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SPECIFICTION
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Promptic vs PromptPex

Promptic

promptic.eu

PromptPex

microsoft.github.io

Side by side

FigurePrompticPromptPex
Price from€149/mo—
Free planYes—
Free trial——
Open source——
WebYesYes
Windows—Yes
macOS—Yes
Linux—Yes
APIYes—
Self-hosted—Yes
Plans published40
Spec score · AI Prompt Generators8.0 (#12)7.2 (#21)
SupportThe pricing page lists community support for Free, standard support for Team, priority support for Business, and dedicated support for Enterprise.The transparency note invites feedback and reports of unexpected or offensive behavior by email to listed project contacts.

Plans

Promptic

  • FreeFree
  • Team€149 a month
  • Business€599 a month
  • EnterprisePriced on request

Promptic pricing →

PromptPex

No plans published.

PromptPex pricing →

In their own words

Promptic

Promptic has no write-up on Specifiction yet; its figures are above.

Visit promptic.eu

PromptPex

PromptPex is an open-source tool for developers who keep AI prompts in their codebase and want to examine how models follow prompt requirements. It treats prompts as functions, extracts output rules expressed in natural language, and generates unit tests to check whether a model follows those rules. It can also evaluate prompt performance across models, using an LLM to judge whether outputs meet stated requirements. Test generation can be shaped with scenarios, expansions, custom instructions, and effort levels. The CLI accepts Markdown or text templates, Prompty files, and GitHub Models prompt YAML files. Setup lists OpenAI, Azure OpenAI, GitHub Models, and Ollama as supported providers. PromptPex is packaged as an npm CLI and can run in Docker; its setup guide also describes a local web interface. Documented integrations include OpenAI Evals, Azure OpenAI Stored Completions, GitHub Models Evals, and GitHub Actions. Microsoft is identified as the maker. The project is intended for research, and its maker advises against downstream use without additional evaluation of robustness, safety, harm, and bias. Vague prompts can lead to suboptimal results, and rule extraction may be less accurate with complex or idiomatic language.

Visit microsoft.github.io