PostHog vs LogRocket
Two of fourteen models named PostHog first on the direct prompt; zero named LogRocket. Both were named by all fourteen models and PostHog carries 37 labels and LogRocket 42, so the shares are not directly comparable.
Named in seven categories this edition.
Named in two categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen 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 session replay and heatmaps page.
By framing
PostHogFirst choices, of fourteen modelsLogRocket
Across every category in the October 2026 Edition, PostHog and LogRocket were named in the same answer sixty-three times, of the 386 answers naming PostHog and the 120 naming LogRocket. In those answers LogRocket took the first choice two times and PostHog seven.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 | | | | | ||
| GPT-6 Luna | | | | | ||
| Muse Glimmer 30B | | | |
PostHog LogRocket first choice named as an alternative argued againstblank: not namedEach cell is one answer, PostHog on the left and LogRocket on the right.The direct prompt
PostHog first, LogRocket an alternative
2 of 14 modelsLogRocket was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
9 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 of 14 modelsThe answer made no first choice from these two in this category.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
PostHog2%#8 of 12LogRocket2%#6 of 12What the models said about PostHog
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“PostHog does offer data scrubbing features, but I was recently surprised to see their announcement about integrating AI to analyze session replays on behalf of users. This raises a red flag” Muse Glimmer 30B · negative prompt · soft negative
“If you enable network capture, scrutinize it separately from replay masking: requests and responses can contain sensitive headers or bodies” GPT-6 Luna · negative prompt · soft negative
“I'd generally recommend PostHog if you want the best balance of visitor behavior analytics, product analytics, and session replay in one place” GPT-5.4 mini · paraphrase prompt · first choice
What the models said about LogRocket
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“avoid or exercise extreme caution with Hotjar, FullStory, Mouseflow, LogRocket, and Smartlook” Mistral Small · negative prompt · hard negative
“early versions had PII exposure risks... litigation history warrants review” Grok 4.1 Fast · negative prompt · hard negative
“reviews highlight that it only guarantees that passwords are never recorded. It does not automatically mask other sensitive information” GLM 4.7 FlashX · negative prompt · soft negative
“The sweet spot is usually FullStory, Contentsquare, or LogRocket” DeepSeek V4 Flash · scale prompt · first choice
“LogRocket — If engineering debugging is priority #1” Kimi K2 · scale prompt · first choice
“FullStory / LogRocket: Industry standard for engineers/devs, deeper technical logging (network/XHR), slightly steeper learning curve.” Qwen 3.7 Flash · scale prompt · alternative
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.