Affinda Resume Parser vs OpenResumeParser
Affinda Resume Parser
affinda.comOpenResumeParser
github.comSide by side
| Figure | Affinda Resume Parser | OpenResumeParser |
|---|---|---|
| Price from | $300/mo | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 10 | 1 |
| Spec score · Resume Parsing Software | 8.5 (#7) | 7.4 (#24) |
| Deployment | Customers can use the hosted API or run the parser as a container inside their own infrastructure, and both deployment options expose the same API paths and JSON response. | The project includes a Dockerfile and docker-compose setup. |
| Extracted fields | Its response can include candidate details, work history, education, skills, languages, right to work, availability, projects, and referees. | It extracts names, email addresses, phone numbers, skills, experience, and education. |
| Purpose | The Resume Parser API converts resumes and CVs into structured candidate data for ATS, recruitment platforms, and HR products. | The project extracts structured data from resumes and matches skills against job descriptions. |
| Support | Affinda says customers can contact its team about pricing and deployment fit, and the company will respond to general inquiries within two working days. | The README directs users to open a GitHub issue for questions or bugs. |
Plans
Affinda Resume Parser
- Hosted self-serve$0.10, paid once
- Hosted annual — 66,000 documents$3,600 a year
- Hosted annual — 132,000 documents$6,000 a year
- Hosted annual — 240,000 documents$9,000 a year
- Self-hosted annual — 500,000 documents$12,000 a year
- Hosted annual — 600,000 documents$15,000 a year
- Hosted annual — 1,200,000 documents$21,000 a year
- Hosted annual — 2,500,000 documents$31,000 a year
- Hosted annual — 5,000,000 documents$48,000 a year
- Hosted annual — Above 5,000,000Priced on request
In their own words
Affinda Resume Parser
Affinda Resume Parser converts resumes and CVs into structured candidate data for ATS, recruitment platforms, and HR products. Its API can return candidate details, work history, education, skills, languages, right-to-work information, availability, projects, and referees. It accepts PDF, DOC, DOCX, TXT, RTF, HTML, PNG, JPG, TIFF, ODT, XLS, and XLSX files, including scans and photographs, and supports more than 50 languages. It normalizes skills, job titles, dates, and locations, and classifies employment type, education level, and language proficiency. Teams can use a hosted API in APAC, EU, or US regions, or run the parser as a container in their own infrastructure; both options expose the same API paths and JSON response. Hosted self-serve costs US$0.10 per document, with 1,000 free documents in the first 14 days. The self-hosted trial includes 10,000 free parses. Affinda says it encrypts data in motion and at rest, meets listed security and compliance standards, and does not use customer documents to train its models.
OpenResumeParser
OpenResumeParser is a free, MIT-licensed project that extracts structured information from resumes and compares skills with job descriptions. It reads PDF and TXT files and extracts names, email addresses, phone numbers, skills, experience, and education. Its matching output includes a score, matched skills, and missing skills. The project offers a command-line interface and a FastAPI REST API, with API access and batch processing listed. A curated database covers more than 120 technical skills and can be customized in extractor.py. Dockerfile and docker-compose files support deployment, while Pydantic models are used in a type-safe design described by the README. The listed platforms are API, self-hosted, and Windows. The MIT license permits personal and commercial use. Important limits include no scanned-PDF or OCR support, heuristic experience and education extraction, a curated rather than machine-learning skills database, and regular-expression date extraction.