Heap vs Kubit

Which one do AI engines recommend, and how do they differ on price, fit and features?

The short answer

Heap is the stronger AI pick in Product Analytics: it has a visibility score of 36.1 versus 0.3 for Kubit, and ranks ahead in 100% of the answers that name both.

Choose Heap if…

You want the brand AI engines rank higher (36.1 vs 0.3)
You need SOC 2 Type II or GDPR compliant or CCPA compliant or ISO 27001 or ISO 27701 or ISO 27017 or ISO 27018 coverage
Automatic event capture (autocapture)
Retroactive analysis without a predefined tracking plan

Choose Kubit if…

Recommended as an alternative for product analytics on your own data warehouse

AI visibility side by side

Latest weekly run · Nov 3

Metric	Heap	Kubit
Visibility score	36.1%	0.3%
Change vs last week	up+9.1%	New
Rank in Product Analytics	#4	#26
Share of voice	11.1%	0.2%
Mentions this run	52	1
Positive in AI answers	15%	—

The stronger value in each row is marked with a ✓.

Visibility score trend

Weekly runs over the last 60 days

Heap +9.100000000000001pp · Kubit +0.3pp over 1 weekly runs.

Engine by engine

Heap leads on 3 of 3 engines

ChatGPT

43%

0.9%

AI Mode

31.3%

Perplexity

34.1%

HeapKubit

Head to head in AI answers

1 engine answers name both brands

1Heap ranked higher

0Kubit ranked higher

Prompt	Engine	Heap	Kubit
Compare the leading Product Analytics platforms	ChatGPT	#4	#8

Features, pricing & fit

Dimension	Heap	Kubit
Pricing model	Usage-based	—
Entry price	Contact sales	—
Free plan	Yes	—
Deployment	Cloud-based; deployment starts by installing a free tracking snippet on the site	Runs inside the customer's own data warehouse, with no SDK or pipeline to install.
Integrations	100+	—
Compliance	SOC 2 Type II, GDPR compliant, CCPA compliant, ISO 27001, ISO 27701, ISO 27017, ISO 27018	—
Support	Email support, Standard support, Dedicated customer success manager (Premier), Heap Community	Email support@kubit.ai, /kubit:support command in Claude Code
Founded	—	2018
Headquarters	—	Los Altos, California, US
Employees	—	11–50
Funding	Series D · $205M raised	Series A, led by Insight Partners with Shasta Ventures and TSVC · $24M raised
Best for	Product and growth teams that want complete user behavior data without engineering-led event tagging	Teams running AI agents that want to find failure patterns in traces already stored in their data warehouse.