Azure AI Speech vs spaCy
Azure AI Speech
azure.microsoft.comspaCy
spacy.ioSide by side
| Figure | Azure AI Speech | spaCy |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 3 | 1 |
| Spec score · Natural Language Processing Software | 8.5 (#1) | 8.4 (#2) |
| Founded | 1975 | 2015 |
| Headquarters | Redmond, Washington, United States | Berlin, Germany |
| Integrations | Microsoft lists Azure OpenAI, Microsoft Foundry, Azure Content Understanding, Azure Translator, and Azure Language as related products. | The documentation describes integrations with transformer pipelines, CuPy for GPU support and an extension for Visual Studio Code configuration files. |
Plans
Azure AI Speech
- Free (F0)Free
- Pay as You GoPrice not published
- Pay as You GoPriced on request
In their own words
Azure AI Speech
Azure AI Speech has no write-up on Specifiction yet; its figures are above.
spaCy
spaCy is a free, open-source Python library for building natural language processing applications, with a focus on production use and large volumes of text. Its components handle named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphology, and entity linking. The project lists support for 75+ languages and 84 trained pipelines for 25 languages, though trained pipelines are not available for every language. Developers can configure and train custom pipelines, describe training runs with configuration files, and package pipelines for installation as Python packages. spaCy can use pretrained transformers through spacy-transformers, and supports custom models in PyTorch, TensorFlow, and other frameworks. spaCy Projects supports workflows for downloads, commands, checksum verification, and caching, along with integrations for tracking, demos, model serving, distributed training, and Hugging Face Hub. Installation is available through pip, conda, or source on Linux, macOS, and Windows. The library can run on a user's servers without data leaving them. It is not a hosted software-as-a-service product or web application. The library is free under AGPL or a commercial license; transformer models require a GPU to run effectively.