.

Compare that to the main hub’s fully loaded model for an in-house program running 1,000 genomes a year, which lands at $1,168 per genome, more than three times higher. Both figures are correct. They are not measuring the same thing.

Table 3. Reconciling the $374 rate-card production figure against the main hub’s $1,168 fully loaded figure. The rate card is a genuine, useful benchmark for what sequencing itself costs. It is not a substitute for a program-level budget.

The practical use of this reconciliation is not to decide which number is right. It is to know which number you are being handed. A vendor slide quoting a low per-genome figure is very likely a rate-card-style number, sequencing and library prep only, at maximum batching, on the largest available flow cell. A budget review that only has that number will be blindsided by everything Table 3’s second column adds back in. Bringing your own program’s figures into this same reconciliation is covered by the cost calculator at NGS Cost Calculator: Model Your Own Cost per Sample, and the marketplace side of capital cost, buying new, used, or refurbished, is covered from a purchasing rather than a benchmarking angle in What Does a Sequencer Really Cost? Purchase, Reagents and Service.

This article was produced under Lab Manager's AI Editorial Guidelines.