What top GTM stacks actually look like
Based on 30,000+ modeled scans — not opinions or vendor claims
Most teams cut spend in CRM, data, and engagement layers
Across 30,000+ modeled stacks
Distribution shift: mass moves out of 10–16 tool stacks toward tighter footprints.
Full cohort · no stacks with tool lists yet
Refreshing cohort…
Tools that replace the most stack
Ranked by consolidation impact across real stacks.
Strong automation and model-friendly surface. Plays well with Salesforce and HubSpot. Watch implementation cost if your stack is still spreadsheet-heavy.
Strong automation and model-friendly surface. Plays well with Segment and Snowflake. Watch implementation cost if your stack is still spreadsheet-heavy.
Strong automation and model-friendly surface. Plays well with Salesforce and HubSpot. Watch implementation cost if your stack is still spreadsheet-heavy.
Strong automation and model-friendly surface. Plays well with Slack and HubSpot. Watch implementation cost if your stack is still spreadsheet-heavy.
Strong automation and model-friendly surface. Plays well with Slack and Notion. Watch implementation cost if your stack is still spreadsheet-heavy.
Showing top 16 for this view. Rankings use the StackScan authority catalog and engine scoring — not survey popularity.
See where your stack ranks
Run StackScan on your real tools — overlap, swaps, and modeled economics use this same scoring layer.
See my stack analysis →What's changing
What teams are removing vs adding right now
Replacement pressure and inflow, normalized to the same scale — hover a row for context.
What consolidation actually saves
How monthly GTM spend changes after removing overlap and unused tools
Fewer overlapping vendors → lower redundant run-rate. StackScan refines this with your inventory.
Stacks are getting smaller
Cohort mean moves from ~13.2 to ~9 tools after optimization.
~13.2
tools before
~9
tools after
32% reduction · 4 tools removed on average
Most stacks consolidate to 7–10 tools after optimization
AI-native tools are replacing legacy stacks
Share of tools that read as AI-native in the catalog — rises as legacy sequencing and point solutions roll off.
4 ways modern GTM stacks are evolving
Four recurring shapes — share of stacks, typical footprint, and modeled cost band.
High overlap debt; AI-native tools below median — renewal risk concentrated in MAP + data.
12–16 tools with parallel CRM/seq/data; consolidation yields fastest payback.
Fewer vendors, higher AI-native share — overlap cost lower, sequencing more automated.
Actively replacing legacy engagement & data while keeping core CRM stable.