Promptic vs PromptWizard
Promptic
promptic.euPromptWizard
microsoft.github.ioSide by side
| Figure | Promptic | PromptWizard |
|---|---|---|
| Price from | €149/mo | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 4 | 1 |
| Spec score · AI Prompt Generators | 8.0 (#12) | 8.4 (#6) |
| Headquarters | Frankfurt, Germany | Redmond, Washington, USA |
| Product | Promptic describes itself as a GenAI optimization platform for tracing, evaluating, and optimizing AI agents and LLM prompts. | PromptWizard is an open source framework for automated prompt and example optimization using a feedback-driven critique and synthesis process. |
| Security | Its listed safeguards include TLS in transit, access controls, row-level database security patterns, encrypted provider secrets, monitoring, backup and recovery, and incident response. | The repository links a security policy, but the pages opened do not state specific security controls or compliance certifications. |
Plans
In their own words
PromptWizard
PromptWizard is a free, MIT-licensed open-source framework for automated prompt and example optimization. It generates prompt variations, scores them, critiques their outcomes, and refines instructions over repeated rounds. It can also optimize in-context examples and synthesize examples suited to a task. The described workflows cover prompt optimization without examples, with generated examples, or with training data. Listed datasets include GSM8k, SVAMP, AQUARAT, and Instruction Induction (BBII). Custom datasets use JSONL samples with question and answer fields, and require task-specific answer extraction and evaluation functions, configuration, and data files. PromptWizard can generate chain-of-thought reasoning for in-context examples, with an option to turn that off to reduce prompt length. Installation uses the GitHub repository as a Python package in development mode, with instructions for Windows, macOS, and Linux. Model access setup supports OpenAI API keys and Azure OpenAI endpoints, so access requires credentials for those services. The README reports average optimization times of around 20–30 minutes in experiments on the listed datasets, with timing dependent on the dataset, and notes that human supervision can help tune outputs.