whatismmm

DECIDE · STEP 2 OF 4

Build vs buy

Set your spend, channel count, and whether a data scientist already exists on payroll. The board below prices both paths with sourced numbers only: BLS salary data times a stated loading factor on the build side, published vendor prices on the buy side, and a verdict sentence written to be pasted into a budget slide.

By Oliver Wakefield-Smith · Updated 2 August 2026 · Every number on this page resolves to a primary source in the ledger below.

Build in-house

Median DS salary1
$120,230
Loading factor2
x1.46
FTE fraction
1.0
Open-source license
$0
Annual total
$175,852

Buy managed

Published floor3
$35,880/yr
Published ceiling4
$220,000+/yr
Quote-only vendors
9 of 12
Published range
$35,880 to $220,000+

At $10M paid spend, in-house runs about $176k/yr fully loaded versus a published managed range of $36k to $220k/yr. Get two managed quotes before committing either way; most vendors do not publish pricing.

The 1.0 / 0.5 FTE fraction is a stated assumption, not a measured figure. Salary and loading factor are sourced in the ledger below; the managed range uses published prices only, and 9 of the 12 managed vendors in the registry publish none.

The arithmetic, shown

In-house: median US data-scientist salary $120,2301 (BLS OEWS, May 2025, SOC 15-2051) times 1.462 loading (BLS ECEC, March 2026: total compensation $49.32/hr against wages $33.72/hr) = $175,852 fully loaded, times an FTE fraction of 1.0 for a first build without an existing data scientist, or 0.5 where MMM joins an existing scientist's load. The FTE fraction is our stated assumption; the salary and loading numbers are not.

Managed: the floor is Sellforte's published Growth tier, $2,990/mo3 = $35,880/yr; the ceiling is Paramark's published Enterprise from-price, $220,000/yr4, both verified 2 August 2026. Nine of twelve managed vendors publish nothing, so your quote may land outside this bracket; the bracket is simply everything any vendor was willing to print.

What the calculator does not price

Time to first defensible number (typically a quarter in-house, less with a vendor whose pipeline your data fits), key-person risk on the build side, black-box risk on the buy side, and the calibration experiments both sides need. Those trade-offs are qualitative and covered in open-source vs managed.

Next: step 3, the full cost anatomy.

Source ledger

Every numeric claim on this page resolves to one of these primary sources.

  1. [1]BLS OEWS, Data Scientists (SOC 15-2051), May 2025 national estimateshttps://www.bls.gov/oes/current/oes152051.htmretrieved 2026-08-02
  2. [2]BLS Employer Costs for Employee Compensation, March 2026https://www.bls.gov/news.release/ecec.nr0.htmretrieved 2026-08-02
  3. [3]Sellforte pricing pagehttps://sellforte.com/pricingretrieved 2026-08-02
  4. [4]Paramark pricing pagehttps://paramark.com/pricingretrieved 2026-08-02