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Customer intelligence and data science · September 2026 Edition

PostHog vs Fullstory

Zero of twelve models named PostHog first on the direct prompt; zero named Fullstory. PostHog was named by ten of the twelve models and Fullstory by ten and PostHog carries 16 labels and Fullstory 14, so the shares are not directly comparable.

PostHog

accepted challenger

Named in seven categories this edition.

Fullstory

accepted challenger

Named in four categories this edition.

First-choice share6%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%21%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#7A position in a field of 14; printed, not drawn.
Labels1614A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, PostHog reading right to left. Rank and label count are printed, not drawn.Google Analytics 4 vs PostHog · Google Analytics 4 vs Fullstory · Segment vs PostHog

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the customer intelligence and data science page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
PostHogFirst choices, of twelve modelsFullstory
Direct00
Paraphrase011 against Fullstory
Comparative01
Budget-constrained301 against Fullstory
Scale-constrained00
Negative001 against Fullstory
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Across every category in the September 2026 Edition, PostHog and Fullstory were named in the same answer fifty-five times, of the 257 answers naming PostHog and the 80 naming Fullstory. In those answers Fullstory took the first choice one time and PostHog eighteen.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where PostHog and Fullstory stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
PostHog Fullstory first choice named as an alternative argued againstblank: not namedEach cell is one answer, PostHog on the left and Fullstory on the right.

The direct prompt

The plain question, one answer per model, grouped by where PostHog and Fullstory stood in it.

Neither was the first choice, one was named

2 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashChurnZero alternatives: 6sense, Hightouch, HockeyStack, HubSpot Breeze Intelligence, Mixpanel, Oliv, PostHog, Vitally
Qwen 3.7 FlashGong, HubSpot, Mixpanel alternatives: Amplitude, Clari Copilot, Gainsight, Hightouch, PostHog, Segment, Totango

Neither was named

10 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Gainsight alternatives: Crunchbase, Demandbase
GPT-5.4 miniMicrosoft Dynamics 365 Customer Insights alternatives: 6sense, Adobe Real-Time CDP, Salesforce CRM Analytics
Perplexity SonarSegment alternatives: ChurnZero, Contentsquare, Hightouch, Outreach, Tealium
Grok 4.1 FastSegment alternatives: Hightouch, ZoomInfo
Mistral SmallHightouch, Segment alternatives: Planhat, Tealium
DeepSeek V4 FlashVitally alternatives: Apollo.io, Planhat, Salesmotion
Llama 4 Maverickno first choice
Kimi K2HubSpot Operations Hub + Breeze AI alternatives: 6sense, Amplitude, Avoma, Clari Copilot, Mixpanel
GLM 4.7 FlashXBuildBetter, Segment alternatives: Enterpret, HubSpot Marketing Hub, Salesforce Data Cloud
MiniMax M2.5HyperOrbit, Planhat alternatives: Chattermill, Thematic, Unwrap

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
PostHog leads by ten points.
PostHog10%#4 of 10
Fullstory0%#– of 10
The full small business standing →
Mid-marketThe figures above
PostHog leads by four points.
PostHog6%#4 of 14
Fullstory2%#7 of 14
The full mid-market standing →
Enterprise
PostHog leads by two points.
PostHog2%#– of 14
Fullstory0%#– of 14
The full enterprise standing →

What the models said about PostHog

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.

“PostHog is arguably the best single "customer analytics and intelligence platform" for a limited budget” DeepSeek V4 Flash · budget prompt · first choice
“The Best All-in-One Platform: PostHog ... combines them into one dashboard, saving you thousands of dollars” Gemini 3.5 Flash · budget prompt · first choice

What the models said about Fullstory

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.

“Most heatmap and session recording tools (like Hotjar or FullStory) can get very expensive.” Qwen 3.7 Flash · budget prompt · soft negative
“Purely B2C-oriented tools like Heap, FullStory... weaker at account-level B2B analysis” DeepSeek V4 Flash · paraphrase prompt · soft negative
“Fullstory is the top choice for understanding users' behavior across digital platforms” Claude Haiku 4.5 · comparative prompt · first choice
“Start with FullStory if you are purely digital/service-focused.” Qwen 3.7 Flash · paraphrase prompt · first choice
“Choose Contentsquare or FullStory if you care most about user experience diagnosis and session replay.” GPT-5.4 mini · comparative prompt · alternative
Also compared

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.