How do their growth rates compare?

Isahit has a month-over-month growth rate of +80.2%; Patched has +30%.

How does pricing compare?

Isahit: Contact for Pricing. Isahit does not publish public rates; pricing is quoted per project after a scoping call, with offers for one-off labels, ongoing data-processing outsourcing and managed workforce engagements. A free small labelling project is offered through its self-service annotation tool. Patched: Paid, Contact for Pricing. Pricing scales with operational volume and is quoted per engagement; the company asks prospects to book a call and start with their hardest workflow. No self-serve tier or public price list is shown on the homepage, and the pricing page is not publicly accessible to crawlers.

What are alternatives to Isahit and Patched?

Other tools in related categories include Prolific, Zapier, n8n, Invisible Technologies, KaneAI.

Isahit vs Patched — Traffic (13.14K vs 3.11K), Pricing & Verdict

Open AI Tools

Isahit vs Patched — Traffic (13.14K vs 3.11K), Pricing & Verdict | Open AI Tools

Side-by-Side Traffic & Specification Matrix

Dimension / Metric	Isahit	Patched
Official Domain	isahit.com	patched.codes
Pricing Model	Contact for Pricing	Paid, Contact for Pricing
Monthly Visits	13.14K (13,136)	3.11K (3,111)
MoM Traffic Delta	+5.85K (+80.2%)	+718 (+30%)
Primary Market & Share	🇫🇷 France (30.7%)	🇺🇸 United States (67.3%)
Direct Visits / mo	5.48K	1.23K
User Rating	4.1	4.1
Supported Platforms	Website	Website

Isahit

Ethical human-in-the-loop data labeling and AI validation

Image, video and computer vision labeling

Annotation services for object detection, classification and segmentation across still images and frame-by-frame video, aimed at autonomous driving, drones and industrial inspection models.

NLP, speech and audio annotation

Text extraction, intent tagging, transcription and speech dataset work to train and evaluate language, conversational and voice models.

Generative AI and RLHF workflows

Human feedback collection, prompt monitoring, A/B testing and output evaluation to fine-tune LLMs and correct biases in generative and agentic systems.

AI Act trust and validation

Independent human testing, risk evaluation and traceable evidence to help teams document and validate AI systems against regulatory requirements.