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Index › Marketing › Experimentation › Optimizely CMS vs PostHog
Experimentation and personalization · October 2026 Edition

Optimizely CMS vs PostHog

Zero of fourteen models named Optimizely CMS first on the direct prompt; zero named PostHog. Optimizely CMS was named by fourteen of the fourteen models and PostHog by eleven and Optimizely CMS carries 55 labels and PostHog 19, so the shares are not directly comparable.

Optimizely CMS

criticized challenger

By Optimizely, San Francisco, United States, founded 2010. Named in eight categories this edition.

PostHog

accepted challenger

Named in seven categories this edition.

First-choice share7%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate47%5%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#7A position in a field of 12; printed, not drawn.
Labels5519A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Optimizely CMS reading right to left. Rank and label count are printed, not drawn.VWO was named alongside these two in eleven of the fourteen direct answers. VWO vs Optimizely CMS · VWO vs PostHog · Convert Experiences vs Optimizely CMS

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 experimentation and personalization page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Optimizely CMSFirst choices, of fourteen modelsPostHog
Direct007 against Optimizely CMS · 1 against PostHog
Paraphrase105 against Optimizely CMS
Comparative70
Budget-constrained123 against Optimizely CMS
Scale-constrained201 against Optimizely CMS
Negative0010 against Optimizely CMS
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Optimizely CMS and PostHog 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
GPT-6 Luna
Muse Glimmer 30B
Optimizely CMS PostHog first choice named as an alternative argued againstblank: not namedEach cell is one answer, Optimizely CMS on the left and PostHog on the right.

The direct prompt

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

Neither was the first choice, one was named

3 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashVWO alternatives: Abmatic AI, Croct, Ploy, PostHog
Kimi K2VWO alternatives: Intellimize, Mutiny, PostHog
GPT-6 LunaVWO alternatives: Optimizely CMS

Neither was named

11 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5VWO alternatives: Mutiny
GPT-5.4 miniKameleoon alternatives: Optimizely Web Experimentation, VWO Testing
Perplexity SonarVWO alternatives: Personyze
Grok 4.1 FastVWO alternatives: AB Tasty, Convert Experiences, Mutiny
Mistral SmallOptimizely Web Experimentation, Personyze alternatives: Kameleoon
DeepSeek V4 FlashConvert Experiences, Mutiny alternatives: Abmatic AI, VWO
Llama 4 MaverickOptimizely Web Experimentation alternatives: HubSpot, Kameleoon, Personyze
Qwen 3.7 FlashVWO alternatives: AB Tasty, Convert Experiences, Mutiny
GLM 4.7 FlashXMutiny alternatives: Abmatic AI, VWO
MiniMax M2.5Markettailor, VWO alternatives: AB Tasty, Mutiny
Muse Glimmer 30BMutiny, VWO alternatives: HubSpot, Kameleoon, Personyze

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 three points.
PostHog3%#6 of 14
Optimizely CMS0%#14 of 14
The full small business standing →
Mid-marketThe figures above
The order flips: Optimizely CMS leads at mid-market.
Optimizely CMS7%#5 of 12
PostHog4%#7 of 12
The full mid-market standing →
Enterprise
Optimizely CMS leads by forty-six points.
Optimizely CMS46%#1 of 11
PostHog0%#– of 11
The full enterprise standing →

What the models said about Optimizely CMS

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

“more enterprise-y, pricier ~$36K/yr min, dev-heavy). Avoid if mid-market budget/timeline constrained.” Grok 4.1 Fast · direct prompt · hard negative
“What to avoid at this size: Optimizely is enterprise and engineering-led... typically overkill” Muse Glimmer 30B · paraphrase prompt · hard negative
“Avoid enterprise tools like Optimizely or Adobe Target—they typically start at $36k+/year.” DeepSeek V4 Flash · budget prompt · hard negative
“I'd lean toward VWO or Optimizely depending on your budget... Optimizely provides more advanced personalization if you have the budget.” Claude Haiku 4.5 · paraphrase prompt · first choice
“Leader in Digital Experience Platforms (DXP)... For Enterprise Organizations: Optimizely, Dynamic Yield, or Monetate offer comprehensive suites” MiniMax M2.5 · comparative prompt · first choice
“The heavyweight, "do-everything" enterprise platform... Recognized as a Leader in the *Gartner Magic Quadrant for Personalization Engines*” Gemini 3.5 Flash · comparative prompt · first choice

What the models said about PostHog

No label in this category carried a quote.

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.