StackSwap original data · 2026

State of the GTM Stack 2026

We generated 100,000 synthetic go-to-market stacks across 12 archetypes — from bootstrapped founders to multi-region enterprises — and ran every one through the same engine that powers StackScan. This is what the modern GTM stack actually looks like: what it costs, how many tools it carries, where the overlaps hide, and how much of the bill is recoverable. Every figure is modeled and reproducible — and hardens toward observed as real audits accrue.

Median monthly spend
$20.7K
GTM software, median stack
Recoverable / yr
$93.2K
$7,770/mo median waste
Median tool count
9
→ 8 optimized (−10.5%)
Stacks with overlap
81.66%
≥1 redundant pair flagged
AI readiness
43% → 71%
current → post-optimization

The verdict on the average stack

Run every modeled tool through the keep / replace / remove rubric and a clear shape emerges: most tools earn their seat, but nearly one in five is doing a job a leaner or already-present tool could do. Only ~10.54% are pure dead weight. The money isn’t in ripping tools out — it’s in collapsing overlap.

Keep71.32%
Replace18.14%
Remove10.54%
Across 885,273 tool verdicts in 100,000 modeled stacks.

Spend scales faster than headcount

This is the most important pattern in the dataset. Lean teams (1–15) carry almost no recoverable waste — they haven’t had time to accumulate overlap. But recoverable spend climbs steeply with size as every new team buys its own tools on top of what the org already pays for. By 201–500 people the median stack is leaking $76,280/mo; at 1000+ it’s $239,890/mo. Overlap is an emergent property of scale.

1-5 people$0/mo
6-15 people$80/mo
16-25 people$5,120/mo
26-50 people$8,680/mo
51-100 people$21,410/mo
101-200 people$25,890/mo
201-500 people$76,280/mo
501-1000 people$130,630/mo
1000+ people$239,890/mo
Median recoverable spend per month, by team size. Modeled.
Team sizeMedian spend/moRecoverable/moRecoverable %n (stacks)
1-5 people$580$00%7,806
6-15 people$1,950$802.3%13,043
16-25 people$11,750$5,12039.8%11,053
26-50 people$21,090$8,68037.9%32,025
51-100 people$46,030$21,41042%18,012
101-200 people$74,050$25,89033%5,974
201-500 people$178,300$76,28041.6%7,150
501-1000 people$407,360$130,63035.3%1,608
1000+ people$751,620$239,89036.4%3,329

The tools everyone runs

Presence isn’t the interesting signal — replace rate is. Slack, Salesforce, HubSpot, and Notion are near-universal keeps. But several tools that appear in more than half of all stacks get flagged for replacement at high rates the moment a leaner equivalent covers the same job. Highlighted bars below are tools the engine replaces in 80%+ of the stacks they appear in.

Slack100%
Notion62.81%
ZoomInfo61.37%
Outreach54.38%
Salesforce50.88%
Gong50.88%
HubSpot49.12%
LinkedIn Sales Navigator46.49%
Calendly44.6%
Apollo.io34.69%
Share of modeled stacks containing each tool. Highlighted = replaced in 80%+ of stacks where present.
Tool% of stacksReplace rate when present
Slack100%0%
Notion62.81%0%
ZoomInfo61.37%84.19%
Outreach54.38%100%
Salesforce50.88%0%
Gong50.88%0%
HubSpot49.12%0%
LinkedIn Sales Navigator46.49%0%
Calendly44.6%0%
Apollo.io34.69%0%
HubSpot Marketing Hub32.94%0%
Marketo27.67%18.4%

Where the overlaps hide

The most common redundancies are the ones teams stop seeing: two tools quietly doing the same job in different corners of the org. These are the highest-prevalence overlap pairs across the dataset, with the modeled annual recovery from consolidating each.

Overlapping pair% of stacksMedian recovery/yr
HubSpot Marketing Hub + Salesforce29.97%$1,800
Clari + Gong23.79%$1,200
Apollo.io + ZoomInfo20.49%$2,400
Outreach + Salesloft18.74%$1,200
Apollo.io + Outreach17.75%$1,200
Linear + Notion15.2%$960
Clearbit + ZoomInfo13.99%$3,600
Chorus + Gong12.98%$1,200

What the engine consolidates toward

When a tool is flagged for replacement, the engine names what it consolidates toward. The recommendations skew heavily to platforms that absorb multiple point tools — the consolidation thesis in one list:

Recommended replacementShare of replace verdicts
Smartlead45.54%
Clay37.56%
Fireflies.ai8.08%
HubSpot5.64%
HubSpot Marketing3.17%

The archetypes behind the numbers

The 100,000 stacks are distributed across 12 archetypes weighted to reflect the real market. The largest cohorts:

FAQ

It's StackSwap's own benchmark of the modern go-to-market software stack, built from 100,000 synthetic GTM stacks generated across 12 archetypes (from bootstrapped 1–15 teams to late-stage multi-region enterprises) and run through the exact same scoring engine that powers StackScan. Unlike survey reports, every number here is reproducible from a fixed seed — re-run `SIM_SEED=42 npm run simulate:100k` and you get the same dataset. It will harden from modeled toward observed as real StackScan audits accumulate.

Across 100,000 modeled stacks, the median team spends $20,700/month on GTM software and carries $7,770/month — $93,240/year — in recoverable waste from overlapping, redundant, or replaceable tools. The spread is wide: the 25th percentile recovers almost nothing ($280/mo) while the 95th percentile recovers $119,236/mo. 81.66% of stacks contained at least one overlap pair the engine flagged.

The median modeled stack runs 9 tools (mean 8.9). After the engine applies its keep / replace / remove rubric, the median optimized stack is 8 tools — a 10.5% reduction in tool count. The bigger lever is usually consolidation of overlapping spend, not raw tool-count reduction: only 10.54% of tools get a "remove" verdict, while 18.14% get "replace."

By prevalence across the modeled universe: Slack (100%), Notion (62.81%), ZoomInfo (61.37%), Outreach (54.38%), Salesforce (50.88%), Gong (50.88%). The interesting signal isn't presence — it's replace rate. Tools like ZoomInfo and Outreach appear in more than half of stacks but get flagged for replacement at very high rates when a leaner equivalent covers the same job, whereas Slack, Salesforce, and HubSpot are near-universal keeps.

It scales faster than headcount. A 1–5 person team's median stack spend is $580/mo with effectively no recoverable waste, because lean teams haven't accumulated overlap yet. By 51–100 people the median spend is $46,030/mo with $21,410/mo recoverable, and at 1000+ it's $751,620/mo with $239,890/mo recoverable. Overlap is an emergent property of scale — every new team that buys its own tools adds a redundancy the org already paid for elsewhere.

Modeled — and we say so on every surface. These are synthetic stacks built to match real archetype distributions, then scored by the production StackScan engine. That makes the dataset large, reproducible, and bias-controlled, but it is not a survey of real companies. As real StackScan audits accumulate past the statistical-mass threshold per cohort, the observed numbers overlay the modeled ones automatically (the same flywheel documented at /methodology), and this page updates with them.

Run StackScan. Paste your tools and it applies the same keep / replace / remove rubric used to build this benchmark, then tells you your overlap pairs, recoverable spend, and where you sit against peers in your motion and team size — in about 60 seconds, free, no signup.

Statistics derived from 100,000 synthetic GTM stacks generated across 12 archetypes and run through the same scoring engine that powers StackScan. Methodology: /methodology. Reproduce: `SIM_SEED=42 npm run simulate:100k`. Dataset generated 2026-06-02. See also our GTM Stack Benchmark and all benchmarks.