Compare

Funnelkeeper compared

Three honest comparisons, including the rows where the other tool wins. Funnelkeeper does one join — spend to revenue — and guards it with a human.

How is Funnelkeeper different from PostHog?

PostHog tells you what users do. Funnelkeeper tells you if the spend paid back. PostHog is the stronger product-analytics tool and stays that way; Funnelkeeper reads the same kinds of events and adds the two things it does not join — ad spend and revenue — so you get a payback verdict per channel.

Product analytics vs the spend-to-revenue join. Both can run together.
CapabilityFunnelkeeperPostHog
Product analytics, session replay Not built for it Yes — its core
Ad spend joined by channel Yes Manual, via warehouse
Cohort payback and LTV:CAC Yes Build it yourself in SQL
Human-approved ad ops Yes No
Driven by an AI agent over MCP Yes Manual SDK setup

How is Funnelkeeper different from Cometly and other attribution suites?

Attribution suites are built for agencies buying media at scale, priced and staffed accordingly. Funnelkeeper is built for one founder with a handful of products and an AI agent doing the work. The measurement overlaps; the price, the multi-product model and the agent surface do not.

Attribution suites vs a founder-scale money layer.
DimensionFunnelkeeperCometly / suites
Entry price $0, then $39/mo Typically $200–1,000/mo
Several products in one account Yes Usually one workspace each
Usage-based revenue cohorts Yes Checkout-event focused
Agent-native API and MCP Yes No
Unattributed revenue shown separately Yes Often modelled away

Why not just ask ChatGPT or Claude to analyse my numbers?

Ask it and you get a good answer once. What you do not get is shared state: a number your co-founder sees too, a dashboard that is still there tomorrow, and a memory of which actions were taken and what happened next. Funnelkeeper is the state your AI drives.

A chat answer vs standing shared state. Funnelkeeper is designed to be driven by the model, not to replace it.
What you needFunnelkeeperAsking an LLM
One-off analysis of pasted data Yes Yes — and fast
Same numbers for the whole team Yes No
Standing dashboard tomorrow Yes No
Memory of actions and outcomes Yes — audit log No
Executes nothing without a human tap Enforced server-side Depends on the tools it holds

Run it next to whatever you already have — it reads, it never rewires.

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